computer science phd project topics

Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic. 

Overview: CompSci Research Topics

• Algorithms & data structures
• Artificial intelligence ( AI )
• Computer networking
• Database systems
• Human-computer interaction
• Information security (IS)
• Software engineering
• Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

• An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
• A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
• An evaluation of machine learning algorithms used for recommender systems in streaming services
• A review of approximation algorithm approaches for solving NP-hard problems
• An analysis of parallel algorithms for high-performance computing of genomic data
• The influence of data structures on optimal algorithm design and performance in Fintech
• A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
• A comparison of streaming algorithm performance for the detection of elephant flows
• A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
• Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
• Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
• A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
• A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
• Exploring the feasibility of applying information theory to feature extraction in retail datasets.
• Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

• Applying deep learning algorithms for speech recognition in speech-impaired children
• A review of the impact of artificial intelligence on decision-making processes in stock valuation
• An evaluation of reinforcement learning algorithms used in the production of video games
• An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
• An analysis of the ethical and social implications of artificial intelligence-based automated marking
• The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
• An examination of the use of artificial intelligence in orthopaedic surgery
• The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
• An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
• A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Topics & Ideas: Networking

• An analysis of the impact of 5G technology on internet penetration in rural Tanzania
• Assessing the role of software-defined networking (SDN) in modern cloud-based computing
• A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
• Exploring the influence of cloud computing on security risks in fintech.
• An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
• Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
• An evaluation of the challenges and opportunities in 6G wireless network adoption
• The role of network congestion control algorithms in improving network performance on streaming platforms
• An analysis of network coding-based approaches for data security
• Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Topics & Ideas: Database Systems

• An analysis of big data management systems and technologies used in B2B marketing
• The impact of NoSQL databases on data management and analysis in smart cities
• An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
• Exploring the role of data warehousing and business intelligence in global consultancies
• An analysis of the use of graph databases for data modelling and analysis in recommendation systems
• The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
• An examination of the challenges and opportunities of distributed databases in supply chain management
• Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
• An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
• Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

• An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
• An exploration of how artificial intelligence is changing human-computer interaction patterns in children
• An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
• Assessing the influence of virtual and augmented reality on consumer purchasing patterns
• An examination of the use of gesture-based interfaces in architecture
• Exploring the impact of ease of use in wearable technology on geriatric user
• Evaluating the ramifications of gamification in the Metaverse
• A systematic review of user experience (UX) design advances associated with Augmented Reality
• A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
• Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Topics & Ideas: Information Security

• A bibliometric review of current trends in cryptography for secure communication
• An analysis of secure multi-party computation protocols and their applications in cloud-based computing
• An investigation of the security of blockchain technology in patient health record tracking
• A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
• A systematic review of secure data storage solutions used for cloud computing in the fintech industry
• An analysis of intrusion detection and prevention systems used in the healthcare sector
• Assessing security best practices for IoT devices in political offices
• An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
• A comparative study of digital signature schemes adoption in property transfers
• An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

• A study of agile software development methodologies and their impact on project success in pharmacology
• Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
• A study of the impact of DevOps practices on software development and delivery in the healthcare sector
• An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
• A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
• An investigation of software testing techniques and methodologies for subscription-based offerings
• A review of software security practices and techniques for protecting against phishing attacks from social media
• An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
• Exploring the impact of software development outsourcing on project success in multinational contexts
• An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• An array-based optimization framework for query processing and data analytics (Chen, 2021)
• Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
• Embedding constructural documentation in unit tests (Nassif, 2019)
• PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
• Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
• Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
• Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
• Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
• Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
• Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
• Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
• A state-based approach to context modeling and computing (Yue, 2019)
• A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
• HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

Investigating the impacts of software refactoring techniques and tools in blockchain-based developments.

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Investigating the impacts of software refactoring techniques and tools in blockchain-based developments is in my favour. May i get the proper material about that ?

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Potential PhD projects

There are opportunities for talented researchers to join the School of Computer Science and Engineering, with projects in the following areas:

Artificial intelligence

Bioinformatics and computational biology group, biomedical image computing, data processing and knowledge discovery, embedded systems.

• Networked systems

Service oriented computing

Software engineering and software security, trustworthy systems.

Supervisory team : Professor Claude Sammut 

Project summary : Our rescue robot has sensors that can create 3D representations of its surroundings. In a rescue, it's helpful for the incident commander to have a graphical visualisation of the data so that they can reconstruct the disaster site. The School of Computer Science and Engineering and the Centre for Health Informatics have a display facility (VISLAB) that permits users to visualise data in three dimensions using stereo projection onto a large 'wedge' screen. 

This project can be approached in two stages. In the first stage, the data from the robot are collected off-line and programs are written to create a 3D reconstruction of the robot's surroundings to be viewed in the visualisation laboratory. In the second stage, we have the robot transmit its sensor data to the VISLAB computers for display in real-time. 

This project requires a good knowledge of computer graphics and will also require the student to learn about sensors such as stereo cameras, laser range finders and other 3D imaging devices. Some knowledge of networking and compression techniques will be useful for the second stage of the project. 

A scholarship/stipend may be available. 

For more information contact:  Prof. Claude Sammut

Supervisory team : Wenjie Zhang, Dong Wen, Xiaoyang Wang

Project summary : This project explores the integration of artificial intelligence (AI) techniques with fundamental data processing problems, such as predictive modeling, forecasting, and anomaly detection. The project aims to develop machine learning and deep learning algorithms to gain insights from large volumes of data, which produce novel solutions for various real-world tasks and data types. The research has the potential to revolutionize the way data processing systems are designed, operated, and used in various applications and domains.

A scholarship/stipend may be available.

For more information contact: [email protected]          

Supervisory team : Dr Raymond Louie

Project summary : Accurately predicting disease outcomes can have a significant impact on patient care, leading to early detection, personalized treatment plans, and improved clinical outcomes. Machine learning algorithms provide a powerful tool to achieve this goal by identifying novel biomarkers and drug targets for various diseases. By integrating machine learning algorithms with biological data, you will have the opportunity to push the boundaries of precision medicine and contribute to algorithms that can revolutionize the field.

We are looking for a highly motivated student who is passionate about applying computational skills to solve important health problems. Don’t worry, no specific biological knowledge is necessary, the important thing is you are enthusiastic and willing to learn. Please get in touch if you have any questions. 

For more information contact:  Dr. Raymond Louie

Supervisory team: Dr. Aditya Joshi

Project Summary: Discrimination and bias towards protected attributes have legal, social, and commercial implications for individuals and businesses. The project aims to improve the state-of-the-art in the detection of discrimination and bias in text. The project will involve creation of datasets, and development of new approaches using natural language processing models like Transformers. The datasets may include different text forms such as news articles, job advertisements, emails, or social media posts. Similarly, the proposed approaches may use techniques such as chain-of-thought prompting or instruction fine-tuning.

For more information, contact [email protected] .

Supervisory team: Wenjie Zhang, Dong Wen, Xiaoyang Wang

Project Summary: Large Language Models (LLMs) like GPT are revolutionizing the field of data science. Research in this area is multifaceted, exploring the development, application, and implications of these models. The project aims to utilize the LLMs to solve a wide spectrum of tasks in data science, from data preprocessing to predictive modeling and beyond. The outcome of the project will push the boundaries of data processing techniques, creating more intelligent, efficient, and ethical data science solutions.

A scholarship/stipend may be available. For more information contact: [email protected]          

Supervisory team: Dr Sasha Vassar

Project Summary: You will be working as part of a team that develops educational large language models, including fine-tuning, design, evaluation and deployment to large audiences.

For more information contact: [email protected]

Eligibility Criteria: 

• domestic applicants (Citizens or Permanent Residence of Australia and New Zealand)  
• with first or upper second-class Honours, or an equivalent qualification

Supervisory team:  Dr Gelareh Mohammadi, Professor Arcot Sowmya, Dr Gideon Kowadlo

Project summary: The standard model of decision-making in biological systems involves a combination of model-free and model-based reinforcement learning (RL) algorithms. These processes are reflected in the Striatum (model-free) and the Prefrontal Cortex (PFC, model-based). Research shows that the model-free Striatum exerts gating control over the model-based PFC, a relationship captured in the influential PBWM framework (Frank and O'Reilly 2006) within the context of working memory. This intricate functional connectivity underpins decision-making, possibly balancing the strengths of both systems.

In AI, model-free and model-based RL algorithms have achieved significant advancements in applications like game playing and robot control. However, these systems face notable challenges: model-free RL is notoriously data-hungry and struggles with environmental changes, while model-based RL, though more adaptable, is computationally intensive, particularly at decision time. These limitations hinder the efficiency and productivity of AI systems, especially in dynamic and real-time environments.

This project aims to develop a novel RL architecture inspired by the biological interplay between the Striatum and PFC. We propose a "model-free-gated, model-based" recurrent system where the world model provides context/high-level goals to the model-free controller, which in turn exerts gating control over the world model. By integrating the strengths of both approaches, this architecture is designed to enhance the flexibility and efficiency of decision-making processes, reducing the data inefficiency of model-free methods while mitigating the computational burden of model-based planning. Through comparison with human data, we will evaluate this architecture's ability to overcome the limitations of traditional RL systems, ultimately contributing to AI systems that are more productive, adaptable, and capable of making efficient decisions in complex, changing environments.

This project will be conducted in close collaboration with Cerenaut.ai , an independent research group.

For more information contact:  Dr. Gelareh Mohammadi

Project summary: The brains of all bilaterally symmetric animals, including humans, are divided into left and right hemispheres. While the anatomy and physiology of these hemispheres overlap significantly, they specialize in different attributes, which contributes to enhanced cognitive and motor functions. Despite this, the principle of hemispheric specialization remains underexplored in artificial intelligence (AI), machine learning (ML), and motor control systems. A preliminary study [ Rinaldo24 ] demonstrated that it is possible to replicate this type of hemispheric specialization for motor control in AI, where the dominant system excels in trajectory planning, and the non-dominant system specializes in positional control. This study also revealed the potential for exploiting such specialization to improve the performance of simple one-armed motor tasks.

The aim of this project is to extend th research to a two-armed system and more complex tasks, focusing on how hemispheric specialization can enhance productivity and performance in robotic systems. Specifically, we will explore whether the left and right hemispheres can collaborate to improve the performance of a single arm, and how they might enhance task efficiency when each arm performs complementary aspects of a task (e.g., holding an object with the non-dominant hand while the dominant hand performs precise actions). Additionally, we will investigate how smoothly switching between these modes can further optimize robotic performance.

By building a model with left and right neural networks connected via a corpus callosum (interhemispheric communication) to perform motor tasks, and comparing this model to human performance and standard ML approaches, this research will not only contribute to a deeper understanding of why brains are divided into left and right hemispheres but also establish a new principle for motor control in robotics. This approach promises to significantly enhance the efficiency and productivity of robotic systems, leading to more effective and adaptable robots capable of performing complex tasks with greater precision and coordination.

Project summary:  The brains of all bilaterally symmetric animals, including humans, are divided into left and right hemispheres, each specializing in different cognitive functions. While this principle is well-documented in biology, it remains underutilized in artificial intelligence (AI) and machine learning (ML). According to the Novelty-Routine Hypothesis (NRH), the right hemisphere acts as a 'generalist' that excels in handling novel tasks, while the left hemisphere specializes in routine tasks, with cognitive activity shifting from the right to the left as tasks become more familiar. This natural specialization is particularly relevant to the challenges faced in continual reinforcement learning (RL), where an agent must learn a sequence of tasks while avoiding catastrophic forgetting of previous knowledge.

Current approaches in RL primarily focus on maximizing performance on specific tasks, often neglecting the agent's initial performance on new and unfamiliar tasks. However, in many real-world applications, it is critical that an agent performs competently from the outset, as failures during the learning phase can be costly or dangerous. In a preliminary study [ Nicholas24 ], we developed a bi-hemispheric RL agent that leverages the generalist capabilities of a right-hemisphere-inspired model to maintain strong initial performance on novel tasks.

The goal of this project is to enhance this model by incorporating interhemispheric communication, mimicking the corpus callosum found in biological brains. This communication channel, shown to be beneficial in bilateral models for motor control [ Rinaldo24 ], will enable our RL agent to smoothly transition knowledge between hemispheres, further improving its adaptability and performance in continual learning settings. By focusing on graceful task adaptation, this research aims to create AI systems that not only achieve high performance over time but also maintain robust and reliable productivity when faced with new challenges, making them more suitable for deployment in dynamic and safety-critical environments.

Supervisory team: Dr Raymond Louie, Dr Sara Ballouz

Project Summary: In machine learning, feature selection has become a key step in improving the predictive performance of the algorithm by eliminating redundant variables and selecting for those that are likely critical. In the biomedical field, these features are extremely useful; they can be used for understanding the underlying biology, further validated as biomarkers of disease or clinical diagnostic markers, and as targets for drug therapy. Many feature selection methods exist, but the best approach to use in experiments relating to multi-omics has yet to be assessed. This project will involve the development/assessment of different methods and their application to cancers, autoimmunity, and viral infections.

For more information contact [email protected] , [email protected]

Supervisory team:  Dr Yang Song

Project summary:  Various types of microscopy images are widely used in biological research to aid our understanding of human biology. Cellular and molecular morphologies give lots of information about the underlying biological processes. The ability to identify and describe the morphological information quantitative, objectively and efficiently is critical. In this PhD project, we'll investigate various computer vision, machine learning (especially deep learning) and statistical analysis methodologies to develop automated morphology analysis methods for microscopy images.

More research topics in computer vision and biomedical imaging can be found  here .

For more information contact:  Dr Yang Song

Supervisor team:  Professor Erik Meijering and Dr John Lock

Project summary:  Biologists use multiparametric microscopy to study the effects of drugs on human cells. This generates multichannel image data sets that are too voluminous for humans to analyse by eye and require computer vision methods to automate the data interpretation. The goal of this PhD project is to develop, implement, and test advanced computer vision and deep learning methods for this purpose to help accelerate the challenging process of drug discovery for new cancer therapies. This project is in collaboration with the School of Medical Sciences (SoMS) and will utilise a new and world-leading cell image data set capturing the effects of 114,400 novel drugs on the biological responses (phenotypes) of >25 million single cells.

For more information contact:  [email protected] ,  [email protected]

Supervisory team:  Dong Wen, Wenjie Zhang

Project summary:  Many complex systems and phenomena in the real world can be represented as graphs, such as social networks, biological networks, transportation networks, and communication networks. Under the research theme of Big Data, big graph processing is a key area that draws on concepts from data structure, algorithms, graph theory, distributed systems, parallel computing, machine learning, and database systems to address the unique challenges posed by large-scale graph data. This project aims to develop algorithms, techniques, and systems to efficiently analyze and manipulate big graphs. The research advances knowledge across multiple disciplines and drives innovation in fields ranging from computer science and engineering to biology, sociology, and beyond.

For more information contact: [email protected]

Supervisory team:  Sri Parameswaran 

Project summary:  Reliability is becoming an essential part in embedded processor design due to the fact that they are used in safety critical applications and they need to deal with sensitive information. The first phase in the design of reliable embedded systems involves the identification of faults that could be manipulated into a reliability problem. A technique that is widely used for this identification process is called fault injection and analysis. The aim of this project is to develop a fault injection and detection engine at the hardware level for an embedded processor. 

For more information contact:  [email protected]

Human-Centred computing

Supervisory team: Dr Gelareh Mohammadi ,  Prof. Wenjie Zhang

Project description: Previous studies have shown that cognitive training can effectively improve people's skillsets and emotional capabilities in cognitive deficits. Such training programs are known to enhance the participants' brain health and better prepare them for an independent life. However, the existing conventional technologies for such training are not scalable and lack personalized features to optimize the efficacy. In this project, we will develop a technology platform for automatically acquiring and processing multimodal training data. The project will be conducted in collaboration with Stronger Brains, a not-for-profit organization that provides cognitive training. We aim to develop a fully automated social and cognitive function assessment framework based on multimodal data. Such a framework is essential to establish a  system with less involvement of experts and increase its scalability. The project involves:

• Data collection.
• Developing multimodal predictive models for cognitive functions and affective states in cognitive deficits.
• Developing adaptation techniques to personalize the framework.

Supervisory team: Dr Gelareh Mohammadi , A/Prof. Nadine Marcus

Project description: The fields of Science, Technology, Engineering and Math, otherwise known as STEM, play a key role in the sustained growth and stability of any economy and are a critical component in shaping the future of our society. This project aims to develop new evidence-based guidelines for designing highly effective teaching simulations for a STEM subject that personalizes training to learner proficiency. In particular, we aim to design a novel AI-powered framework for dynamic adaptive learning in STEM educational technology to improve learning outcomes in an accessible and engaging environment. The potential contributions of the project involve:

• Developing a multimodal physio-behavioural AI for rapid assessment of proficiency level.
• Integration of affective state and cognitive load with proficiency level to form a comprehensive cognitive diagnosis and capture the interplay between affective and cognitive processes.
• Establishing dynamic adaptive learning in real-time based on the cognitive diagnosis that responds to the current individual needs of the learner.

Networked systems and security

Supervisory team:  Sanjay Jha, Salil Kanhere 

Project summary:  This project aims to develop scalable and efficient one-to-many communication, that is, broadcast and multicast, algorithms in the next generation of WMNs that have multi-rate multi-channel nodes. This is a significant leap compared with the current state of the art of routing in WMNs, which is characterised by unicast in a single-rate single-channel environment. 

For more information contact:  [email protected]

Supervisory team:  Mahbub Hanssan 

Project summary:  A major focuses of the Swimnet project will be to look at a QoS framework for multi-radio multi-channel wireless mesh networks. We also plan to develop traffic engineering methodologies for multi-radio multi-channel wireless mesh networks. Guarding against malicious users is of paramount significance in WMN. Some of the major threats include greedy behaviour exploiting the vulnerabilities of the MAC layer, location-based attacks and lack of cooperation between the nodes. The project plans to look at a number of such security concerns and design efficient protection mechanisms (Mesh Security Architecture). 

For more information contact:  [email protected]   

Supervisory team:  Wen Hu  

Project summary:  The mission of the SENSAR (Sensor Applications Research) group is to investigate the systems and networking challenges in realising sensor network applications. Wireless sensor networks are one of the first real-world examples of "pervasive computing", the notion that small, smart and cheap, sensing and computing devices will eventually permeate the environment. Though the technologies still in their early days, the range of potential applications is vast - track bush fires, microclimates and pests in vineyards, monitor the nesting habits of rare sea-birds, and control heating and ventilation systems, let businesses monitor and control their workspaces, etc. 

For more information contact:  [email protected]

Supervisory team:  Boualem Benatallah, Lina Yao, Fabio Casati

Project summary:  This project investigates the significant and challenging issues that underpin the effective integration of software-enabled services with cognitive and conversational interfaces. Our work builds upon advances in natural language processing, conversational AI and services composition.

We aim to advance the fundamental understanding of cognitive services engineering by developing new abstractions and techniques. We’re seeking to enable and semi-automate the augmentation of software and human services with crowdsourcing and generative model training methods, latent knowledge and interaction models. These models are essential for the mapping of potentially ambiguous natural language interactions between users and semi-structured artefacts (for example, emails, PDF files), structured information (for example, indexed data sets), apps and APIs.

For more information contact:  [email protected]  or  [email protected]

Supervisory team:  Helen Paik

Project summary:  Micro-transactions stored in blockchain create transparent and traceable data and events, providing burgeoning industry disruptors an instrument for trust-less collaborations. However, the blockchain data and its’ models are highly diverse. To fully utilise its potential, a new technique to efficiently retrieve and analyse the data at scale is necessary.

This project addresses a significant gap in current research, producing a new data-oriented system architecture and data analytics framework optimised for online/offline data analysis across blockchain and associated systems. The outcome will strongly underpin blockchain data analytics at scale, fostering wider and effective adoption of blockchain applications. A scholarship/stipend may be available.

For more information contact:  [email protected]

Supervisory team: Fethi Rabhi

Project summary: This project investigates novel architectures & processes to develop AI and machine learning systems for business applications. This includes the use of AutoML and new collaborative “code-free” technologies to simplify AI system design/production within a large enterprise. This project will need a rethink of many traditional software engineering practices in areas of software architecture, development processes and requirements engineering. These issues are all interlinked e.g., adding business objectives may reduce usability and decrease performance, adding more transparency may obscure and decrease trust, and adding more usability may decrease performance. In some cases, ethical and compliance with regulations are other important considerations that need to be taken into account when developing the system.  The main application area is in the financial domain in collaboration with industry partners within the Fintech AI Innovation Consortium .

For more information contact [email protected]

Supervisory team: A/Prof. Yulei Sui

Project summary: Modern software repositories are vast, making understanding the source code of a project especially challenging, particularly for legacy code bases. This project aims to design a code language model to automatically generate source code, detect software vulnerabilities, and provide program repair suggestions by understanding the syntax and semantics of code information (e.g., control-flow and data-flows). This project will be based on our group's existing source code analysis and verification tool SVF . The expected deliverable of this project is an open-source tool that can accept, analyze, and parse user queries to interact with the code language model and SVF, generating high-quality codebases and analyzing large codebases consisting of millions of lines of code. You will work together with our team, including postdocs and PhD students, to conduct exciting research.

For more information contact: [email protected]

Supervisory team:  Gernot Heiser

Project summary:  Project summary: The Trustworthy Systems (TS) group are the creators of seL4, the world's first operating system (OS) kernel with a formal correctness proof. TS continues to conduct research at the intersection of OS, formal methods and programming languages, with the overall aim of producing real-world systems that are provably secure and safe, yet performant.

Specific projects include provable prevention of information leakage through microarchitectural timing channels; OS design and implementation for performance and verification; automatic verification and repeatable verification of OS components; verified compiler for the Pancake systems language; high-assurance worst-case execution-time analysis; provable schedulability of mixed-criticality safety-critical system.

For more information, including availability of scholarships, see https://trustworthy.systems/students/research , or contact [email protected]

Supervisory team: Dr Jesse Laeuchli, Dr Arash Shaghaghi, Prof Sanjay Jha

Project summary:  Remote and embedded devices are the lynchpin of modern networks. Satellites, Aircraft, Remote Sensors and Drones all require numerous embedded devices to function. A key part of ensuring these devices remain ready to carry out operations is to ensure their memory has not been corrupted by an adversary.

In this project we will explore methods for securing remote devices using early generation quantum computers. These have the ability to work with one or two qubits at a time, and operate with very limited quantum memory, but they still provide access to valuable quantum effects which can be used for security.  

The successful student will have an interest in both cyber-security and quantum computing, with a willingness to explore the mathematics needed to exploit quantum algorithms.

Eligibility: Domestic Candidates only, PhD only

For more information contact Dr Jesse Laeuchli or Dr Arash Shaghaghi .

Theoretical computer science

Supervisory team:  Ron van der Meyden 

Project summary:  The technology of cryptocurrency and its concepts can be broadly applicable to range of applications including financial services, legal automation, health informatics and international trade. These underlying ideas and the emerging infrastructure for these applications is known as ‘Distributed Ledger Technology’. 

For more information contact:  [email protected]   

Projects with top up scholarship for domestic students

Supervisors:

• Dr Gelareh Mohammadi ( [email protected] )
• Prof. Wenjie Zhang ( [email protected] )

Project description:

Previous studies have shown that cognitive training can effectively improve people's skillsets and emotional capabilities in cognitive deficits. Such training programs are known to enhance the participants' brain health and better prepare them for an independent life. However, the existing conventional technologies for such training are not scalable and lack personalized features to optimize the efficacy. In this project, we will develop a technology platform for automatically acquiring and processing multimodal training data. The project will be conducted in collaboration with Stronger Brains, a not-for-profit organization that provides cognitive training. We aim to develop a fully automated social and cognitive function assessment framework based on multimodal data. Such a framework is essential to establish a  system with less involvement of experts and increase its scalability. The project involves:

• A/Prof. Nadine Marcus  ([email protected] )

The fields of Science, Technology, Engineering and Math, otherwise known as STEM, play a key role in the sustained growth and stability of any economy and are a critical component in shaping the future of our society. This project aims to develop new evidence-based guidelines for designing highly effective teaching simulations for a STEM subject that personalizes training to learner proficiency. In particular, we aim to design a novel AI-powered framework for dynamic adaptive learning in STEM educational technology to improve learning outcomes in an accessible and engaging environment. The potential contributions of the project involve:

Supervisor:  Dr Rahat Masood ( [email protected] )

Supervisory team:  Prof Salil Kanhere (CSE - UNSW), Suranga Seneviratne (USyd), Prof Aruna Seneviratne (EE&T – UNSW)

Children start using the Internet from a very early age for entertainment and educational purposes and continue to do so into their teen years and beyond. In addition to providing the required functionality, the online services also collect information about their users, track them, and provide content that may be inappropriate such as sexually explicit content; content that promotes hate and violence, and other content compromising users’ safety. Another major issue is that there is no established mechanism to detect the age of users on online platforms hence, leading children to sign up for services that are inappropriate for them. Through this research work, we aim to develop an age detection framework that can help detect children’s activities on online platforms using various behavioural biometrics such as swipes, keystrokes, and handwriting. The core of this project revolves around the ground-breaking idea that “User Touch Gestures” contain sufficient information to uniquely identify them, and the “Touch Behaviour” of a child is very different from that of an adult, hence leading to child detection on online platforms. The success of this project will enable online service providers to detect the presence of children on their platforms and offer age-appropriate content accordingly.

• Dr Rahat Masood ( [email protected] )
• Prof. Salil Kanhere (CSE - UNSW)

Users unintentionally leave digital traces of their personal information, interests and intents while using online services, revealing sensitive information about them to online service providers. Though, some online services offer configurable privacy controls that limit access to user data. However, not all users are aware of these settings and those who know might misconfigure these controls due to the complexity or lack of clear instructions. The lack of privacy awareness combined with privacy breaches on the web leads to distrust among the users in online services. Through this research study, we intend to improve the trust of users on the web and mobile services by designing and developing user-centric privacy-preserving solutions that involve aspects of user privacy settings, user reactions and feedbacks on privacy alerts, user behavioural actions and user psychology. The aforementioned factors will be first used in quantifying privacy risks and later used in designing privacy-preserving solutions. In essence, we aim to improve privacy in mobile and web platforms by investigating various human factors in: i) privacy risk quantification and assessment, and ii) privacy-preserving solutions.

• Dr Yang Song ( [email protected] ) - primary
• Prof. Maurice Pagnucco - joint

Deep learning techniques have shown great success in many applications, such as computer vision and natural language processing. However, in many cases, purely data-driven approaches would provide suboptimal results, especially when limited data are available for training the models. This dependency on large-scale training data is well understood as the main limitation of deep learning models. One way to mitigate this problem is to incorporate knowledge priors into the model, similarly to how humans reason with data; and there are various types of knowledge priors, such as data-specific relational information, knowledge graphs, logic rules and statistical modelling. In this PhD project, we will investigate novel methods that effectively integrate knowledge priors and commonsense reasoning with deep learning models. Such models can be developed for a wide range of application domains, such as computer vision, social networks, biological discovery and human-robot interaction.

Deep learning models are typically considered a black-box, and the lack of explainability has become a major obstacle to deploy deep learning models to critical applications such as medicine and finance. Explainable AI has thus become an important topic in research and industry, especially in the deep learning era. Various methods for explaining deep learning models have been developed, and we are especially interested in explainability in graph neural networks, which is a new topic that has emerged very recently. Graph neural networks are becoming increasingly popular due to their inherent capability of representing graph structured data, yet their explainability is more challenging to explore with the irregular and dynamic nature of graphs. In this PhD project, we will investigate novel ways of modelling explainability in graph neural networks, and apply this to various applications, such as computer vision, biological studies, recommender systems and social network analysis.

Supervision team

•  Dr Jiaojiao Jiao ( [email protected] ) – primary
•  Prof. Sanjay Jha ( [email protected] ) – joint
•  Stephen Doherty ( [email protected] ) - secondary

Most cyber threat intelligence platforms provide scores and metrics that are mainly derived from open-source and external sources. Organisations must then figure out if and how the output is relevant to them.

Research problems

• Dynamic threat risk/exposure score

Continuous monitoring and calculation of an organisation’s ‘Threat Risk’ posture score using a range of internal and external intelligence.

• Customised/targeted newsfeed

A curated cyber and threat newsfeed that is relevant to an organisation. The source of the newsfeed will leverage the internal and external analysis from the first question. The output will include information that helps users understand and digest their organisation’s threat posture in a non-technical manner.

Proposed approaches

We propose to develop dynamic GNN models for discovering dynamic cyber threat intelligence from blended sources. GNN has achieved state-of-the-art performance in many high-impact applications, such as fraud detection, information retrieval, and recommender systems, due to their powerful representation learning capabilities. We propose to develop new GNN models which can take blended intelligence sources into account in the threat intelligence prediction. Moreover, many GNN models are static that deal with fixed structures and parameters. Therefore, we propose to develop dynamic GNN models which can learn the evolution pattern or persistent pattern of dynamic graphs.

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PhD Topics in Computer Science for Real-World Applications

Welcome to the fascinating world of PhD topics in computer science , where innovation, intellect, and real-world applications converge to pave the way for groundbreaking research. In this world of limitless possibilities, computer science PhD topics offer an unparalleled opportunity for aspiring researchers to delve into cutting-edge domains, unleashing their creativity to address the pressing challenges of our time. Embark on a journey of intellectual exploration as we uncover the most captivating and relevant computer science topics for PhD research, guiding you towards shaping the future through your passion for technology and its transformative potential. 

Some Specific Examples of Computer Science Topics For PhD Research That Have Real-World Applications

1 . AI-Powered Healthcare Diagnostics:

Computer science plays a critical role in advancing healthcare diagnostics through artificial intelligence (AI). By leveraging machine learning and deep learning algorithms, researchers can develop systems capable of accurately diagnosing medical conditions from various sources such as medical imaging, patient records, and genetic data. A potential PhD topic in this field could focus on:

- Deep Learning for Medical Image Analysis: Develop advanced convolutional neural networks (CNNs) or other deep learning models to automatically analyze medical images like X-rays, MRIs, or CT scans. The aim is to detect and classify abnormalities, enabling early detection and precise diagnosis.

- Predictive Analytics for Personalized Medicine: Utilize AI techniques to analyze patient data and identify patterns that can lead to personalized treatment plans. By integrating genetic information, medical history, and lifestyle data, the research can help tailor treatments to individual patients, optimizing outcomes.

2. Sustainable Smart Cities:

Computer science offers innovative solutions for creating energy-efficient and sustainable smart cities, integrating information technology with urban infrastructure. A PhD research topic in this domain could explore:

- IoT-Based Resource Management: Design and implement Internet of Things (IoT) solutions to monitor and manage resource consumption in cities, such as energy, water, and waste. Develop algorithms that optimize resource allocation and reduce environmental impact.

- Smart Transportation Systems: Propose intelligent transportation systems that use real-time data, including traffic patterns, public transport usage, and weather conditions, to optimize commuting and reduce congestion, thereby lowering carbon emissions.

3. Cybersecurity for Critical Infrastructures :

With the growing dependence on digital systems, securing critical infrastructures is of paramount importance. A PhD research topic in this field can focus on:

- Threat Detection and Response: Develop AI-driven cybersecurity solutions that use machine learning algorithms to detect and respond to cyber threats in real-time, enhancing the resilience of critical infrastructure systems.

- Blockchain-Based Security for Critical Systems: Investigate the applications of blockchain technology in securing critical infrastructure, such as ensuring the integrity of data and facilitating secure communication between components.

4. Autonomous Systems for Disaster Response:

Autonomous systems can significantly improve disaster response efforts, reducing the risks to human responders and enhancing the speed and effectiveness of rescue missions. A potential PhD topic in this area could be:

- Swarm Robotics for Disaster Response: Explore swarm robotics, where a large number of small robots collaborate to execute search and rescue missions in disaster-stricken areas. Develop algorithms for coordination, path planning, and communication among the robots.

- Real-Time Environmental Sensing with Drones: Investigate the use of drones equipped with sensors to collect real-time data on disaster-affected regions. Develop AI-powered algorithms to analyze this data and aid in decision-making during disaster response operations.

5. Natural Language Processing for Multilingual Communication :

Breaking down language barriers through natural language processing (NLP) can have significant societal and economic impacts. A PhD topic in this area could focus on:

- Cross-Lingual Information Retrieval: Develop NLP algorithms that enable users to search for information in one language and retrieve relevant results from documents in multiple languages, fostering global information access.

- Multilingual Sentiment Analysis: Explore sentiment analysis techniques that can accurately determine emotions and opinions expressed in text across different languages. This research can find applications in brand monitoring, customer feedback analysis, and social media sentiment tracking.

Identifying a Research Topic That Aligns With Both Researchers’ Interests and the Current Needs of Industries

1. Self-Reflection and Passion Discovery: Begin by delving deep into your own interests and strengths within computer science. What excites you the most? What problems ignite your curiosity? Identifying your true passions will pave the way for a research topic that you can wholeheartedly dedicate yourself to.

2. Stay Abreast of Industry Trends: Immerse yourself in the dynamic landscape of computer science industries. Follow the latest advancements, read research papers, and attend conferences to understand the pressing challenges faced by technology-driven sectors. Engaging with industry experts and professionals can provide valuable insights into potential research gaps.

3. Dialogue with Academic Mentors: Seek guidance from experienced academics or mentors in the field of computer science. They can help you refine your research interests and align them with the current needs of industries and society. Discussions with experts can unearth potential avenues for impactful research.

4. Collaborate and Network: Engage in interdisciplinary collaborations with researchers from diverse fields. This can open up new perspectives and reveal exciting intersections between your interests and real-world challenges. Attend workshops and seminars to expand your network and gain fresh ideas.

5. Literature Review and Gap Analysis: Conduct a thorough literature review to understand the existing body of knowledge in your chosen area. Identify gaps where your expertise can contribute to solving practical problems. Building upon existing research ensures your work remains relevant and impactful.

At PhD Box, we understand that identifying a research topic that perfectly aligns with your passions and addresses real-world needs is crucial for a fulfilling PhD journey. Our program is designed to support you in this exhilarating quest by providing personalized assistance throughout the process. Through tailored guidance from experienced academics and industry experts, we help you explore your interests, refine your research goals, and identify the most relevant and impactful topics. At PhD Box, we are dedicated to empowering you to embark on a transformative PhD journey, where your passion and expertise converge to create tangible real-world solutions that make a positive and lasting impact.

Striking a Balance Between Theoretical Rigor and Practical Implementation in the Chosen PhD Topic

1. Strong Theoretical Foundation: Lay a sturdy groundwork by thoroughly understanding the theoretical underpinnings of your chosen PhD topic. Immerse yourself in existing literature, grasp fundamental concepts, and study relevant methodologies. A robust theoretical foundation is the bedrock of innovative and impactful research.

2. Identify Real-World Challenges: Ground your research in real-world challenges faced by industries, communities, or societal domains. Strive to comprehend the practical implications of your work and align it with the needs of those who can benefit from your contributions.

3. Formulate Concrete Objectives: Define clear and achievable research objectives that bridge the gap between theory and practice. Outline tangible goals and outcomes that showcase the potential for real-world application and address specific issues.

4. Iterative Prototyping and Testing: Embrace the iterative nature of research. Develop prototypes and practical implementations to validate your theoretical findings. Rigorously test your solutions in simulated or real-world scenarios to ensure their practicality and effectiveness.

5. Engage with End-Users: Collaborate with end-users, industry professionals, or stakeholders who can provide valuable feedback on your research. Involving them from the early stages can offer insights into practical challenges and improve the applicability of your work.

At PhD Box, we recognize the significance of striking a harmonious balance between theoretical rigour and practical implementation in your chosen computer science PhD topic. Our program is tailored to equip you with the tools and support needed to achieve this delicate balance successfully. Through our expert guidance, you can develop a strong theoretical foundation, ensuring that your research is built on solid academic principles. Our cutting-edge resources empower you to prototype and test your solutions, bridging the gap between theory and real-world applicability. At PhD Box, we are committed to nurturing your research journey, empowering you to navigate the complexities of theoretical and practical aspects seamlessly. Let us be your trusted ally in crafting a PhD endeavour that not only showcases theoretical excellence but also translates into tangible, relevant, and impactful contributions in real-world settings.

Final Thoughts

Pursuing a PhD in computer science offers an exhilarating journey of innovation and research, where interdisciplinary collaboration, staying informed about current trends, and focusing on real-world applications play crucial roles. While the process of finding the right topic may be challenging, grounding research in a strong theoretical foundation and identifying gaps in existing literature can aid in narrowing down suitable directions. By embracing determination, dedication, and a passion for making a meaningful difference, computer scientists can leave an indelible mark on the world, contributing to the ever-evolving landscape of technology and addressing pressing global challenges. Let us embark together on this remarkable quest to shape the future of computer science.

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PhD Assistance

How to select the right topic for your phd in computer science, introduction  .

Starting a PhD in Computer Science is an exciting but demanding effort, and choosing the correct computer science research topics is critical to a successful and rewarding experience. This critical decision not only influences the course of your academic interests, but also the effect of your contributions to the field. In this blog, we will look at crucial factors to consider when selecting a research subject, such as connecting with your passion, discovering gaps in current literature, and determining the feasibility of the project. By navigating this process with awareness and strategy, you will be able to begin a meaningful and effective doctorate research path in the dynamic field of computer science.  

• Check our PhD Topic selection examples to learn about how we review or edit an article for Topic selection.  

PhD in computer science is a terminal degree in computer science along with the doctorate in Computer Science, although it is not considered an equivalent degree. Computer science deals with algorithms and data and the computation of them via hardware and software, the principles and constraints involved in the implementation. Choosing a topic for research in computer science can be tricky. The field is as vast as its parent field, mathematics. Taking into account certain factors before choosing a topic will be helpful: it is preferable to choose a topic which is currently being studied by other fellow researchers, this will help to establish bonds and sharing secondary data. Finding a topic that will add value to the field and result in the betterment of existing processes will cement your legacy within the field and will also be helpful in getting funds. Always choose a topic that you are passionate about. Your interest in the topic will help in the long run; PhD research is a long, exhausting process and computational researches will dry you out. If you have an area of interest, read about the existing developments, processes, researches. Reading as much literature as possible will help you identify certain or several research gaps. You can consult with your mentor and choose a particular gap that would be feasible for your research. An extension of the previous method of spotting a research gap is to build on references for future research given in existing dissertations by former researchers. You can be critical of existing limitations and study it.

Besides, there are plenty of enigmatic areas in computer science. The unsolved questions within computer science plenty which you can study and find a solution to build on the existing body of knowledge. Major titles with unsolved questions for research in Computer Science

Computational complexity

The process of arranging computational process according to complexity based on algorithm has had various problems that are unsolved. This includes the Classic P versus the NP, the relationship between NQP and P, NP not known to be P or NP-complete, unique games conjecture, separations between other complexity cases, etc.

Polynomial versus non-polynomial time for specific algorithmic problems

A continuation in computational complexity is the complex case of NP- intermediate which contains within numerous unsolved problems related to algebra and number theory, Boolean logic, computational geometry, and computational topology, game theory, graph algorithm, etc.

Algorithmic problems

Scores of questions within the existing algorithm in computer science can be improved with new processes.

Natural Language Processing algorithms

Natural language processing is an important field within computer science with the onset of deep learning and Artificial and Intelligence. Plenty of researches are being carried in the field to find faster and perfect ways to syllabify, stem, and POS tag algorithms specifically for the English language.

Programming language theory

The case for scope of research about programming language within computer science is evergreen. There are always ways to design, implement, analyze, characterize, and classify programming languages and to develop newer languages.

• Check out our study guide to learn more about How to Select the Best Topics for Research?  

Conclusion:  

In conclusion, the journey of selecting the right PhD topic in computer science topics is a pivotal phase requiring careful deliberation. By combining passion, alignment with current computer science phd topics trends, and feasibility assessment, one can pave the way for a successful and rewarding research endeavor. Remember, the chosen topic will not only define your academic trajectory but also contribute to the evolving landscape of computer science thesis topics. Embrace the challenge with purpose, stay adaptable, and ensure that your research aligns with both personal interests and the broader needs of the field. With these considerations, you are poised to make a lasting impact in the world of Computer Science.  

Example Research Topics in Technology and Computer Science    

• Role of human-computer interaction   
• AI and robotics   
• Software engineering and programming   
• Machine learning and neuron networks  

About PhD Assistance  

At PhD Assistance , we have a team of trained research specialists with topic selection experience. Our writers and researchers have extensive expertise in selecting the appropriate topic and title for a PhD dissertation based on their Specialized subject and personal interests. Furthermore, our professionals are drawn from worldwide and top-ranked colleges in nations such as the United States, United Kingdom, and India. Our writers have the expertise and understanding to choose a PhD research subject that is actually excellent for your study, as well as a snappy title that is unquestionably appropriate for your research aim.  

In summary, it is important to keep in mind the following to choose an apt topic for your PhD research in Computer Science:

Your passion for an area of research

Appositeness of the topic

Feasibility of the research with respect to the availability of the resource

Providing a solution to a practical problem.

Topic selection help for computer science students  

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Home > Engineering > Computer Science > Computer Science Graduate Projects

Computer Science Graduate Projects and Theses

Theses/dissertations from 2023 2023.

High-Performance Domain-Specific Library for Hydrologic Data Processing , Kalyan Bhetwal

Evaluating Learning Geometric Concepts to Generate Predicate Abstract Domains in Static Program Analysis , Patrick Chadbourne

Verifying Data Provenance During Workflow Execution for Scientific Reproducibility , Rizbanul Hasan

Remote Sensing to Advance Understanding of Snow-Vegetation Relationships and Quantify Snow Depth and Snow Water Equivalent , Ahmad Hojatimalekshah

Exploring the Capability of a Self-Supervised Conditional Image Generator for Image-to-Image Translation without Labeled Data: A Case Study in Mobile User Interface Design , Hailee Kiesecker

Fake News Detection Using Narrative Content and Discourse , Hongmin Kim

Anomaly Detection Using Graph Neural Network , Bishal Lakha

Robust Digital Nucleic Acid Memory , Golam Md Mortuza

Risk Assessment and Solutions for Two Domains: Election Procedures and Privacy Disclosure Prevention for Users , Kamryn DeAnn Parker

Sparse Format Conversion and Code Synthesis , Tobi Goodness Popoola

Fair Layouts in Information Access Systems: Provider-Side Group Fairness in Ranking Beyond Ranked Lists , Amifa Raj

Virtual Curtain: A Communicative Fine-Grained Privacy Control Framework for Augmented Reality , Aakash Shrestha

Portable Sparse Polyhedral Framework Code Generation Using Multi Level Intermediate Representation , Aaron St. George

Transformer Reinforcement Learning Approach to Attack Automatic Fake News Detectors , Chandler Underwood

Severity Measures for Assessing Error in Automatic Speech Recognition , Ryan Whetten

Theses/Dissertations from 2022 2022

Improved Computational Prediction of Function and Structural Representation of Self-Cleaving Ribozymes with Enhanced Parameter Selection and Library Design , James D. Beck

Meshfree Methods for PDEs on Surfaces , Andrew Michael Jones

Deep Learning of Microstructures , Amir Abbas Kazemzadeh Farizhandi

Long-Term Trends in Extreme Environmental Events with Changepoint Detection , Mintaek Lee

Structure Aware Smart Encoding and Decoding of Information in DNA , Shoshanna Llewellyn

Towards Making Transformer-Based Language Models Learn How Children Learn , Yousra Mahdy

Ontology-Based Formal Approach for Safety and Security Verification of Industrial Control Systems , Ramesh Neupane

Improving Children's Authentication Practices with Respect to Graphical Authentication Mechanism , Dhanush Kumar Ratakonda

Hate Speech Detection Using Textual and User Features , Rohan Raut

Automated Detection of Sockpuppet Accounts in Wikipedia , Mostofa Najmus Sakib

Characterization and Mitigation of False Information on the Web , Anu Shrestha

Sinusoidal Projection for 360° Image Compression and Triangular Discrete Cosine Transform Impact in the JPEG Pipeline , Iker Vazquez Lopez

Theses/Dissertations from 2021 2021

Training Wheels for Web Search: Multi-Perspective Learning to Rank to Support Children's Information Seeking in the Classroom , Garrett Allen

Fair and Efficient Consensus Protocols for Secure Blockchain Applications , Golam Dastoger Bashar

Why Don't You Act Your Age?: Recognizing the Stereotypical 8-12 Year Old Searcher by Their Search Behavior , Michael Green

Ensuring Consistency and Efficiency of the Incremental Unit Network in a Distributed Architecture , Mir Tahsin Imtiaz

Modeling Real and Fake News Sharing in Social Networks , Abishai Joy

Modeling and Analyzing Users' Privacy Disclosure Behavior to Generate Personalized Privacy Policies , A.K.M. Nuhil Mehdy

Into the Unknown: Exploration of Search Engines' Responses to Users with Depression and Anxiety , Ashlee Milton

Generating Test Inputs from String Constraints with an Automata-Based Solver , Marlin Roberts

A Case Study in Representing Scientific Applications ( GeoAc ) Using the Sparse Polyhedral Framework , Ravi Shankar

Actors for the Internet of Things , Arjun Shukla

Theses/Dissertations from 2020 2020

Towards Unifying Grounded and Distributional Semantics Using the Words-as-Classifiers Model of Lexical Semantics , Stacy Black

Improving Scientist Productivity, Architecture Portability, and Performance in ParFlow , Michael Burke

Polyhedral+Dataflow Graphs , Eddie C. Davis

Improving Spellchecking for Children: Correction and Design , Brody Downs

A Collection of Fast Algorithms for Scalar and Vector-Valued Data on Irregular Domains: Spherical Harmonic Analysis, Divergence-Free/Curl-Free Radial Basis Functions, and Implicit Surface Reconstruction , Kathryn Primrose Drake

Privacy-Preserving Protocol for Atomic Swap Between Blockchains , Kiran Gurung

Unsupervised Structural Graph Node Representation Learning , Mikel Joaristi

Detecting Undisclosed Paid Editing in Wikipedia , Nikesh Joshi

Do You Feel Me?: Learning Language from Humans with Robot Emotional Displays , David McNeill

Obtaining Real-World Benchmark Programs from Open-Source Repositories Through Abstract-Semantics Preserving Transformations , Maria Anne Rachel Paquin

Content Based Image Retrieval (CBIR) for Brand Logos , Enjal Parajuli

A Resilience Metric for Modern Power Distribution Systems , Tyler Bennett Phillips

Theses/Dissertations from 2019 2019

Edge-Assisted Workload-Aware Image Processing System , Anil Acharya

MINOS: Unsupervised Netflow-Based Detection of Infected and Attacked Hosts, and Attack Time in Large Networks , Mousume Bhowmick

Deviant: A Mutation Testing Tool for Solidity Smart Contracts , Patrick Chapman

Querying Over Encrypted Databases in a Cloud Environment , Jake Douglas

A Hybrid Model to Detect Fake News , Indhumathi Gurunathan

Suitability of Finite State Automata to Model String Constraints in Probablistic Symbolic Execution , Andrew Harris

UNICORN Framework: A User-Centric Approach Toward Formal Verification of Privacy Norms , Rezvan Joshaghani

Detection and Countermeasure of Saturation Attacks in Software-Defined Networks , Samer Yousef Khamaiseh

Secure Two-Party Protocol for Privacy-Preserving Classification via Differential Privacy , Manish Kumar

Application-Specific Memory Subsystem Benchmarking , Mahesh Lakshminarasimhan

Multilingual Information Retrieval: A Representation Building Perspective , Ion Madrazo

Improved Study of Side-Channel Attacks Using Recurrent Neural Networks , Muhammad Abu Naser Rony Chowdhury

Investigating the Effects of Social and Temporal Dynamics in Fitness Games on Children's Physical Activity , Ankita Samariya

BullyNet: Unmasking Cyberbullies on Social Networks , Aparna Sankaran

FALCON: Framework for Anomaly Detection In Industrial Control Systems , Subin Sapkota

Investigating Semantic Properties of Images Generated from Natural Language Using Neural Networks , Samuel Ward Schrader

Incremental Processing for Improving Conversational Grounding in a Chatbot , Aprajita Shukla

Estimating Error and Bias of Offline Recommender System Evaluation Results , Mucun Tian

Theses/Dissertations from 2018 2018

Leveraging Tiled Display for Big Data Visualization Using D3.js , Ujjwal Acharya

Fostering the Retrieval of Suitable Web Resources in Response to Children's Educational Search Tasks , Oghenemaro Deborah Anuyah

Privacy-Preserving Genomic Data Publishing via Differential Privacy , Tanya Khatri

Injecting Control Commands Through Sensory Channel: Attack and Defense , Farhad Rasapour

Strong Mutation-Based Test Generation of XACML Policies , Roshan Shrestha

Performance, Scalability, and Robustness in Distributed File Tree Copy , Christopher Robert Sutton

Using DNA For Data Storage: Encoding and Decoding Algorithm Development , Kelsey Suyehira

Detecting Saliency by Combining Speech and Object Detection in Indoor Environments , Kiran Thapa

Theses/Dissertations from 2017 2017

Identifying Restaurants Proposing Novel Kinds of Cuisines: Using Yelp Reviews , Haritha Akella

Editing Behavior Analysis and Prediction of Active/Inactive Users in Wikipedia , Harish Arelli

CloudSkulk: Design of a Nested Virtual Machine Based Rootkit-in-the-Middle Attack , Joseph Anthony Connelly

Predicting Friendship Strength in Facebook , Nitish Dhakal

Privacy-Preserving Trajectory Data Publishing via Differential Privacy , Ishita Dwivedi

Cultivating Community Interactions in Citizen Science: Connecting People to Each Other and the Environment , Bret Allen Finley

Uncovering New Links Through Interaction Duration , Laxmi Amulya Gundala

Variance: Secure Two-Party Protocol for Solving Yao's Millionaires' Problem in Bitcoin , Joshua Holmes

A Scalable Graph-Coarsening Based Index for Dynamic Graph Databases , Akshay Kansal

Integrity Coded Databases: Ensuring Correctness and Freshness of Outsourced Databases , Ujwal Karki

Editable View Optimized Tone Mapping For Viewing High Dynamic Range Panoramas On Head Mounted Display , Yuan Li

The Effects of Pair-Programming in a High School Introductory Computer Science Class , Ken Manship

Towards Automatic Repair of XACML Policies , Shuai Peng

Identification of Unknown Landscape Types Using CNN Transfer Learning , Ashish Sharma

Hand Gesture Recognition for Sign Language Transcription , Iker Vazquez Lopez

Learning to Code Music : Development of a Supplemental Unit for High School Computer Science , Kelsey Wright

Theses/Dissertations from 2016 2016

Identification of Small Endogenous Viral Elements within Host Genomes , Edward C. Davis Jr.

When the System Becomes Your Personal Docent: Curated Book Recommendations , Nevena Dragovic

Security Testing with Misuse Case Modeling , Samer Yousef Khamaiseh

Estimating Length Statistics of Aggregate Fried Potato Product via Electromagnetic Radiation Attenuation , Jesse Lovitt

Towards Multipurpose Readability Assessment , Ion Madrazo

Evaluation of Topic Models for Content-Based Popularity Prediction on Social Microblogs , Axel Magnuson

CEST: City Event Summarization using Twitter , Deepa Mallela

Developing an ABAC-Based Grant Proposal Workflow Management System , Milson Munakami

Phoenix and Hive as Alternatives to RDBMS , Diana Ornelas

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Computer Science Ph.D. Program

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The Cornell Ph.D. program in computer science is consistently ranked among the top six departments in the country, with world-class research covering all of computer science. Our computer science program is distinguished by the excellence of the faculty, by a long tradition of pioneering research, and by the breadth of its Ph.D. program. Faculty and Ph.D. students are located both in Ithaca and in New York City at the Cornell Tech campus . The Field of Computer Science also includes faculty members from other departments (Electrical Engineering, Information Science, Applied Math, Mathematics, Operations Research and Industrial Engineering, Mechanical and Aerospace Engineering, Computational Biology, and Architecture) who can supervise a student's Ph.D. thesis research in computer science.

Over the past years we've increased our strength in areas such as artificial intelligence, computer graphics, systems, security, machine learning, and digital libraries, while maintaining our depth in traditional areas such as theory, programming languages and scientific computing.  You can find out more about our research here . 

The department provides an exceptionally open and friendly atmosphere that encourages the sharing of ideas across all areas. 

Cornell is located in the heart of the Finger Lakes region. This beautiful area provides many opportunities for recreational activities such as sailing, windsurfing, canoeing, kayaking, both downhill and cross-country skiing, ice skating, rock climbing, hiking, camping, and brewery/cider/wine-tasting. In fact, Cornell offers courses in all of these activities.

The Cornell Tech campus in New York City is located on Roosevelt Island.  Cornell Tech  is a graduate school conceived and implemented expressly to integrate the study of technology with business, law, and design. There are now over a half-dozen masters programs on offer as well as doctoral studies.

FAQ with more information about the two campuses .

Ph.D. Program Structure

Each year, about 30-40 new Ph.D. students join the department. During the first two semesters, students become familiar with the faculty members and their areas of research by taking graduate courses, attending research seminars, and participating in research projects. By the end of the first year, each student selects a specific area and forms a committee based on the student's research interests. This “Special Committee” of three or more faculty members will guide the student through to a Ph.D. dissertation. Ph.D. students that decide to work with a faculty member based at Cornell Tech typically move to New York City after a year in Ithaca.

The Field believes that certain areas are so fundamental to Computer Science that all students should be competent in them. Ph.D. candidates are expected to demonstrate competency in four areas of computer science at the high undergraduate level: theory, programming languages, systems, and artificial intelligence.

Each student then focuses on a specific topic of research and begins a preliminary investigation of that topic. The initial results are presented during a comprehensive oral evaluation, which is administered by the members of the student's Special Committee. The objective of this examination, usually taken in the third year, is to evaluate a student's ability to undertake original research at the Ph.D. level.

The final oral examination, a public defense of the dissertation, is taken before the Special Committee.

To encourage students to explore areas other than Computer Science, the department requires that students complete an outside minor. Cornell offers almost 90 fields from which a minor can be chosen. Some students elect to minor in related fields such as Applied Mathematics, Information Science, Electrical Engineering, or Operations Research. Others use this opportunity to pursue interests as diverse as Music, Theater, Psychology, Women's Studies, Philosophy, and Finance.

The computer science Ph.D. program complies with the requirements of the Cornell Graduate School , which include requirements on residency, minimum grades, examinations, and dissertation.

The Department also administers a very small 2-year Master of Science program (with thesis). Students in this program serve as teaching assistants and receive full tuition plus a stipend for their services.

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PhD in Computer Science

The PhD in Computer Science is a small and selective program at Pace University that aims to cultivate advanced computing research scholars and professionals who will excel in both industry and academia. By enrolling in this program, you will be on your way to joining a select group at the very nexus of technological thought and application.

Learn more about the PhD in Computer Science .

Forms and Research Areas

General forms.

• PhD Policies and Procedures Manual – The manual contains all the information you need before, during, and toward the end of your studies in the PhD program.
• Advisor Approval Form (PDF) – Completed by student and approved by faculty member agreeing to the role as advisor.
• Committee Member Approval Form (PDF) – Completed by student with signatures of each faculty member agreeing to be on dissertation committee.
• Change in Advisor or Committee Member Approval Form (PDF) – Completed by student with the approval of new advisor or committee member. Department Chair approval needed.
• Qualifying Exam Approval Form (PDF) – Complete and return form to the Program Coordinator no later than Week 6 of the semester.

Dissertation Proposal of Defense Forms

• Application for the Dissertation Proposal of Defense Form (PDF) – Completed by student with the approval of committee members that dissertation proposal is sufficient to defend. Completed form and abstract and submitted to program coordinator for scheduling of defense.
• Dissertation Proposal Defense Evaluation Form (PDF) – To be completed by committee members after student has defended his dissertation proposal.

Final Dissertation Defense Forms

• Dissertation Pre- Defense Approval Form (PDF) – Committee approval certifying that the dissertation is sufficiently developed for a defense.
• Dissertation Defense Evaluation Form (PDF) – Completed by committee members after student has defended his dissertation.

All completed forms submitted to the program coordinator.

Research Areas

The Seidenberg School’s PhD in Computer Science covers a wealth of research areas. We pride ourselves on engaging with every opportunity the computer science field presents. Check out some of our specialties below for examples of just some of the topics we cover at Seidenberg. If you have a particular field of study you are interested in that is not listed below, just get in touch with us and we can discuss opportunities and prospects.

Some of the research areas you can explore at Seidenberg include:

Algorithms And Distributed Computing

Algorithms research in Distributed Computing contributes to a myriad of applications, such as Cloud Computing, Grid Computing, Distributed Databases, Cellular Networks, Wireless Networks, Wearable Monitoring Systems, and many others. Being traditionally a topic of theoretical interest, with the advent of new technologies and the accumulation of massive volumes of data to analyze, theoretical and experimental research on efficient algorithms has become of paramount importance. Accordingly, many forefront technology companies base 80-90% of their software-developer hiring processes on foundational algorithms questions. The Seidenberg faculty has internationally recognized strength in algorithms research for Ad-hoc Wireless Networks embedded in IoT Systems, Mobile Networks, Sensor Networks, Crowd Computing, Cloud Computing, and other related areas. Collaborations on these topics include prestigious research institutions world-wide.

Machine Learning In Medical Image Analysis

Machine learning in medical imaging is a potentially disruptive technology. Deep learning, especially convolutional neural networks (CNN), have been successfully applied in many aspects of medical image analysis, including disease severity classification, region of interest detection, segmentation, registration, disease progression prediction, and other tasks. The Seidenberg School maintains a research track on applying cutting-edge machine learning methods to assist medical image analysis and clinical data fusion. The purpose is to develop computer-aided and decision-supporting systems for medical research and applications.

Pattern recognition, artificial intelligence, data mining, intelligent agents, computer vision, and data mining are topics that are all incorporated into the field of robotics. The Seidenberg School has a robust robotics program that combines these topics in a meaningful program which provides students with a solid foundation in the robotics sphere and allows for specialization into deeper research areas.

Cybersecurity

The Seidenberg School has an excellent track record when it comes to cybersecurity research. We lead the nation in web security, developing secure web applications, and research into cloud security and trust. Since 2004, Seidenberg has been designated a Center of Academic Excellence in Information Assurance Education three times by the National Security Agency and the Department of Homeland Security and is now a Center of Academic Excellence in Cyber Defense Education. We also secured more than $2,000,000 in federal and private funding for cybersecurity research during the past few years.

Pattern Recognition And Machine Learning

Just as humans take actions based on their sensory input, pattern recognition and machine learning systems operate on raw data and take actions based on the categories of the patterns. These systems can be developed from labeled training data (supervised learning) or from unlabeled training data (unsupervised learning). Pattern recognition and machine learning technology is used in diverse application areas such as optical character recognition, speech recognition, and biometrics. The Seidenberg faculty has recognized strengths in many areas of pattern recognition and machine learning, particularly handwriting recognition and pen computing, speech and medical applications, and applications that combine human and machine capabilities.

A popular application of pattern recognition and machine learning in recent years has been in the area of biometrics. Biometrics is the science and technology of measuring and statistically analyzing human physiological and behavioral characteristics. The physiological characteristics include face recognition, DNA, fingerprint, and iris recognition, while the behavioral characteristics include typing dynamics, gait, and voice. The Seidenberg faculty has nationally recognized strength in biometrics, particularly behavioral biometrics dealing with humans interacting with computers and smartphones.

Big Data Analytics

The term “Big Data” is used for data so large and complex that it becomes difficult to process using traditional structured data processing technology. Big data analytics is the science that enables organizations to analyze a mixture of structured, semi-structured, and unstructured data in search of valuable information and insights. The data come from many areas, including meteorology, genomics, environmental research, and the internet. This science uses many machine learning algorithms and the challenges include data capture, search, storage, analysis, and visualization.

Business Process Modeling

Business Process Modeling is the emerging technology for automating the execution and integration of business processes. The BPMN-based business process modeling enables precise modeling and optimization of business processes, and BPEL-based automatic business execution enables effective computing service and business integration and effective auditing. Seidenberg was among the first in the nation to introduce BPM into curricula and research.

Educational Approaches Using Emerging Computing Technologies

The traditional classroom setting doesn’t suit everyone, which is why many teachers and students are choosing to use the web to teach, study, and learn. Pace University offers online bachelor's degrees through NACTEL and Pace Online, and many classes at the Seidenberg School and Pace University as a whole are available to students online.

The Seidenberg School’s research into new educational approaches include innovative spiral education models, portable Seidenberg labs based on cloud computing and computing virtualization with which students can work in personal enterprise IT environment anytime anywhere, and creating new semantic tools for personalized cyber-learning.

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Idrees, Ifrah

• Stefanie A Tellex , advisor

Kumar, Indra

• Suresh Venkatasubramanian , advisor

Nguyen, Thao

Nokiz, Pegah

Ren, Yanyan

• Kathi Fisler , advisor

Rosenbloom, Leah

• Anna Lysyanskaya , advisor

Traylor, Aaron

• Ellie Pavlick , advisor

Zerveas, George

• Carsten Eickhoff , advisor

Abbatematteo, Ben - Exploiting Structure for Efficient Robotic Manipulation

• George D. Konidaris , advisor

Allen, Cameron

Corsaro, Matthew - Learning Task-Specific Grasps

Ebert, Dylan

Engel, Daniel

• Maurice P Herlihy , advisor

Ibrahim, Abdelrahman

• Sherief Reda , advisor

Kristo, Ani

Lee, Jun Ki

Lovering, Charles

Markatou, Evangelia

• Roberto Tamassia , advisor

Naseer, Usama

•Theophilus Benson, advisor

Patel, Roma

Rahimzadeh Ilkhechi, Amir

• Ugur Cetintemel , advisor

Rosen, Eric

Spiegelberg, Leonhard

• Malte Schwarzkopf , advisor

Wallace, Shaun

• Jeff Huang , advisor

Wang, Kai - Learning Autoregressive Generative Models of 3D Shapes and Scenes

• Daniel C Ritchie , advisor

Webson, Albert - Tuning Language Models to Follow Instructions

Xue, Yingjie

Zheng, Kaiyu

Computer Science at Brown University Providence, Rhode Island 02912 USA Phone: 401-863-7600 Map & Directions / Contact Us

We have 155 Latest Computer Science PhD Projects, Programmes & Scholarships

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Latest Computer Science PhD Projects, Programmes & Scholarships

Doctoral research associate - eu msca fellow, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Funded PhD Project (Students Worldwide)

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Joining up Solar and Stellar Flare Energy Estimates

Self-funded phd students only.

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Integrated Data Driven Materials Modelling of Interfaces for Sustainable Applications

Funded phd project (uk students only).

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

MRes - Minimising inter-observer variability in cover estimates of sessile organisms (SAS0204)

Investigating the epigenetic control of female-biased autoimmune disorders, machine learning for radiative transfer applications in multidimensional flare simulations, competition funded phd project (uk students only).

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. The funding is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

Computer Science: Fully Funded EPSRC DTP PhD Scholarship: Vertical Multi-Purpose Farming Robotic System

Quality of experience (qoe) in object-based media, phd on the quantification of the impact of natural variability and possible volcanic futures on climate projections across the irish and british isles, large language models for biologics manufacturing challenges, phd informatics scholarship: applied machine learning for chronic pain physical rehabilitation, a machine learning enhanced digital twin toward sustainable pharmaceutical tablet manufacturing, competition funded phd project (students worldwide).

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Computer Science: EPSRC and Swansea University Funded PhD Scholarship: Explainable AI for Mathematical Modelling

Ai for clinical-grade mobile mental health management, awaiting funding decision/possible external funding.

This supervisor does not yet know if funding is available for this project, or they intend to apply for external funding once a suitable candidate is selected. Applications are welcome - please see project details for further information.

The Architecture of Future Healthcare Environments

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Unraveling the Mysteries of PhD Project Topics Selection

Blog Summary

A PhD requires distinct skill sets from a master’s and a bachelor’s. The biggest obstacle for PhD candidates is choosing a project subject or problem statement. This blog article aims to inform readers about how to select and complete their PhD projects. Your inner motivation and areas of interest should be the top considerations while selecting your specialization. Never start a PhD program without getting clarification on the research labs you should choose. For application alerts, while enrolled in your master’s program, register with PhD Portals. Select an interest-provoking subject, then read everything there is to know about it. A successful thesis requires adhering to the “Write, Rewrite, and Write” cycle.

Tips to Write your PhD Thesis

Pursuing a PhD, unlike your master’s or bachelor’s program, demands altogether different skill sets. You have a fixed set of subjects with some open elective and core-elective to study in those programs. But in a PhD program , you are aware of your stream of study like computer science, management, finance, humanities, etc.

But the PhD project topics on which you carry out research are wide open. You are supposed to narrow down to a particular thesis topic idea or field of study. Selecting a PhD project topic or problem statement is the biggest challenge for PhD students. This blog post attempts to educate scholars on selecting a PhD project of their choice and completing it.

How Do I Choose a PhD Project?

Choosing a PhD project topic is the primary work in pursuing a PhD program. It is not like choosing an undergraduate or postgraduate program. It demands patience. So, take your time.

Next, you should be in a position to decide what type of PhD project you want to pursue. Broadly there are three types of PhD projects:

• Advertised PhD projects
• Self-proposed PhD projects
• Professional Doctorates

The Advertised Projects are common in Science, Technology, Engineering, and Medicine (STEM) . Research groups and Well-established laboratories offer these programs.

The Self-proposed projects are common in the Humanities and Arts arena. Here, you are free to choose a thesis topic as long as it falls in the purview of a research topic.

Professional Doctorates in vocational subjects like Business and Management awarded to practitioners are not academic qualifications.

What Makes a good PhD Project?

A PhD project should, first of all, have a clear goal. So, it starts with a proposal. A PhD proposal is a clear and concise document illustrating the problem statement and the goals of your work. It should also highlight why it is worth pursuing?

A typical PhD project involves Five steps:

• Identifying a problem statement
• Carrying out a comprehensive literature review
• Conducting Original Research and finding out results
• Producing a Thesis that documents your results
• Writing the thesis and taking up Viva-Voce 

Tips for choosing a PhD project and topics

Here you have two sets of Tips:

• Tips to Apply for a PhD project and choosing a PhD project topic

Tips to Apply for a PhD project

1. Be Aware of Your Niche

Just because you are a computer science postgraduate and AI or Data Science is the trend; You needn’t select these areas. What matters is your interest and inner drive that should be the priority in choosing your niche.

2. Your Comfort Level to Relocate to Another City

Once you have identified your niche and the University/Research Labs, you may have to relocate to a new city. Make up your mind to relocate and also be decisive in making your choice.

3. Identify the Departments and Research Labs Succinctly

You are supposed to conduct a lot of research before boiling it down to a particular Department or University. This is a necessity as it is crucial to identify your core interests and ideas.

4. Obtain Clarity from Your Research Supervisor

Never dive into a PhD program without seeking clarity about the Research labs you are supposed to join. If it is a funded project, get clarification about all facts that are not obvious. Have one to one discussion with your Research Supervisor over Skype or any messenger to seek clarity regarding questions like,

• How many people work in the lab?
• What are their designations?
• Are you supposed to collaborate with any of them?

5. Register with PhD Portals to Get Application Alerts

During your Master’s Program, register with online portals that provide information on PhD programs offered by various Labs and Universities. This helps you to be informed about itineraries of multiple institutes.

6. Seek Seniors and Teachers Help

Ignorance is the biggest culprit that sinks your career ship. Regardless of how small your doubt is, get it clarified from your professors and seniors. Discuss issues like how to formulate an email, cover letter, resume, and other application procedures.

7. Understand the Team Well

It is not only the project that should create enthusiasm; it is also the team you will be working with. The team is vital to complete a project. Before diving into a project, try to understand whether you can get along with your teammates. 

8. Different Types of Funding Exists

When you apply for funded projects, you often come across various types. Some are not funded, while some are competition-funded also. Your enthusiasm for getting into the project plays a vital role in the supervisors picking you in competitive funding. So, Love your work to the core. 

9. Always Apply for More than One University/Institute

Prepare as many applications as possible and shoot them to different institutes. This process provides a wide array of experience in how to draft an application and approach the institutes. Such skills will help you in the long term.

10. Failure is the Stepping Stone to Success

You might fail once or twice in getting shortlisted or fail to perform in the interview. The number of interviews you have faced will help nurture your interpersonal skills.

Below are the general tips any PhD scholar should follow to be successful.

• While choosing a PhD project topic most crucial parameter is to rely on a topic that is interesting for you.
• Thoroughly read everything about the topic.
• Find a theoretical basis to support your idea.
• Be prepared to shift gears as the research progresses and your presumptions about the outcomes change.
• Be open to taking inputs from others to fine-tune your views.
• Formulate a committee of researchers,
• Be diligent in gathering data.
• The Panache for Effective Thesis Writing is Follow ing the “Write, Rewrite, and Write” Cycle. It doesn’t matter if your writing is good or bad; take tips from professional writers online. Most importantly, Good writing is all about Editing again and again. So, never feel daunted by Thesis writing; enjoy every bit of it.
• Sit with your Research Supervisor and prepare well-structured content with a Table of Content adequately defined. Regardless of being an expert writer or novice, your first draft always needs tweaking. Never be disheartened by re-editing work patience is key here.
• Thesis Writing needn’t be boring and monotony work. Bring in flair to your writing by inserting adjectives, says, expert writers.
• A chronologically written thesis is a misconception. As soon as you complete a piece of experiment or research, document it neatly when it is fresh in your mind. Later it can be integrated into the Final Thesis as per the Table of Contents.
• Once you research and write a chapter, take a break and come back with a critical perspective to discover possible mistakes. This always helps. Do not write in a marathon-style take breaks.
• Plagiarism is the biggest enemy of any research document. Whenever you quote an existing work, paraphrase properly and provide references and citations. 
• All universities have their Templates and Preferred Style of References . Religiously stick to the guidelines given by your university.
• Follow the same house style of spellings does not club “-ize” with “-ise” styles. If you prefer to use “improvize,” use it in all places, do not mix up with “improvise.”
• While quoting from other sources, ensure that you do not make spelling mistakes. Copy the quotes exactly.
• Your thesis is the window to showcase both your professionalism and research abilities to the outer world. Work with diligence and give it a professional appeal.

Why TSL-UCN?

Taksha Smartlabz in association with the University of Central Nicaragua (TSL-UCN) provides various PhD programs with an advanced blended learning system that is designed with working professionals in mind. It provides the opportunity to study from anywhere and at any time.

Taking up a PhD project involves various steps. Initially, you have to identify the domain of your interest and apply for a university or research lab. On getting selected, get involved in the meticulous work of carrying out research, documenting your findings, publishing papers, coming up with thesis work, and defending your work in research gathering.

The process of selecting your PhD project is the most crucial step in the entire process. Understanding whether you are looking out for Advertised/Self Proposed PhD projects or Professional Doctorates is vital in the initial stages.

Start your journey to obtaining a PhD

Enroll now, to reap the benefits of this program, and obtain a PhD in your niche.

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PhD in Computer Science Topics 2023: Top Research Ideas

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If you want to embark on a  PhD  in  computer science , selecting the right  research topics  is crucial for your success. Choosing the appropriate  thesis topics  and research fields will determine the direction of your research. When selecting thesis topics for your research project, it is crucial to consider the compelling and relevant issues. The topic selection can greatly impact the success of your project in this field.

We’ll delve into various areas and subfields within  computer science research , exploring different projects, technologies, and ideas to help you narrow your options and find the perfect thesis topic. Whether you’re interested in  computer science research topics  like  artificial intelligence ,  data mining ,  cybersecurity , or any other  cutting-edge field  in computer science engineering, we’ve covered you with various research fields and analytics.

Stay tuned as we discuss how a well-chosen topic can shape your research proposal, journal paper writing process, thesis writing journey, and even individual chapters. We will address the topic selection issues and analyze how it can impact your communication with scholars. We’ll provide tips and insights to help research scholars and experts select high-quality topics that align with their interests and contribute to the advancement of knowledge in technology. These tips will be useful when submitting articles to a journal in the field of computer science.

Top PhD research topics in computer science for 2024

Exploration of Cutting-Edge Research Areas

As a Ph.D. student in computer science, you can delve into cutting-edge research areas such as technology, cybersecurity, and applications. These fields are shaping the future of deep learning and the overall evolution of computer science. One such computer science research field is  quantum computing , which explores the principles of quantum mechanics to develop powerful computational systems. It is an area that offers various computer science research topics and has applications in cybersecurity. By studying topics like quantum  algorithms  and quantum information theory, you can contribute to advancements in this exciting field. These advancements can be applied in various applications, including deep learning techniques. Moreover, your research in this area can also contribute to your thesis.

Another burgeoning research area is  artificial intelligence (AI) . With the rise of deep learning and the increasing integration of AI into various applications, there is a growing need for researchers who can push the boundaries of AI technology in cybersecurity and big data. As a PhD student specializing in AI, you can explore deep learning, natural language processing, and computer vision and conduct research in the field. These techniques have various applications and require thorough analysis. Your research could lead to breakthroughs in autonomous vehicles, healthcare diagnostics, robotics, applications, deep learning, cybersecurity, and the internet.

Discussion on Emerging Fields

In addition to established research areas, it’s important to consider emerging fields, such as deep learning, that hold great potential for innovation in applications and techniques for cybersecurity. One such field is cybersecurity. With the increasing number of cyber threats and attacks, experts in the cybersecurity field are needed to develop robust security measures for the privacy and protection of internet users. As a PhD researcher in cybersecurity, you can investigate topics like network security, cryptography, secure software development, applications, internet privacy, and thesis. Your work in the computer science research field could contribute to safeguarding sensitive data and protecting critical infrastructure by enhancing security and privacy in various applications.

Data mining is an exciting domain that offers ample opportunities for research in deep learning techniques and their analysis applications. With the rise of cloud computing, extracting valuable insights from vast amounts of data has become crucial across industries. Applications, research topics, and techniques in cloud computing are now essential for uncovering valuable insights from the data generated daily. By focusing your PhD studies on data mining techniques and algorithms, you can help organizations make informed decisions based on patterns and trends hidden within large datasets. This can have significant applications in privacy management and learning.

Bioinformatics is an emerging field that combines computer science with biology and genetics, with applications in big data, cloud computing, and thesis research. As a Ph.D. student in bioinformatics, you can leverage computational techniques and applications to analyze biological data sets and gain insights into complex biological processes. The thesis could focus on the use of cloud computing for these analyses. Your research paper could contribute to advancements in personalized medicine or genetic engineering applications. Your thesis could focus on learning and the potential applications of your findings.

Highlighting Interdisciplinary Topics

Computer science intersects with cloud computing, fog computing, big data, and various other disciplines, opening up avenues for interdisciplinary research. One such area is healthcare informatics, where computer scientists work alongside medical professionals to develop innovative solutions for healthcare challenges using cloud computing and fog computing. The collaboration involves the management of these technologies to enhance healthcare outcomes. As a PhD researcher in healthcare informatics, you can explore electronic health records, medical imaging analysis, telemedicine, security, learning, management, and cloud computing. Your work in healthcare management could profoundly impact improving patient care and streamlining healthcare systems, especially with the growing importance of learning and implementing IoT technology while ensuring security.

Computational social sciences is an interdisciplinary field that combines computer science with social science methodologies, including cloud computing, fog computing, edge computing, and learning. Studying topics like social networks or sentiment analysis can give you insights into human behavior and societal dynamics. This learning can be applied to mobile ad hoc networks (MANETs) security management. Your research on learning, security, cloud computing, and IoT could contribute to understanding and addressing complex social issues such as online misinformation or spreading infectious diseases through social networks.

Guidance on selecting thesis topics for computer science PhD scholars

Importance of aligning personal interests with current trends and gaps in existing knowledge.

Choosing a thesis topic is an important decision for  computer science PhD scholars , especially in IoT. It is essential to consider topics related to learning, security, and management to ensure a well-rounded research project. It is essential to align personal interests with current trends in learning, management, security, and IoT and fill gaps in existing knowledge. By choosing a learning topic that sparks your passion for management, you are more likely to stay motivated throughout the research process on the cutting edge of IoT. Aligning your interests with the latest advancements in cloud computing and fog computing ensures that your work in computer science contributes to the field’s growth. Additionally, staying updated on the latest developments in learning and management is essential for your professional development.

Conducting thorough literature reviews is vital to identify potential research gaps in the field of learning management and security. Additionally, it is important to consider the edge cases and scenarios that may arise. Dive into relevant academic journals, conferences, and publications to understand current research in learning management, security, and mobile. Look for areas with limited studies or conflicting findings in security, fog, learning, and management, indicating potential gaps that need further exploration. By identifying these learning and management gaps, you can contribute new insights and expand the existing knowledge on security and fog.

Tips on Conducting Thorough Literature Reviews to Identify Potential Research Gaps

When conducting literature reviews on mobile learning management, it is important to be systematic and comprehensive while considering security. Here are some tips for effective mobile security management and learning. These tips will help you navigate this process effectively.

• Start by defining specific keywords related to your research area, such as security, learning, mobile, and edge, and use them when searching for relevant articles.
• Utilize academic databases like IEEE Xplore, ACM Digital Library, and Google Scholar for comprehensive cloud computing, edge computing, security, and machine learning coverage.
• Read abstracts and introductions of articles on learning, security, blockchain, and cloud computing to determine their relevance before diving deeper into full papers.
• Take notes while learning about security in cloud computing to keep track of key findings, methodologies used, and potential research gaps.
• Look for recurring themes or patterns in different studies related to learning, security, and cloud computing that could indicate areas needing further investigation.

By following these steps, you can clearly understand the existing literature landscape in the fields of learning, security, and cloud computing and identify potential research gaps.

Consideration of Practicality, Feasibility, and Available Resources When Choosing a Thesis Topic

While aligning personal interests with research trends in security, learning, and cloud computing is crucial, it is equally important to consider the practicality, feasibility, and available resources when choosing a thesis topic. Here are some factors to keep in mind:

• Practicality: Ensure that your research topic on learning cloud computing can be realistically pursued within your PhD program’s given timeframe and scope.
• Feasibility: Assess the availability of necessary data, equipment, software, or other resources required for learning and conducting research effectively on cloud computing.
• Consider whether there are learning opportunities for collaboration with industry partners or other researchers in cloud computing.
• Learning Cloud Computing Advisor Expertise: Seek guidance from your advisor who may have expertise in specific areas of learning cloud computing and can provide valuable insights on feasible research topics.

Considering these factors, you can select a thesis topic that aligns with your interests and allows for practical implementation and fruitful collaboration in learning and cloud computing.

Identifying good research topics for a Ph.D. in computer science

Strategies for brainstorming unique ideas

Thinking outside the box and developing unique ideas is crucial when learning about cloud computing. One effective strategy for learning cloud computing is to leverage your personal experiences and expertise. Consider the challenges you’ve faced or the gaps you’ve noticed in your field of interest, especially in learning and cloud computing. These innovative research topics can be a starting point for learning about cloud computing.

Another approach is to stay updated with current trends and advancements in computer science, specifically in cloud computing and learning. By focusing on  emerging technologies  like cloud computing, you can identify areas ripe for exploration and learning. For example, topics related to artificial intelligence, machine learning, cybersecurity, data science, and cloud computing are highly sought after in today’s digital landscape.

Importance of considering societal impact and relevance

While brainstorming research topics, it’s crucial to consider the societal impact and relevance of your work in learning and cloud computing. Think about how your research in cloud computing can contribute to learning and solving real-world problems or improving existing systems. This will enhance your learning in cloud computing and increase its potential for funding and collaboration opportunities.

For instance, if you’re interested in learning about cloud computing and developing algorithms for autonomous vehicles, consider how this technology can enhance road safety, reduce traffic congestion, and improve overall learning. By addressing pressing issues in the field of learning and cloud computing, you’ll be able to contribute significantly to society through your research.

Seeking guidance from mentors and experts

Choosing the right research topic in computer science can be overwhelming, especially with the countless possibilities within cloud computing. That’s why seeking guidance from mentors, professors, or industry experts in computing and cloud is invaluable.

Reach out to faculty members who specialize in your area of interest in computing and discuss potential research avenues in cloud computing with them. They can provide valuable insights into current computing and cloud trends and help you refine your ideas based on their expertise. Attending computing conferences or cloud networking events allows you to connect with professionals with firsthand knowledge of cutting-edge research areas in computing and cloud.

Remember that feedback from experienced individuals in the computing and cloud industry can help you identify your chosen research topic’s feasibility and potential impact.

Tools and simulation in computer science research

Overview of popular tools for simulations, modeling, and experimentation.

In computing and cloud, utilizing appropriate tools and simulations is crucial for conducting effective studies in computer science research. These computing tools enable researchers to model and experiment with complex systems in the cloud without the risks associated with real-world implementation. Valuable insights can be gained by simulating various scenarios in cloud computing and analyzing the outcomes.

MATLAB is a widely used tool in computer science research, which is particularly valuable for computing and working in the cloud. This software provides a range of functions and libraries that facilitate numerical computing, data visualization, and algorithm development in the cloud. Researchers often employ MATLAB for computing to simulate and analyze different aspects of computer systems, such as network performance or algorithm efficiency in the cloud. Its versatility makes computing a popular choice across various domains within computer science, including cloud computing.

Python libraries also play a significant role in simulation-based studies in computing. These libraries are widely used to leverage the power of cloud computing for conducting simulations. Python’s extensive collection of libraries offers researchers access to powerful tools for data analysis, machine learning, scientific computing, and cloud computing. With libraries like NumPy, Pandas, and TensorFlow, researchers can develop sophisticated models and algorithms for computing in the cloud to explore complex phenomena.

Network simulators are essential in computer science research, specifically in computing. These simulators help researchers study and analyze network behavior in a controlled environment, enabling them to make informed decisions and advancements in cloud computing. These computing simulators allow researchers to study communication networks in the cloud by creating virtual environments to evaluate network protocols, routing algorithms, or congestion control mechanisms. Examples of popular network simulators in computing include NS-3 (Network Simulator 3) and OMNeT++ (Objective Modular Network Testbed in C++). These simulators are widely used for testing and analyzing various network scenarios, making them essential tools for researchers and developers working in the cloud computing industry.

The Benefits of Simulation-Based Studies

Simulation-based studies in computing offer several advantages over real-world implementations when exploring complex systems in the cloud.

• Cost-Effectiveness: Conducting large-scale computing experiments in the cloud can be prohibitively expensive due to resource requirements or potential risks. Simulations in cloud computing provide a cost-effective alternative that allows researchers to explore various scenarios without significant financial burdens.
• Cloud computing provides a controlled environment where researchers can conduct simulations. These simulations enable them to manipulate variables precisely within the cloud. This level of control in computing enables them to isolate specific factors and study their impact on the cloud system under investigation.
• Rapid Iteration: Simulations in cloud computing enable researchers to iterate quickly, making adjustments and refinements to their models without the need for time-consuming physical modifications. This agility facilitates faster progress in  research projects .
• Scalability: Computing simulations can be easily scaled up or down in the cloud to accommodate different scenarios. Researchers can simulate large-scale computing systems in the cloud that may not be feasible or practical to implement in real-world settings.

Application of Simulation Tools in Different Domains

Simulation tools are widely used in various domains of computer science research, including computing and cloud.

• In robotics, simulation-based studies in computing allow researchers to test algorithms and control strategies before deploying them on physical robots. The cloud is also utilized for these simulations. This approach helps minimize risks and optimize performance.
• For studying complex systems like traffic flow or urban planning, simulations in computing provide insights into potential bottlenecks, congestion patterns, or the effects of policy changes without disrupting real-world traffic. These simulations can be run using cloud computing, which allows for efficient processing and analysis of large amounts of data.
• In computing, simulations are used in machine learning and artificial intelligence to train reinforcement learning agents in the cloud. These simulations create virtual environments where the agents can learn from interactions with simulated objects or environments.

By leveraging simulation tools like MATLAB and Python libraries, computer science researchers can gain valuable insights into complex computing systems while minimizing costs and risks associated with real-world implementations. Using network simulators further enhances their ability to explore and analyze cloud computing environments.

Notable algorithms in computer science for research projects

Choosing the right research topic is crucial. One area that offers a plethora of possibilities in computing is algorithms. Algorithms play a crucial role in cloud computing.

PageRank: Revolutionizing Web Search

One influential algorithm that has revolutionized web search in computing is PageRank, now widely used in the cloud. Developed by Larry Page and Sergey Brin at Google, PageRank assigns a numerical weight to each webpage based on the number and quality of other pages linking to it in the context of computing. This algorithm has revolutionized how search engines rank webpages, ensuring that the most relevant and authoritative content appears at the top of search results. With the advent of cloud computing, PageRank has become even more powerful, as it can now analyze vast amounts of data and provide accurate rankings in real time. This algorithm played a pivotal role in the success of Google’s computing and cloud-based search engine by providing more accurate and relevant search results.

Dijkstra’s Algorithm: Finding the Shortest Path

Another important algorithm in computer science is Dijkstra’s algorithm. Named after its creator, Edsger W. Dijkstra, this computing algorithm efficiently finds the shortest path between two nodes in a graph using cloud technology. It has applications in various fields, such as network routing protocols, transportation planning, cloud computing, and DNA sequencing.

RSA Encryption Scheme: Securing Data Transmission

In computing, the RSA encryption scheme is one of the most widely used algorithms in cloud data security. Developed by Ron Rivest, Adi Shamir, and Leonard Adleman, this asymmetric encryption algorithm ensures secure communication over an insecure network in computing and cloud. Its ability to encrypt data using one key and decrypt it using another key makes it ideal for the secure transmission of sensitive information in the cloud.

Recent Advancements and Variations

While these computing algorithms have already left an indelible mark on  computer science research projects , recent advancements and variations continue expanding their potential cloud applications.

• With the advent of  machine learning techniques  in computing, algorithms like support vector machines (SVM), random forests, and deep learning architectures have gained prominence in solving complex problems involving pattern recognition, classification, and regression in the cloud.
• Evolutionary Algorithms: Inspired by natural evolution, evolutionary algorithms such as genetic algorithms and particle swarm optimization have found applications in computing, optimization problems, artificial intelligence, data mining, and cloud computing.

Exploring emerging trends: Big data analytics, IoT, and machine learning

The computing and computer science field is constantly evolving, with new trends and technologies in cloud computing emerging regularly.

Importance of Big Data Analytics

Big data refers to vast amounts of structured and unstructured information that cannot be easily processed using traditional computing methods. With the rise of cloud computing, handling and analyzing big data has become more efficient and accessible. Big data analytics in computing involves extracting valuable insights from these massive datasets in the cloud to drive informed decision-making.

With the exponential growth in data generation across various industries, big data analytics in computing has become increasingly important in the cloud. Computing enables businesses to identify patterns, trends, and correlations in the cloud, leading to improved operational efficiency, enhanced customer experiences, and better strategic planning.

One significant application of big data analytics is in computing research in the cloud. By analyzing large datasets through advanced techniques such as data mining and predictive modeling in computing, researchers can uncover hidden patterns or relationships in the cloud that were previously unknown. This allows for more accurate predictions and a deeper understanding of complex phenomena in computing, particularly in cloud computing.

The Potential Impact of IoT

The Internet of Things (IoT) refers to a network of interconnected devices embedded with sensors and software that enable them to collect and exchange data in the computing and cloud fields. This computing technology has the potential to revolutionize various industries by enabling real-time monitoring, automation, and intelligent decision-making in the cloud.

Computer science research topics in computing, including IoT and cloud computing, open up exciting possibilities. For instance, sensor networks can be deployed for environmental monitoring or intrusion detection systems in computing. Businesses can leverage IoT technologies for optimizing supply chains or improving business processes through increased connectivity in computing.

Moreover, IoT plays a crucial role in industrial computing settings, facilitating efficient asset management through predictive maintenance based on real-time sensor readings. Biometrics applications in computing benefit from IoT-enabled devices that provide seamless integration between physical access control systems and user authentication mechanisms.

Enhancing Decision-Making with Machine Learning

Machine learning techniques are leading the way in technological advancements in computing. They involve computing algorithms that enable systems to learn and improve from experience without being explicitly programmed automatically. Machine learning is a branch of computing with numerous applications, including natural language processing, image recognition, and data analysis.

In research projects, machine learning methods in computing can enhance decision-making processes by analyzing large volumes of data quickly and accurately. For example, deep learning algorithms in computing can be used for sentiment analysis of social media data or for predicting disease outbreaks based on healthcare records.

Machine learning also plays a vital role in automation. Autonomous vehicles heavily depend on machine learning models for computing sensor data and executing real-time decisions. Similarly, industries can leverage machine learning techniques in computing to automate repetitive tasks or optimize complex business processes.

The future of computer science research

We discussed the top PhD research topics in computing for 2024, provided guidance on selecting computing thesis topics, and identified good computing research areas. Our research delved into the tools and simulations utilized in computing research. We specifically focused on notable algorithms for computing research projects. Lastly, we touched upon emerging trends in computing, such as big data analytics, the Internet of Things (IoT), and machine learning.

As you embark on your journey to pursue a PhD in computing, remember that the field of computer science is constantly evolving. Stay curious about computing, embrace new computing technologies and methodologies, and be open to interdisciplinary collaborations in computing. The future of computing holds immense potential for groundbreaking discoveries that can shape our world.

If you’re ready to dive deeper into the world of computing research or have any questions about specific computing topics, don’t hesitate to reach out to experts in the computing field or join relevant computing communities where computing ideas are shared freely. Remember, your contribution to computing has the power to revolutionize technology and make a lasting impact.

What are some popular career opportunities after completing a PhD in computer science?

After completing a PhD in computer science, you can explore various career paths in computing. Some popular options in the field of computing include becoming a university professor or researcher, working at renowned tech companies as a senior scientist or engineer, pursuing entrepreneurship by starting your own tech company or joining government agencies focusing on cutting-edge technology development.

How long does it typically take to complete a PhD in computer science?

The duration of a Ph.D. program in computing varies depending on factors such as individual progress and program requirements. On average, it takes around four to five years to complete a full-time computer science PhD specializing in computing. However, part-time options may extend the duration.

Can I specialize in multiple areas within computer science during my PhD?

Yes! Many computing programs allow students to specialize in multiple areas within computer science. This flexibility in computing enables you to explore diverse research interests and gain expertise in different subfields. Consult with your academic advisor to plan your computing specialization accordingly.

How can I stay updated with the latest advancements in computer science research?

To stay updated with the latest advancements in computing, consider subscribing to relevant computing journals, attending computing conferences and workshops, joining online computing communities and forums, following influential computing researchers on social media platforms, and participating in computing research collaborations. Engaging with the vibrant computer science community will inform you about cutting-edge computing developments.

Are there any scholarships or funding opportunities available for PhD students in computer science?

Yes, numerous scholarships and funding opportunities are available for  PhD students  in computing. These computing grants include government agency grants, university or research institution fellowships, industry-sponsored computing scholarships, and international computing scholarship programs. Research thoroughly to find suitable options that align with your research interests and financial needs.

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Early in Michaelmas Term you need to submit a project proposal that describes what you plan to do and how you plan to evaluate it. In order to help with this process, you are assigned two Project Checkers, who, together with your Supervisor and Director of Studies, will provide advice on your ideas. The deadline for project proposals is a little over one week into term, and is a hard deadline .

Choosing a project

You have a great deal of freedom in the selection of a project, and should start narrowing down the possibilities by identifying starting points or ideas that appeal to you. These initial ideas should be refined to a coherent project plan, which is then submitted as the project proposal. The proposal will be discussed informally with your Project Checkers, but is then submitted to the Head of the Department as a formal statement of intent.

The main sources of inspiration are commonly:

• Ideas proposed by candidates.
• Suggestions made by Supervisors or Directors of Studies.
• The project suggestions on the projects web page .
• Past years’ projects. Most recent dissertations are available to read online ,
• Proposals put forward by industry, especially companies who have provided vacation employment for students.

When ideas are first suggested or discussed it is good to keep an open mind about them—a topic that initially seems very interesting may prove unreasonable on further consideration, perhaps because it will be too difficult. Equally, many ideas on topics that are unfamiliar to you will need study before you can appreciate what would be involved in following them. Almost all project suggestions should also be seen as starting points rather than fully worked out proposals.

Notes on project choice

Some project ideas can be discarded very quickly as inappropriate. It is almost always best to abandon a doubtful idea early on rather than to struggle to find a slant that will allow the Project Checkers to accept it. Projects are expected to have a significant Computer Science content; for example, writing an application program or game-playing program, where the main intellectual effort relates to the area supported rather than to the computation, are not suitable. Projects must also be about the right size to fit into the time available. The implications of this will best be judged by looking at past years’ projects and by discussing plans with a Supervisor or Project Checker. They should not allow you to waste much time considering either ideas that would prove too slight or ones that are grossly overambitious.

It is important to pick a project that has an achievable core and room for extension. You should pick a suitably challenging project, where you will likely have to learn new things in order to successfully complete it. In addition, it is expected that you will make use of existing libraries and tools (i.e. don’t reinvent the wheel) unless there is a good reason for producing your own implementation.

Re-use of projects that have been attempted in the past

Projects are intended to give you a chance to display your abilities as a computer scientist. You are not required (or indeed expected) to conduct research or produce radically new results. It is thus perfectly proper to carry out a project that has been attempted before, and it is commonplace to have two students in the same year both basing their projects on the same original idea.

In such cases it is not acceptable to run a simple action replay of a previous piece of work. Fortunately all projects of the required scale provide considerable scope for different approaches; producing a new variation on an existing theme will not be hard. Furthermore the report produced at the end of a previous attempt at a project will often identify areas that led to unexpected difficulties, or opportunities for new developments—both these provide good scope for putting a fresh slant on the ideas involved.

Supervision

In some cases the most critical problem will be finding a suitable project Supervisor, somebody whom you will see regularly to report your progress and obtain guidance about project work throughout the year. This might be one of your main course Supervisors or a separate, specialist project Supervisor, but it should not be assumed that a person suggesting a project will be willing to supervise it. Supervisors have to be appointed by your Director of Studies, but in most cases it will be left up to you to identify somebody willing and able to take on the task. The Project Checkers will be interested only in seeing that someone competent has agreed to supervise the project, and that your Director of Studies is content with that arrangement.

Each project will have a number of critical resources associated with its completion. If even one of these fails to materialise then it will not be possible to proceed with a project based on the idea; your Director of Studies can help you judge what might be a limiting issue.

The project proposal must contain as its last section a Resources Declaration. This must explicitly list the resources needed and give contact details for any person (apart from yourself) responsible for ensuring their availability. In particular, you should name the person responsible for you if your work requires access to the Department research area. The signatures of these people should also be present on the project cover sheet before submission.

What qualifies as a critical resource?

In some cases a project may need to use data or build on algorithms described in a technical report or other document known to exist but not immediately available in Cambridge. In this case, this must be considered critical even if work could start without the report or data.

Using any hardware or software other than that available through a normal student account on UIS equipment (e.g. MCS) is considered non-standard. This includes personal machines, other workstations (e.g. research machines in the Department), FPGA boards, or even Raspberry Pis if they belong to someone else. Likewise, use of software written or owned by someone else that is not freely available as open-source will be considered as non-standard and should be declared.

Additional MCS Resources

It is reasonable to suppose that disk space and machine time will be made available in amounts adequate for all but extreme projects. Those who consider they may need more should provide a reasoned estimate of the resources required in the project proposal in consultation with the Supervisor. Additional file space should be requested through a web form , noting that:

• you should state in your application that you are Part II CST;
• requests for small increases of MCS space will need a very brief justification: please don't send your proposal;
• requests for substantial increases should also be accompanied by a brief supporting email to [email protected] from your Supervisor.

Note that some MATLAB toolkits are not available on the MCS but might be available on Department accounts.

Use of your own computer

If you are using your own computer, please state its specifications and also state your contingency plan in case it should fail (such as using MCS or another personal computer). Please also state your file backup plan and the revision control system you plan to use. If using your own computer please include the following text in your declaration:

I accept full responsibility for this machine and I have made contingency plans to protect myself against hardware and/or software failure.

Department Accounts

Access to Departmental computers can be granted if there is a good reason, e.g. 

• collaboration with a particular research group; 
• use of software not available on the MCS facility. 

If you plan to use a Department account then state this and explain why it is needed in your resources declaration. If relevant, the signature of a sponsoring member of the department (e.g. the owner of the specific resource) is required as an extra signature on the project cover sheet. In addition, your Supervisor should send an email to [email protected] requesting the account with a brief justification. 

Some Department resources and the people who can authorise their use: 

• Requests for resources involving a Department research machine should be authorised by a Lecturer, Reader or Professor who is in charge of managing the equipment. 

Access to the Department can be granted if there is a good reason. If you require access to the secure part of the William Gates Building, you should state who will be responsible for you whilst you are on the premises. They should sign your Project Proposal Coversheet as a Special Resource Sponsor. 

Third-Party Resources

Resources provided by your College, other University departments or industrial collaborators must be declared. The name and contact details (including email address) of the person in charge of the resource must be stated and their signature must be present on the project cover sheet. Resources from third parties can sometimes disappear unexpectedly, so please state why you believe this is not going to happen or else state your contingency plan in case it does.

In the case of projects that rely on support from outside the University it will be necessary to procure a letter from the sponsors that confirms both that their equipment will remain available right up to the end of the academic year and that they understand that the results of work done by students cannot be viewed as secret or proprietary.

You should bear in mind that the Examiners will require electronic submission of your dissertation and code. Therefore, you should not sign anything, such as a non-disclosure agreement, that would prevent you from submitting them.

Working with human participants

If your project involves collection of data via surveys, interviews or online, release of instrumented software, fieldwork, or experiments with human participants, such as usability trials or asking people to evaluate some aspect of your work, then you must seek approval by submitting a human participants request to the departmental Ethics Committee and record that you are going to do this, by ticking the appropriate box on your cover sheet.  This must occur before any of these activities start. Please read the Department's ethics policy .

Your project Supervisor will help you to fill in an online form ( read-only version ) containing two questions:

• A brief description of the study you plan to do;
• The precautions you will take to avoid any risk.

Simple guidance related to the most common types of study is available on the School of Technology Research Guidance site .  You may also find it useful to discuss your plans with the person supervising you for the Part II HCI course.

After submitting the ethics review form, you will receive feedback from the Ethics Committee within a few days. You must not start any study involving human participants without approval from the Ethics Committee.

Planning the project

As part of the project proposal, you should provide a detailed description of the work that needs to be performed, broken down into manageable chunks.  You will need to identify the key components that will go to make up your final product.  Credit is awarded specifically for showing a professional approach using any relevant management or software engineering methods at all stages of project design, development and testing. Plan an order in which you intend to implement the project components, arranging that both the list of tasks and the implementation order provide you with a sequence of points in the project where you can assess progress. Without a set of milestones it is difficult to pace your work so that the project as a whole gets completed on time.

When you have decomposed your entire project into sub-tasks you can try to identify which of these sub-tasks are going to be hard and which easy, and hence estimate the relative amounts of effort involved in each. These estimates, together with the known date when the dissertation must be submitted, should allow you to prepare a rough timetable for the work. The timetable should clearly make allowance for lecture loads, unit-of-assessment coursework, vacations, revision and writing your dissertation. Looking at the details of such a plan can give you insight into the feasibility of the project.  Ideally you should plan to start writing the dissertation at least six weeks before the submission date.

Languages and tools

It will also be necessary to make decisions about operating systems, programming languages, tools and libraries. In many cases there will be nothing to decide, in that the essence of the project forces issues. However, where you do have a choice, then take care to balance out the pros and cons of each option.  It is expected that students will be prepared to learn a new language or operating system if that is a natural consequence of the project they select.

Uncommon languages or ones where the implementation is of unknown reliability are not ruled out, but must be treated with care and (if at all possible) fall-back arrangements must be made in case insuperable problems are encountered.

Risk management

Projects are planned at the start of the year, and consequently it can be hard to predict the results of decisions that are made; thus any project proposal involves a degree of risk. Controlling and managing that risk is one of the skills involved in bringing a project to a successful conclusion. It is clear where to start: you should identify the main problem areas early and either allow extra margins of time for coping with them or plan the project so that there are alternative ways of solving key problems. A good example of this latter approach arises if a complete project requires a solution to a sub-problem X and a good solution to X would involve some complicated coding. Then a fall-back position where the project can be completed using a naive (possibly seriously inefficient, but nevertheless workable) solution to X can guard against the risk of you being unable to complete and debug the complicated code within the time limits.

Planning the write-up

As well as balancing your risks, you should also try to plan your work so that writing it up will be easy and will lead to a dissertation in which you can display breadth as well as depth in your understanding. This often goes hand-in-hand with a project structure which is clearly split into sub-tasks, which is, of course, also what you wanted in order that your management of your work on the project could be effective.

A good dissertation will be built around a varied portfolio of code samples, example output, tables of results and other evidence of the project’s successful completion. Planning this evidence right from the start and adjusting the project specification to make documenting it easier can save you a lot of agony later on.

Preparing the Project Proposal and consulting Project Checkers

You should keep in touch with both your Project Checkers from the briefing session until the final draft of your project proposal, making sure that they know what state your planning is in and that they have had a chance to read and comment on your ideas. Project Checkers will generally be reluctant to turn down a project outright, but if you feel that yours sound particularly luke-warm about some particular idea or aspect of what you propose you would do well to think hard (and discuss the issues with your Supervisor) before proceeding. If Project Checkers declare a project plan to be unacceptable, or suggest that they will only accept subject to certain conditions, rapid rearrangement of plans may be called for.

Dealings with your Project Checkers divide into three phases between the briefing session and submitting your proposal. Most of the communications will be best arranged by Moodle comments in the feedback box and all submissions of work are on Moodle.  Please be sure to take note of the various deadlines .

Phase 1 report: Selecting a topic

You start by preparing a Phase 1 report which, for 23/24 must be submitted on or before the first day of Michaelmas Full Term in October  Please pay careful attention to the points raised in the briefing lectures regarding selection of an appropriate topic. You must certainly choose something that has a defined and achievable success criterion. Note also that the marking scheme explicitly mentions preparation and evaluation, so please select something that will require a corresponding initial research/study phase and a corresponding (preferably systematic) evaluation phase.

You should complete a copy of the “Phase 1 Project Selection Status Report” and upload it to Moodle .

Phase 2: Full proposal draft: Filling out details

The details will include:

• Writing a description, running to a few hundred words.
• Devising a timetable, dividing the project into about 10 work packages each taking about a fortnight of your effort. The first couple of these might be preparatory work and the last three writing your dissertation, with the practical work in the middle. These should be identifiable deliverables and deadlines leading to submission of your dissertation at the beginning of the Easter Term. You will probably write your progress report as part of the fifth work package.
• Determining special resources and checking their availability.
• Securing the services of a suitable Supervisor.

Send all this to your Project Checkers and ask them to check the details. 

Phase 3: Final proposal

In the light of your Project Checkers’ comments, produce a final copy in PDF format. 

You do not secure signatures from your Project Checkers at this stage. Simply submit the proposal. 

Shortly after submission the Project Checkers will check your proposal again and, assuming that the foregoing steps have been followed carefully, all should be well and they will sign the proposal to signify formal acceptance. If the proposal is not acceptable you will be summoned for an interview.

Submission and Content of the Project Proposal

Completed project proposals must be submitted via Moodle by noon on the relevant day.

Format of the proposal

A project proposal is expected to up to 1000 words long. It consists of the following:

• A standard cover sheet
• The body of the proposal (see below).

When emailing drafts of your proposal to Project Checkers, please make sure they contain all of the information required on the final cover sheet.

The body of the proposal should incorporate:

• An introduction and description of the work to be undertaken.
• A statement of the starting point.
• Description of the substance and structure of the project: key concepts, major work items, their relations and relative importance, data structures and algorithms.
• A criterion that can later be used to determine whether the project has been a success.
• Plan of work, specifying a timetable and milestones.
• Resource declaration.

Introduction and description

This text will expand on the title quoted for your project by giving further explanation both of the background to the work you propose to do and of the objectives you expect to achieve. Quite often a project title will do little more than identify a broad area within which you will work: the accompanying description must elaborate on this, giving details of specific goals to be achieved and precise characterisations of the methods that will be used in the process. You should identify the main sub-tasks that make up your complete project and outline the algorithms or techniques to be adopted in completing them. A project description should give criteria that can be used at the end of the year to test whether you have achieved your goals, and should back this up by explaining what form of evidence to this effect you expect to be able to include in your dissertation.

Starting point

A statement of the starting point must be present to ensure that all candidates are judged on the same basis. It should record any significant bodies of code or other material that will form a basis for your project and which exist at project proposal time. Provided a proper declaration is made here, it is in order to build your final project on work you started perhaps even a year earlier, or to create parts of your programs by modifying existing ones written by somebody else. Clearly the larger the input to your project from such sources the more precise and detailed you will have to be in reporting just what baseline you will be starting from. The Examiners will want this section to be such that they can judge all candidates on the basis of that part of work done between project proposal time and the time when dissertations are submitted. The starting point should describe the state of existing software at the point you write your proposal (so work that you may have performed over the summer vacation is counted as preparatory work).

Success criterion

Similarly, a proposal must specify what it means for the project to be a success. It is unacceptable to say “I’ll just keep writing code in this general area and what I deliver is what you get”. It is advisable to choose a reasonably modest, but verifiable, success criterion which you are as certain as possible can be met; this means that your dissertation can claim your project not only satisfies the success criterion but potentially exceeds it. Projects that do not satisfy the success criterion are, as in real life, liable to be seen as failures to some extent.

You will need to describe how your project is split up into two- or three-week chunks of work and milestones, as explained in the planning section .

Resource declaration

You should list resources required, as described in the resources section .

Failure to submit a project proposal on time

Any student who fails to submit a project proposal on time is in breach of a Regulation and will no longer be regarded as a Candidate for Part II of the Computer Science Tripos. The Chairman of Examiners will write to the appropriate Senior Tutor as follows:

Dear Senior Tutor,

XXX has failed to submit a project proposal for Part II of the Computer Science Tripos.  The Head of Department was therefore unable to approve the title by the deadline specified in Regulation 17 for the Computer Science Tripos [Ordinances 2005, p268,amended by Notices (Reporter, 2010-11, pp.94 and 352, http://www.admin.cam.ac.uk/univ/so/2011/chapter04-section9.html#heading2-43 )].  XXX is therefore in breach of the regulation and is thus no longer eligible to be a Candidate for Part II of the Computer Science Tripos.  Please could you take appropriate action. I am copying this  letter to the Secretary of the Applications Committee of the Council.

Yours sincerely,

------------------------- Chair of the Examiners Department of Computer Science and Technology William Gates Building JJ Thomson Avenue Cambridge, CB3 0FD

Department of Computer Science and Technology University of Cambridge William Gates Building 15 JJ Thomson Avenue Cambridge CB3 0FD

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Top 35 Computer Science Project Topics of 2024 [Source Code]

Home Blog Web Development Top 35 Computer Science Project Topics of 2024 [Source Code]

Choosing the best computer science project topic is critical to the success of any computer science student or employee. After all, the more engaging and interesting topic, the more likely it is that students or employees will be able to stay motivated and focused throughout the duration of the project. However, with so many options out there, it can be tough to decide which one is right for you.

To help you get started, I have compiled a list of best computer science project topics for students and professionals like myself. These ideas cover everything from machine learning algorithms to data mining techniques, promising to be both challenging and engaging. If staying current with the latest trends is a bit tricky while brainstorming computer science project topics, I'd recommend opting for the online course in Web Development . The coursework gets updated regularly, ensuring there's always something new to learn.

Till then, pick a topic from this blog and get started on your next great computer science project. You will find  projects for professionals, interns, freelancers, as well as final year projects for computer science.

Computer Science Project Ideas with Key Information

Source: crio.do

Top Computer Science Project Topics with Source Code

1. hospital management system.

Type :  Application development, Database management, Programming

There is no shortage of computer science project topics out there. But if you are looking for something that's both technically challenging and socially relevant, consider a hospital management system. Such a system would include features like:

• Developing an application to manage patient records.
• Creating a database to store patient information.
• Programming a system to track medical appointments.
• designing an algorithm to improve the efficiency of hospital processes.
• Investigating the security risks associated with hospital data.
• Examining the impact of computerized systems on hospital staff morale.
• Evaluating the effectiveness of existing hospital management software.

Source Code: Hospital Management System

2. Weather Forecasting APP

Type: Application development, Web development, Programming

A weather forecasting app is a great idea for final year projects for CSE and can be used to provide users with real-time information about the weather, allowing them to make better decisions about their activities. To develop such an app, you will need to have a strong understanding of computer science concepts such as data structures and algorithms. In addition, you will also need to be familiar with the various APIs that are available for accessing weather data.

Source Code: Weather Forecast App

3. News Feed App

Type: Application designing, Application development, Programming

A news feed app is a great choice for a computer science project. Not only will you learn how to create a user interface, but you'll also gain experience with databases and newsfeed algorithms. To get started, you'll need to gather data from a variety of sources. You can use RSS feeds, APIs, or web scraping techniques to collect this data.

Once you have a dataset, you will need to process it and transform it into a format that can be displayed in your app. This will require some basic Natural Language Processing (NLP) techniques. Finally, you will need to design an algorithm that determines which stories are displayed in the news feed. This can be based on factors such as recency, popularity, or user interests. By working on a news feed app, you will gain valuable skills that are essential for any software developer.

Source Code: News Feed App

4. Optical Character Recognition System (OCR)

Type: Algorithm design, Optical recognition, System Development, Programming

An optical character recognition system, or OCR system, can be a great computer science project topic. OCR systems are used to convert scanned images of text into machine-readable text. This can be a difficult task, as there are often many different fonts and formatting styles that must be taken into account.

However, with the right approach, an OCR system can be an extremely useful tool. Not only can it help to reduce the amount of paper used in an office setting, but it can also help to increase efficiency by allowing users to search through large amounts of text quickly and easily. If you are interested in working on a project that will have a real-world impact, then an OCR system may be the right choice for you.

Source Code: OCR System

5. Library Management System

Libraries are increasingly using computers to manage their collections and circulation. As a result, Library Management Systems (LMS) have become an important tool for library staff. LMSs are designed to help libraries track and manage their books, e-books, journals, and other materials. They can also be used to manage patron information and circulation records.

Library Management Systems can be a great Computer Science project topic because they provide an opportunity to learn about databases and information management. In addition, developing an LMS can be a challenging programming project that requires the use of advanced data structures and algorithms. As a result, working on an LMS can be a great way to develop your skills as a computer programmer.

Source Code: Library Management System

6. Virtual Private Network

Type: Application development, Data security, Networking, Programming

A virtual private network (VPN) is a great project topic for computer science students. VPNs allow users to securely connect to a private network over the internet. By Encrypting data and routing traffic through a VPN server, VPNs can provide a high level of security and privacy. In addition, VPNs can be used to bypass internet censorship and access blocked websites. As a result, VPNs have become increasingly popular in recent years.

There are many different ways to set up a VPN, so computer science students can choose a method that best suits their skills and interests. With a little research, computer science students can create a functional and user-friendly VPN that will be sure to impress their instructors.

Source Code: VPN Project

7. e-Authentication System

Type: Authentication, Information security, System Development, Programming

There are many computer science project ideas   out there, but one that is particularly interesting is an e-authentication system. This system would be used to authenticate users and provide them with access to secure online services. The project would involve developing a database of user information, as well as a mechanism for authenticating users.

Depending on the scope of the project, it could also involve developing a user interface and testing the system. This would be a great computer science project for students who are interested in security and authentication. It would also be a good opportunity to learn about databases and web development.

Source Code: e-Authentication System

8. Real-time web search engine

Type: Machine learning, AI, Web annotation, Programming

Real-time web search engines would be a great project for computer science. The idea is to create a search engine that can index and search the web in real time. This would be a major undertaking and would require a team of computer science experts. However, the rewards would be great.

Such a search engine would be immensely useful to everyone who uses the internet. It would also be a major coup for the team that developed it. Therefore, if you are looking for a computer science project that is both challenging and impactful, a real-time web search engine is a great option.

Source Code: Real-time Search Engine

9. Task Management Application

Type: Application design, Application development, Authentication, Database management, Programming

While developing this application, students would learn about database design and development, user interface design, and data structures and algorithms. Ultimately, the goal would be to create an application that is both functional and easy to use.

Source Code: Task Management App

10. Chat App

Type: Application Development, Application designing, Networking, Socket programming, Multi-thread programming

A chat app is a great way to get started with coding and can be one of the ideal mini-project topics for CSE. Not only will you learn how to create a user interface, but you'll also learn how to work with databases and manage user input. Plus, a chat app is a useful tool that you can use in your everyday life. To get started, simply choose a coding language and framework. Then, create a new project in your chosen IDE and start coding! You can begin by designing the UI and then move on to adding features like messaging and file sharing.

Once you have completed the project, you will have a valuable skill that you can use to build other apps or start your own chat app business. And if creating apps intrigues you a lot, you can consider taking a Full Stack Engineer course to polish your skill and attract various hiring companies. With this course, you will gain a deep understanding of how to build, implement, secure and scale programs and access knowledge across the business logic, user interface, and database stacks. Moreover, the professionals may also assist you with your final year project topics for computer engineering.

Source Code: Chatapp

Top Computer Science Project Ideas for Students 2024

Here I’ve compiled a list of the best innovative project ideas for computer science students that you can explore.

1. Face Detection

One popular computer science project is building a face detection system. This involves training a machine learning algorithm to recognize faces in images. Once the algorithm is trained, it can then be used to detect faces in new images. This can be used for a variety of applications, such as security systems and social media apps.

Source Code: Face Detection

2. Online Auction System  

Another popular project idea is to build an online auction system. This can be used to sell products or services online. The system would need to include features such as bidding, payments, and shipping. It would also need to be secure so that only authorized users can access the auction site. 

Source Code: Online Auction System

3. Evaluation of Academic Performance  

This project focuses on developing a system that can evaluate the academic performance of students. The system would need to be able to input data such as grades and test scores. It would then use this data to generate a report card for each student. This project would require knowledge of statistical analysis and machine learning algorithms. 

Source Code: Student Performance Analysis

4. Crime Rate Prediction  

This project involves building a system that can predict crime rates in different areas. The system would need to input data such as population density, unemployment rate, and average income. It would then use this data to generate predictions for crime rates in different areas. This project would require knowledge of statistical modeling and machine learning algorithms. 

Source Code: Crime Prediction App

5. Android Battery Saver System  

This project focuses on developing an Android app that can save battery life. The app would need to be able to track the battery usage of other apps on the device. It would then use this information to provide recommendations on how to save battery life. This project would require knowledge of Android development and battery-saving techniques.

Source Code: Android Battery Saver

6. Online eBook Maker 

This project focuses on developing a web-based application that can be used to create eBooks. The application would need to allow users to input text, images, and videos into the eBook maker. It would then generate a PDF file that can be downloaded by the user. This project would require knowledge of web development and design principles.

These are just a few ideas for computer science projects that you can try out. If you're stuck for ideas, why not take inspiration from these?

Source Code: Online Ebook Maker

7. Mobile Wallet with Merchant Payment  

With a mobile wallet, users can make payments by simply waving their phones in front of a contactless payment terminal. This is not only convenient for consumers but also for merchants, as it reduces the time needed to process payments.

For your project, you could develop a mobile wallet app that includes a merchant payment feature. This would allow users to make payments directly from their mobile wallets to participating merchants. To make things more interesting, you could also add loyalty rewards or coupons that could be redeemed at participating merchants.

Source Code: Mobile wallet

8. Restaurant Booking Website  

Another great project idea is to develop a restaurant booking website. This type of website would allow users to search for restaurants by location, cuisine, price range, etc. Once they have found a restaurant they are interested in, they will be able to view available tables and book a reservation.

To make your project stand out, you could focus on making the booking process as smooth and seamless as possible. For example, you could allow users to book tables directly from the restaurant's website or through a third-party platform like OpenTable. You could also integrate with popular calendar apps so that users can easily add their reservations to their calendars.

Source Code: Restaurant Booking System

9. SMS Spam Filtering  

With the rise of smartphones, text messaging has become one of the most popular communication channels. However, this popularity has also made it a target for spam messages.

For your project, you could develop an SMS spam filter that uses artificial intelligence techniques to identify and block spam messages. To make things more challenging, you could also develop a system that automatically responds to spam messages with humorous or sarcastic responses.

Source Code: SMS Spam Filtering

10. Twitter Sentiment Analysis  

Source Code: Twitter Sentiment Analysis

Top Final-Year Project Ideas for Computer Science Students

As a computer science student, you have the unique opportunity to use your skills to create projects that can make a difference in the world. From developing new algorithms to creating apps that solve real-world problems, there are endless possibilities for what you can create. 

To get you started, here are the top innovative final-year project ideas for computer science students: 

1. Advanced Reliable Real Estate Portal

As the world becomes more digitized, the real estate industry is also starting to move online. However, there are still many challenges with buying and selling property online. For example, it can be difficult to verify the accuracy of listings, and there is often a lack of transparency around fees. 

As a computer science student, you could create a more reliable and transparent real estate portal that helps buyers and sellers connect with each other. This could potentially revolutionize the way people buy and sell property, making it simpler and more efficient. 

Source Code: Real Estate Portal

2. Image Processing by using Python  

Python is a versatile programming language that can be used for a wide range of applications. One area where Python is particularly useful in image processing. You could use Python to develop algorithms that improve the quality of images or that help identify objects in images. This could have applications in areas like security or medicine. 

Source Code: Image Processing Using Python

3. Admission Enquiry Chat Bot Project  

The process of applying to university can be very daunting, especially for international students. You could create a chatbot that helps prospective students with the admission process by answering their questions and providing information about specific programs. This would make it easier for students to navigate the university application process and increase transparency around admissions requirements. 

Source Code: Admission Enquiry Chatbot

4. Android Smart City Travelling Project  

With the rise of smart cities, there is an increasing demand for apps that make it easy to get around town. You could develop an Android app that helps users find the fastest route to their destination based on real-time traffic data. This could potentially help reduce traffic congestion in cities and make it easier for people to get where they need to go.

Source Code: Smart City Travelling App

5. Secure Online Auction Portal Project  

Auction websites are a popular way to buy and sell items online. However, there are often concerns about security when conducting transactions on these sites. As a computer science student, you could create a secure online auction portal that uses encryption to protect users' personal information. This would give users peace of mind when buying or selling items online and could help increase trust in auction websites. 

Source Code: Auction portal

6. Detection of Credit Card Fraud System  

With the increase in online shopping and transactions, credit card fraud has become a major problem. With your knowledge of computer science, you can help solve this problem by developing a system that can detect fraudulent activity. This project will require you to analyze data from credit card transactions and look for patterns that indicate fraud. Once you have developed your system, it can be used by businesses to prevent fraudulent transactions from taking place. 

Source Code: Credit Card Fraud detection

7. Real Estate Search Based on the Data Mining  

The process of buying or selling a home can be a long and complicated one. However, as a computer science student, you can make this process easier by developing a real estate search engine that uses data mining techniques. This project will require you to collect data from various sources (such as MLS listings) and then use analytical methods to identify trends and patterns. This information can then be used to help buyers and sellers find the perfect home. 

Source Code: Real Estate Search Based Data Mining

8. Robotic Vehicle Controlled by Using Voice  

With the increasing popularity of voice-controlled devices, it's no surprise that there is also interest in developing voice-controlled robotic vehicles. By taking such projects for computer science students, you can help create this technology by developing a system that allows a robotic vehicle to be controlled by voice commands. This project will require you to design and implement software that can interpret voice commands and then convert them into actions that the robotic vehicle can perform. 

Source Code: Voice Controlled robot

9. Heart Disease Prediction: Final Year Projects for CSE  

Heart disease is one of the leading causes of death worldwide. However, with early detection, many heart diseases can be effectively treated. As a computer science student, you can develop a system that predicts the likelihood of someone developing heart disease based on their medical history and other risk factors. This project will require you to collect data from medical records and then use machine learning algorithms to develop your prediction system.

Source Code: Heart Disease prediction

10. Student Attendance by using Fingerprint Reader  

Taking attendance in class is often a time-consuming process, especially in larger classes. As a computer science student, you can develop a fingerprint reader system that automates the attendance-taking process. This project will require you to design and implement software that can read fingerprints and then compare them against a database of students' fingerprints. Once the match is made, the student's name will be added to the attendance list automatically.

Source Code: Attendance with Fingerprint Management

11. Cloud Computing for Rural Banking Project  

This project aims to provide an efficient and secure banking system for rural areas using cloud computing technology. The project includes the development of a web-based application that will allow users to access their accounts and perform transactions online. The application will be hosted on a remote server and will be accessible from any location with an internet connection. The project will also include the development of a mobile app for users to access their accounts on their smartphones.

Source Code: Banking System

12. Opinion Mining for Comment Sentiment Analysis 

This project involves developing a system that can automatically analyze the sentiment of comments made on online platforms such as news articles, blog posts, and social media posts. The system will use natural language processing techniques to identify the sentiment of each comment and generate a report accordingly. This project can be used to monitor public opinion about various topics and issues.

Source Code: Opinion Mining Sentiment Analysis

13. Web Mining for Suspicious Keyword Prominence  

This project involves developing a system that can crawl through websites and identify keywords that are being used excessively or in a suspicious manner. The system will flag these keywords and notify the administrator so that they can further investigate the matter. This project can be used to detect spam websites or websites that are engaged in black hat SEO practices.

Source Code: Web Mining

14. Movies recommendations by using Machine Learning  

This project involves developing a system that can recommend movies to users based on their previous watching history. The system will use machine learning algorithms to learn the user's preferences and make recommendations accordingly. This project can be used to create a personalized movie recommendation system for each user.

Source Code: Movie Recommender System

15. Online Live Courier Tracking and Delivery System Project  

This project aims to develop a system that can track the live location of courier packages and provide real-time updates to the sender and receiver about the status of the delivery. The system will use GPS technology to track the location of courier packages and update the status in the database accordingly. This information will then be made available to users through a web-based or mobile application.

Source Code: Courier Tracking & Delivery System

How to Choose a Project Topic in Computer Science?

Picking a project topic in computer science can feel like a challenge. However, I've found a few steps that can make the process a bit easier.

1. Define your goals

The first step is to define your goals for the project. What do you hope to achieve by the end of it? Do you want to develop a new skill or build on existing ones? Do you want to create something that will be used by others? Once you have defined your goals, you can narrow down your focus and start thinking about potential topics. 

2. Do your research and Get inspired by real-world problems  

Once you have an idea of what you want to do, it's time to start researching potential topics. Talk to your supervisor, read through course materials, look at past projects, and search online for ideas. When doing your research, it is important to keep your goals in mind so that you can identify topics that will help you achieve them. 

3. Consider the feasibility  

Once you have shortlisted some potential topics, it's time to consider feasibility. Can the topic be completed within the timeframe and resources available? Is there enough information available on the topic? Are there any ethical considerations? These are all important factors to take into account when choosing a topic. 

4. Make a decision  

After considering all of the above factors, it's time to make a decision and choose a topic for your project. Don't worry if you don't know exactly what you want to do at this stage, as your supervisor will be able to help guide you in the right direction. The most important thing is that you choose a topic that interests you and that you feel confident about tackling it. 

Looking to master Python? Discover the online Python programming course that guarantees results. Unleash your coding potential and become a Python pro today!

Conclusion   

If you are a student looking for a computer science project topic or an employee searching for interesting ideas to improve your skills, I hope this article has given you some helpful direction. I have provided a variety of project topics in different areas of computer science so that you can find one that sparks your interest and challenges you to learn new things.  

I also want to encourage you to explore the resources available online and through your own community to continue expanding your knowledge in this rapidly changing field. On that note, KnowledgeHut’s online course for Web Development can help you with the different aspects of computer science. With experienced professionals as your instructors, you will be able to gain knowledge and expertise that will benefit you both professionally and academically. Why wait? Learn something new today!

Frequently Asked Questions (FAQs)

Final year projects for computer science are important because they allow students to apply the knowledge and skills that they have acquired over the course of their studies. By working on a real-world problem or challenge, students have the opportunity to develop practical expertise and learn how to work effectively as part of a team. 

Yes, final year projects can be very important for landing a job after graduation. Many employers use final-year projects as a way to assess a candidate's skills and abilities, and they may even use it as a tiebreaker when reviewing multiple candidates who are equally qualified. As such, students should take their final year projects seriously and put forth their best effort. 

Final-year projects also provide students with valuable experience that can help them in their future careers. If you select the best project topics for computer science students and work hard, you may be successful in your final year project.

Failing in a final-year project can be discouraging, but it is not the end of the world. One way to try and ensure passing is by taking mini-project topics for computer science. This will help show that you have the ability to complete projects and pass with flying colors. Additionally, try and get feedback from your professors on what areas you need to improve in.

Abhresh Sugandhi

Abhresh is specialized as a corporate trainer, He has a decade of experience in technical training blended with virtual webinars and instructor-led session created courses, tutorials, and articles for organizations. He is also the founder of Nikasio.com, which offers multiple services in technical training, project consulting, content development, etc.

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10 Best Computer Science Projects To Hone Your Skills

Computer science is that branch of science, which deals with the study, development, and maintenance of computers and computer systems. It is also a diverse field that is the superset of data science, information technology, networking, programming, web development, and a galore of other full-fledged research and interest areas.

The ongoing COVID-19 outbreak has disrupted the traditional way of pursuing education. As such, more and more people prefer to enroll online for distant and virtual modes of learning; if you’re also looking for a suitable computer science degree that you can complete without going out, check out these best online computer science degree programs .

Learning computer science demands developing and building a lot of skills. What could be better than a project to learn - and/or assess your ability that you’ve developed up until now in - computer science! Now, where to get the best computer science projects? Right here! But wait?

Still thinking, “why I need a computer science project to learn/assess my ability in the same?” Let’s answer that first:

• Computer Science Projects - Stepping Stones For A Better, Rewarding Future

Students often tumble over the question of what benefit they will get by working and developing projects in computer science, data science, or programming.

Also, if they are also, somehow, bothered thinking why they should give their best when working on computer science projects, then don’t worry because we are going to make things clear.

Most computer science courses' curriculum focuses on developing various skills, namely web development, programming, data analysis, content management, and more, but the implementation of these skills is something that students have to take care of themselves.

By working on a computer science project, candidates can also carve an opportunity for themselves to implement and test what they have learned. They can develop multiple computer science projects during the process and add them later to their portfolio, which will eventually help them land a good job, or, maybe, champion a college major or some specialization.

So, if anyone wants their future as an IT professional to be bright, they must work on one, or more, of the most popular computer science projects listed here. Much said already! Without wasting - any more - time, let’s get started with our pick of the top 10 computer science projects.

• 10 Best Computer Science Projects

1. Real-Time Weather Forecasting App

Type - Application Development, Programming, Web development Expected Time to Complete - 1 to 3 days Level - Beginner

Objective(s)

• To develop a web-based weather application that provides real-time weather information of a location, such as
• Current temperature, and
• Chances of precipitation.
• Also, it tells if it is going to be a sunny, cloudy, or rainy day ahead.

Project Overview

If you do not have any prior experience working on computer science projects, it’s better to get going with a project idea that is simple and effective.

The development of a weather application, which provides weather data for a particular location, would be a great way to test your coding skills.

To develop a weather application, all you need is the basic knowledge of the trifecta of web development, i.e., HTML, CSS, and Javascript. For creating a proper back-end of the app server in JavaScript, you will have to get familiar with Node.js and Express technologies.

It would be best to learn how to use API calls to get weather data from another website (like weatherstack.com) and display selective data right inside your webapp.

For the weather application’s UI, you need to conjure an input text box where users can enter the name of a location for which they wish to check the weather. As soon as the user hits the search button - most likely to be adjacent to the text box, but you are free to get creative as per your liking - the weather forecast for the entered location should be displayed.

Reference Free Projects @GitHub:

• Weather Forecast Android App
• Weather Forecast App

2. Basic Hospital Management System

Type - Application Development, Database Management, Programming Expected Time to Complete - 2 to 4 days Level - Beginner

• To develop a system that hospitals can use to digitize and manage their data, such as patient information, appointments, lab test results, patient diagnosis details, etc.

Developing a basic hospital management system is quite easy, even if you are a beginner. You can develop a functional hospital management system leveraging basic forms of HTML and CSS.

The developed system should get new data entries, store them, and let hospital officials and/or a system administrator(s) access and view data.

You need to design the hospital management system, so it automatically assigns a unique ID to each patient registered at the said hospital. Other than the patients, the system should also store information about the staff members, all in a local database.

When the database grows, it might become difficult for the hospital staff or the system administrator to find data related to a particular patient or staff member. So, it’ll be a good idea to add search functionality to make it easier to find specific details across hundreds - or even thousands - of stored records.

While you can use the local storage of the machine that runs the hospital management system, it is also possible to use a cloud database. Both have their distinct advantages over one another. You must figure them out on your own to make the project more challenging.

• Hospital Management
• Hospital Management System
• Sozer Hospital Management System

P.S. - Want more computer science projects focusing on HTML? Try these best HTML projects .

Related Course

Computer Science 101: Master the Theory Behind Programming

3. Optical Character Recognition (OCR) System

Type - Algorithm Design, Image Processing, Optical Recognition, Programming, System Development Expected Time to Complete - 4 to 6 days Level - Intermediate

• The optical character recognition (OCR) system should be able to process images and identify characters.
• Also, the system needs to give users the flexibility to search and manipulate the data.

To accomplish this project, you need to work with an algorithm that makes image recognition possible. This algorithm will enable the processing of images and search for characters in them.

Before working on the OCR system development, you must get a clear idea of how optical recognition technology works. Make sure that you build a good understanding of all the underlying concepts beforehand.

The two most popular technologies to develop a character recognition system are Python and MATLAB. It is advisable to select that particular technology which you want to use more frequently in the future.

While planning the project development work, you may need to set some accuracy level for your OCR system to achieve at the end of the project. Remember, the more accurate your OCR system in processing and identifying the characters in an image, the better.

• Tesseract OCR

4. News Feed Application

Type - Application Designing, Application Development, Programming Expected Time to Complete - 3 to 6 days Level - Intermediate

• Development of an online news feed application that gives users access to the latest news and events.
• The application should also be capable of fetching and displaying local as well as global news.

Building a news feed application is a great way to boost your app development skills as a computer science student. You can either create a web-based news feed application that runs inside browsers or a dedicated mobile app for smartphone users or both; the choice is completely yours.

The biggest challenge you need to tackle while developing the news app is ensuring that the app loads in the minimal time while delivering robust performance. The app should be capable of handling multiple requests from different users at the same time without crashing.

To get the latest and trending news, you can use free news APIs offered by various providers, like Bloomberg , Guardian, and Financial Times. Just keep in mind that the freely-available news APIs offer a limited number of API calls on a daily or monthly basis.

You need to create the front-end and the back-end of the app and thus require both front-end and back-end development technologies. The app can be easily created using any popular programming language, like JavaScript, Python, Java, etc.

• Making Headlines
• NewsFeed MVI Dagger

5. Library Management System

Type - Database Management, Database Manipulation, Programming, System Design, System Development Expected Time to Complete - 4 to 7 days Level - Intermediate

• The library management system should make it easier for library professionals to manage their day-to-day activities, such as
• Issuing books,
• Keeping a record of all the books issued, 
• The books that are available for borrowing et cetera.

Developing a library management system will help you become well-versed in database management and data manipulation. The library management system intends to bring automation and eliminate traditional paperwork.

To work on this project, you need to step-up your knowledge about database management (SQL and/or NoSQL database), UI design, and back-end logic development.

The library management system should allow students to create personal accounts that they can use to view the list of available books and initiate requests for issuing the same. Also, the system needs to have separate administrator access for library officials to update the availability of books, review book issue requests, and maintain a list of defaulters.

Additionally, it can also track the fine levied on unreturned or overdue books. It is also possible to add some more advanced features to the library management system, such as issuing ebooks and sending automated SMS notifications to students regarding the due dates for returning the books.

• A Library Management System with PHP and MySQL
• Library Management System
• Library Management System - Java
• LightLib Library Management System

6. Virtual Private Network

Type - Application Development, Data Security, Networking, Programming Expected Time to Complete - 5 to 8 days Level - Intermediate

• The project demands creating an application that allows users to convert their public network into a private network.
• The connection to the internet established using the VPN application will be encrypted, thus ensuring data exchange between the user and the server.

If you are interested in computer networks and the internet, creating a virtual private network (VPN) system would be something that is going to help you boost your knowledge and skills in this particular niche of computer science.

The VPN system proposed in this project will let users add a secure extension to their public networks. But first, you should know that there are two different approaches for creating a VPN, namely  IPSec (Internet Protocol Security) and SSL (Secure Socket Layer). Although both are good options, SSL is the better choice for developing a VPN.

The project will help you get familiar with various principles and technologies associated with computer networks such as authentication, public-key infrastructure (PKI), et cetera.

• Lethean VPN
• Neutron VPNaas
• Private Azure Kubernetes Service Cluster

7. e-Authentication System

Type - Authentication, Information Security, Programming, System Development Expected Time to Complete - 4 to 7 days Level - Intermediate

• In this project, the aim is to develop an e-Authentication system that uses QR code and One Time Password (OTP) to assess the user's authenticity.
• The e-Authentication system can be used to add an extra layer of security for users logging into their accounts on a website or application.

For any website or application where users can create and log in to their accounts, it is essential to rule out the possibility of unauthorized access. To accomplish the same, you can develop an e-Authentication system that uses QR code and OTP to ensure secure user login.

Once a user registers or creates an account on a website/app using a set of credentials, usually the email and password, the e-Authentication System will be put into work when the same user will log into their account.

After entering the email id and password for logging in, the user will then be asked to authenticate themselves using either a QR code or an OTP.

If the user selects and proceeds with the QR authentication method, a random QR code will be generated by the e-Authentication system and sent to the user’s registered email id. On the other hand, while opting for the OTP authentication method, the user will receive an OTP code on the registered email or phone number.

The user will only be logged into their account if they complete the authentication process initiated by the e-Authentication system.

• JWT (JSON Web Token Authentication for Laravel & Lumen)

8. Real-Time Web Search Engine

Type - AI, Machine Learning, Programming, Web Annotation Expected Time to Complete - 6 to 10 days Level - Master/Expert

• This project requires developing a web search engine that displays a list of web resources relevant to the user's search term.

If you have prior experience working on smaller or entry-level computer science projects and want to move a step further, then working on developing a web search engine is a good idea.

For crafting a search engine, you need to use web annotation to allow your search engine to access web pages and other online resources. Like a typical search engine, you need to provide a text box in which users can type their queries and hit the search button or hit enter to get relevant results.

The results displayed by the search engine needs to be arranged in the form of a list. Also, you can limit the number of search items displayed on a page to 10 or 15. This way, the search engine needs to have multiple search result pages.

For search suggestions and ensuring that the most relevant results are displayed, you can use AI and machine learning. However, incorporating such advanced technologies in your search engine will make the project more complex, more time-consuming, but yes, more fascinating too.

• RofiFtw (Rofi for the web)
• AskLawrence Search Engine & Screen
• Sociopedia Twitter Knowledge Engine
• Web Search Engine

9. Task Management Application

Type - Application Design, Application Development, Authentication, Database Management, Programming Expected Time to Complete - 5 to 9 days Level - Master/Expert

• To develop a dedicated task management app that allows users to
• Create personal profiles,
• Log in to their accounts securely with a proper authentication process,
• Add multiple tasks within the app,
• Manage multiple task lists, and
• Mark tasks as completed.

This is yet another project that will test your technical knowledge and coding skills to a greater extent. The task app needs to have an intuitive interface that will make it easier for users to interact with the app and manage their tasks.

The task app must allow users to create distinct accounts and start managing their everyday tasks effectively. A user's data should only be accessible to him/her, and an authentication system needs to be in place to safeguard the account from unauthorized access or accidental login.

As for the app, the user should add individual tasks or organize multiple tasks under a single task list. Also, the user should have the flexibility to create multiple task lists and manage several tasks altogether. Once completed, users can mark a task as completed.

For successfully developing the task, you need to have the knowledge and prior experience of working with full-stack development technologies such as MEAN stack (JavaScript) and LAMP stack.

• Pomo (Command-line application following the Pomodoro time management technique)
• Task Management Application using Vue.js

10. Chat App

Type - Application Development, Application Designing, Multi-thread Processing, Networking, Socket Programming Expected Time to Complete - 5 to 10 days Level - Master/Expert

• The project requires the development of a chat application that supports instant messaging.
• The chat app will allow users to create personal accounts from where they will send messages to other chat apps users.

The project is about developing a chat application using Python. Users can sign up to create their accounts and send instant text messages. The project largely focuses on utilizing concepts of socket programming and multi-thread processing.

The project is a little tricky to work with. You need to understand how sockets work and understand various principles related to computer networks.

You need to set up a server to handle user requests to connect and exchange messages in real-time. The chat app functionality can be extended by allowing users to exchange files along with normal text messages.

• Firebase Codelab: FriendlyChat
• WebSocket Chat
• Simple WebSockets Chat App

That wraps up our list of the best 10 computer science projects. Working on these projects will allow you to successfully prepare yourself for embarking on a professional journey in the lucrative field of computer science and IT or, at the very least, to assess your abilities in the same.

What’s important is that you gain something from these, which you will definitely, if you work on these computer science projects with pure dedication. If that’s done, then that fulfills the purpose of this write-up. Best of wishes! Stay safe, keep learning, and keep growing.

Computer science is a complex, interdisciplinary field of study. In addition to programming, web development, networking, et cetera, computer science succeeding also requires good mathematical abilities. Try these best computer science mathematics tutorials to enhance the same.

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A Computer Science graduate interested in mixing up imagination and knowledge into enticing words. Been in the big bad world of content writing since 2014. In his free time, Akhil likes to play cards, do guitar jam, and write weird fiction.

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HELP MAUI • JOB OPENINGS

Information and Computer Sciences

University of Hawai‘i at Mānoa

Current Ph.D. Students

Program requirements are specified here , and described in more detail on this page. Some degree requirements are imposed by the Graduate Division, which are explained in more detail on the Graduate Division Requirements page.

Program Objectives

The CS Ph.D. program is designed to help the student meet the following objectives: (1) Certify core competency in computer science and address any deficiencies in this competency as efficiently as possible, so that the bulk of the student’s Ph.D. program is focused on research. (2) Prepare to do research through an apprenticeship with a faculty member, demonstrating readiness to do research with a research portfolio that is analogous to a professional tenure and promotion portfolio. (3) Demonstrate contribution of new knowledge to one’s chosen field through a dissertation.

Getting Started with ICS 690

All new students (MS or PhD) must enroll in and pass ICS 690 in the first semester in which it is offered. Since it is offered in the fall, you should enroll in your first semester if you start in the fall, or in your second semester if you started in the spring. This course is supervised by the Graduate Chair and is CR/NC.

ICS 690 is designed to help orient new students to the program and to learn about faculty research areas and interests. It is also required to graduate. If you fail to take it in your first (or second) semester, you will be taking it later when it is no longer as helpful to you.

Completing a Masters’

Students shall complete a Masters’ degree in Computer Science or related field.

• What counts as “related” is at the discretion of the graduate program chair, assisted by the admissions committee.
• Those who enter without a MS shall go through the ICS MS program as part of their degree process.
• Students are considered to be in the “PhD portion” of their studies once they meet the requirement for the MS degree, even if it has not yet been awarded.

Portfolio: Research Readiness and Professional Capacity

By the end of the first year of the PhD portion of studies, the student will choose by mutual consent or be assigned a PhD program advisor. (This need not necessarily be the final PhD dissertation advisor.) The advisor will guide the student in preparing a portfolio that includes the following.

Contents of Portfolio

• Statement of purpose: A one to two page statement, written by the student, of the student’s professional interests in research, teaching, service, and/or product development.
• Evidence of MS degree
• Results of qualifying exam and evidence that any conditions have been met.
• (Optional:) Other evidence, such as professional employment in Computer Science.
• Thesis by the student from MS Plan A.
• Written Literature Review in the proposed area of study of 20-30 pages, following the graduate division dissertation format and reviewing at least 20 published works. (Ideally this review would be in the area identified in the Statement of Purpose and will become part of the dissertation proposal. However, if circumstances later require it, the student may elect to change the area of study at the proposal stage.)
• Publication(s) in reviewed journals or conferences that are relevant to the student’s professional interests. Evidence of quality such as acceptance rates or citation indexing should be provided. For multi-author publications, the student must provide a description of what his/her contribution was to the article.
• Technical report(s) on research project(s) relevant to the student’s professional interests that were supervised by a faculty member and read and approved by two other faculty members. ICS 699 projects may be included.
• Other Evidence of Professional Capacity (Optional): At the discretion of the student and the advisor, other material may be included in the portfolio. A professional vita of employment, professional presentations, reviewing of papers for conferences and journals, competitive fellowships or other external funding awards, patents, teaching, and service on committees or as graduate student representatives contribute to the candidacy decision. Letters of reference may also be included, but are not required. Students should report all forms of research, teaching, and service to the community and to the discipline when preparing their portfolios.

You may find Philip Johnson’s essay Why and how to create a high quality Ph.D. portfolio site useful.

Submission of Portfolio

It is strongly suggested that students submit their portfolio to the Graduate Chair as a URL that points to a Web page that contains all the required material, indexed in the four categories above, rather than providing hard copies.

Evaluation of Portfolio

Approval of the portfolio requires a two-thirds majority vote of a quorum of the ICS faculty (typically at a graduate committee meeting). The portfolio shall be distributed to the faculty at least one week in advance of the meeting at which it will be voted upon (but see Deadline below for student submission).

The graduate program chair shall designate two faculty members who shall review the portfolio and summarize arguments both pro and con, following criteria of academic review. Faculty that have a conflict of interest with the student (e.g., advisor or co-advisor, co-author on research articles, direct supervisor) cannot serve in this capacity. If the student feels there is a serious conflict with a faculty member that should preclude serving in this role the student should discuss it with the graduate chair or program chair more than a week in advance of the meeting.

The student’s advisor is strongly encouraged (but not required) to attend the portfolio review to provide relevant information, but may not vote or be selected as one of the two reviewers.

Deadlines for Portfolios

Portfolios should be submitted at least 10 days in advance of the graduate committee meeting in which they will be evaluated to allow for selection of faculty readers and distribution of materials. (Ask the graduate chair about scheduling.)

Students must submit their portfolio by the end of their second year in the Ph.D. portion of their studies, and must have their portfolio approved by the end of their third year of the Ph.D. portion of their studies.  Failing to meet either deadline will result in dismissal from the program.

The portfolio must be approved before undertaking the Proposal Defense.

Dissertation Committee

A Dissertation requires 5 committee members, including your advisor and a University Representative from outside your program. In addition to  committee requirements  of Graduate Division, an absolute majority of the dissertation committee must be Regular Graduate Faculty in ICS. A 6th member is permitted and can be a convenient way to include outside expertise after the internal requirements have been met. The student should choose committee members in consultation with his/her advisor.

Proposal Defense

Before commencing the final dissertation research, the student shall give a public defense of his or her PhD proposal. Students prepare a research proposal that includes a literature review in the chosen topic area (this usually is but is not required to be derived from the literature review from the portfolio) and a description of research topics to be investigated. This work should be done under the direction of an appropriate faculty advisor. After forming a committee, students take an oral examination covering their general preparation for the research involved, as specified in the General and Graduate Information Catalog. Once the student passes the proposal defense, Form II must be processed.

Scheduling the PhD Proposal Defense

• The student must confirm with the ICS Graduate Chair the eligibility of the proposed committee members before scheduling the defense. The student must submit the proposal title and abstract, a draft of the proposal including references, the proposed committee, and a brief justification of the appropriateness of committee members to the ICS Graduate Chair by 21 days before the proposal defense to allow time for this process.
• The student must schedule a proposal defense meeting at a time that the dissertation committee and the ICS Graduate Chair can attend, and arrange a room (physical or virtual). (If you want to use POST 302, contact the ICS office to reserve it.) The room should be scheduled for 3 hours, in case time is needed to discuss revisions to the work before it commences. (This may be the only time in your career that you receive the advice of 5 or more experts before starting your work, so don’t cut it short!)
• At least 14 days in advance of defending the proposal, the student must provide each member of the dissertation committee and the ICS Graduate Chair with a reading copy of the proposal. Students are encouraged to have received feedback from each committee member and revised the proposal accordingly, so that the proposal copy to be defended reflects at least one round of informed revision.
• At least 14 days in advance of defending the proposal, the student must distribute an announcement of the proposal defense that includes the title and abstract of the proposal by email to all ICS faculty members and graduate students. The announcement must specify the time and place of the defense and specify that the general public (including ICS faculty and students) are invited to attend. (Faculty may elect to do this on behalf of the student, but it is the student’s responsibility to ensure that the announcement is made.)

Grad chair as ex-officio member

Graduate program chairs have the privilege of being ex-officio (nonvoting) members of all committees in their program. Students should include the ICS graduate program chair when scheduling MS Plan A, Phd Proposal, or PhD Dissertation Defenses, and when distributing the associated document.

Final Defense

Students then conduct their research and write a dissertation under the direction of the advisor. The dissertation must be presented to and approved by a doctoral committee, as specified in the General and Graduate Information Catalog.

Scheduling the Ph.D. Final Defense

A . Scheduling of the final oral examination requires submission of the following information to the ICS Graduate Chair at least 21 days in advance of the intended examination date (to allow for resolving issues in time to meet the university requirement for a public announcement 14 days in advance):

• The intended date and time of the defense.
• The intended room, which has been reserved. The room should be reserved for at least 2.5 hours to allow sufficient time for follow-up discussion. (If you want to use POST 302, contact the ICS office to reserve it.)
• The title and abstract to be used for the announcement.
• (a) Written confirmation that the member can attend the specified date and time, except when remote participation or proxy has been approved, in which case the student shall attach appropriate approval forms (not needed for fully remote defenses during the pandemic);
• (b) A written indication of whether or not that member believes that there is reasonable evidence that the research will ready for defense by the specified date;
• (c) Optionally and independently of the judgment in (b), written comments concerning work that the committee member recommends be done before the defense for the research to be acceptable; and
• (d) Committee members may meet this requirement by sending (a-c) to the ICS Graduate Chair via email, with courtesy copy to the student and the dissertation chair.

B. Each committee member has the right to require a draft of the dissertation one week before approving scheduling of the formal defense. A committee member may opt to waive this right if that member already has sufficient evidence of defense readiness from prior communications with the student.

C. A majority of the committee must indicate that the research will be ready for the formal defense before the defense is scheduled. This majority must include the dissertation chair. Assent to schedule the defense does not constitute a promise that the student will pass.

D. At least 14 days in advance of the oral examination, the student shall complete all of the following:

• Meet all appropriate ICS and Graduate Division guidelines for the defense, including the official announcement in the University Calendar ( https://www.hawaii.edu/calendar/manoa/ )
• Distribute an announcement of the final defense that includes the title and abstract of the proposal by email to all ICS faculty members and graduate students. The announcement must specify the time and place of the defense and specify that the general public (including ICS faculty and students) are invited to attend. (Faculty may elect to do this on behalf of the student, but it is the student’s responsibility to ensure that the announcement is made.)
• Provide each member of the dissertation committee and the ICS Graduate Chair with a reading copy of the dissertation.

Dissertation Format. See the Graduate Division Style Policy for format requirements. The ICS department does not have further requirements: students and their advisors can make style and formatting decisions appropriate for the document as long as Graduate Division guidelines are followed. ICS graduate students have created a LaTeX template for the dissertation, which may be used by students writing their dissertation using LaTeX

Conducting the Final Ph.D. Defense

A. The student’s presentation shall not extend beyond one hour from the scheduled start time. Subsequently, all who attend shall be offered the opportunity to question the candidate during the public portion of the defense. However, only committee members participate in determining the outcome. The committee shall have the opportunity to discuss the defense in private (without the public or student present) immediately after the public event has ended and before signatures are requested. At this time, each committee member will assess the final dissertation document via departmental program assessment forms.

B. After the oral examination is complete, the dissertation committee members should sign Form III only when they are ready to indicate one of the following two outcomes:

• A “pass” if the dissertation research is adequate, and the student has successfully defended the dissertation research, and the dissertation document is accepted, possibly subject to specified modifications.
• A “fail” if any of the above conditions are not met.

C. Committee members should not sign “Doctorate – Dissertation Submission (Form IV”) until they believe that any necessary modifications are adequately completed. The student is responsible for providing each committee member with the evidence they require.

D. If the dissertation is accepted, the student shall provide the ICS program with a copy of the complete dissertation after all of the changes and corrections have been made. This copy shall become the property of the ICS program and will be made available to all interested students and members of the faculty.

E. If a dissertation is not accepted, the student may submit another dissertation, subject to Graduate Division and Program time limits.

If you have questions, contact the ICS Graduate Chair .

PHD RESEARCH TOPICS IN COMPUTER SCIENCE

PHD RESEARCH TOPICS IN COMPUTER SCIENCE is a vast domain due to its increasing need. Providing PHD is not our business, it is our passion. Being a computer science graduate, promoting RESEARCH TOPICS become our only aim and purpose of life. We dont believe in business in the field of education. Our great leader Mr.Kumarasami Kamaraj worked for the welfare of students and brought many schemes for promoting education. Following our great leaders, we too believe that education and research is not a business. It is a service; this thought made us to promote research in the field of computer science.

SCIENTIFIC DISCOVERY AND SCIENTIFIC RESEARCH HAVE BEEN ACHIEVED ONLY BY THOSE WHO WORK FOR IT…………..

We work very hard to achieve something, which has not achieved by anybody. For this reason, we focus on our scholars; we create best scholars who can be future scientist to serve our society. It will create a complete network of research which will develop our world to the next level.

Fundamental Research

Before taking up a research, we need to understand what major areas we need to concentrate are. First of all we need to focus on two things; first we need to understand the type of research we are going to choose. Another important aspect is data collection. Can we collect all the data needed for our research should be our concern. Research is basically classified as Fundamental research and Applied research. Fundamental research leads to the invention of something new; it can be a theory or new property of matter.

Applied Research

We can take example of the innovation of new planet. Applied research is used to support the basic research. Applied research has immediate implication due to its nature of application. We also have other types of research like revolutionary research, normal research, action research, explanatory research, exploratory research and comparative research.

So We support all kinds of research for our students. We give complete support for data collection. Also, We have separate team of experts and lab where we maintain both 2D and 3D data sets for our students. We feel that our students should not feel any difficulty in finding the data required for their research. Our experts strengthen in this aspect by

  Making random sampling procedure  Making organized selection to alternative rationale.  Preparing all kinds of dataset and softwares needed for it

                  We work for the satisfaction of students and feel that students should feel free after committing their research work to us.

PHD RESEARCH-TOPICS-IN-COMPUTER-SCIENCE:

There are numerous PHD RESEARCH TOPICS IN COMPUTER SCIENCE , which is difficult to enumerate here. We support all recent technologies and domains.

For the reference of scholars, we have enumerated few topics. Major few topics IN COMPUTER SCIENCE are:

Data Mining Image Processing Cloud computing Networking Vehicular Adhoc Networks Natural Language processing Pervasive computing

  We have provided only few major topics but we work on all major topics in computer science. Even we are ready to work on most recent technologies. If students bring any new tool, we take only few days to learn it and implement on it. Our knowledge makes us so powerful which makes us to shine like a Pole star always.

SKY IS OUR LIMIT………………

We dont follow even the above said quote. As we feel that Sky is not our limit. We have already touched the sky i.e. the highest peak in the field of research. For every domain mentioned above, we have different tools which we choose according to the project.

Lets discuss few domains with their respective tools.

Tools and Domains

For Data Mining, we use Weka, Wordnet, Matlab, Scilab and Java. Image processing can be implemented in Matlab, Scilab, OpenCV, Java, ImagJ, C++ and VC++. For cloud computing, we use java, cloudSim,CLoudanalyst,openstack etc. Regarding Networking , we can use NS2, NS3,Omnet++,Opnet,cloudsim,mininet etc.

Most recent technology like vehicular Adhoc network can be implemented in Omnet++, veins, Sumo. Domains like Natural languge processing can be implemented in Wordnet, sentiwordnet and java. Pervasive computing can be implemented in C++, java etc. It needs special sensors some time.

We provide that for our student at an optimum cost. So We are fully flexible to our students. We never say the word NO to our students. And also We believe that we can do everything and can support our students in any way.

FOR US..,,, THERE IS NO WORD LIKE IMPOSSIBLE………. AS THE WORD ITSELF SAY IMPOSSIBLE……………

Confidentiality.

We work for students satisfaction. We are even ready to give them real time projects, if they wish. Few scholars have doubt that whether we can work upon their idea and concept. We welcome scholars to bring their innovative idea and concept to us. So We are here only to help and guide them. We also maintain full data and concept confidentiality as we know the pain and risk scholars take to find something new. It is not one day work for anybody to seat and think something innovative. It takes scholar years to find something new, realizing their pain, we focus on CONFIDENTIALITY.

If scholars dont have any idea, contact us, we will guide you. We can take risk for our students to any level. We have mentioned few topics above, it is just an example we have provided for scholars to get an idea. Scholars can bring any domain and tool in the field of computer science, we can help them out. If scholars feel they dont have any idea about research itself, then they can surely contact us, we will be back to them. We will be happy to help them as service is our motto.

Related Search Terms

Machine Learning and AI projects aim to build systems that learn from data to make smart choices. These include tech for recognizing images and natural language processing, predicting trends, and running self-driving systems.

One of the best Project ideas for this category is a facial recognition attendance system.

A Facial Recognition Attendance System uses AI to spot and log people’s attendance by scanning their faces. It makes taking attendance automatic without anyone having to do it by hand.

Applications:

People can use this tech in schools, offices, events, or security checkpoints to keep track of who’s there, control who gets in, or monitor crowd demographics.

Click to get 100+ Machine Learning Projects with Source Code [2024]

Blockchain technology is primarily used in projects that require secure, transparent, and decentralized record-keeping. Common project ideas cover cryptocurrency systems , supply chain tracking , voting systems , and smart contracts .

Using the concept of supply chain we can create a secure delivery chain system for e-commerce websites using blockchain technology

Blockchain in delivery systems can enhance transparency, security, and traceability . It can create an immutable record of each step in the supply chain, from order placement to final delivery. This technology can help prevent fraud, ensure product authenticity, and provide real-time tracking information to all parties involved.

Verifying the origin of products, managing smart contracts for automated payments, and creating tamper-proof delivery records. It’s particularly useful for high-value or sensitive shipments where trust and verification are crucial.

Also Read: Top 7 Interesting Blockchain Project Ideas for Beginners 7 Project Ideas on Blockchain For Professionals

Cybersecurity projects aim to secure systems, networks, and data against cyber threats . They entail developing methods to protect information while ensuring privacy and integrity.

Using cybersecurity principles , you can create an image encryption system that encrypts digital photos.

The Image encryption system protects digital photos by transforming them into a coded format. This ensures that only authorized individuals can access or view the image content, limiting unauthorized access to sensitive or private photographs while also protecting data privacy and security.

• Encryption algorithm selection (for example, AES, RSA)
• Secure key management.
• Real-time image encryption and decryption.
• User authorization and access control
• Support for many image formats
• Integration of safe storage solutions

Check out: Top 6 Cybersecurity Projects Ideas for Beginners

Mobile Application Software refers to programs specifically designed to run on mobile devices such as smartphones and tablets. These applications are developed using various platforms and tools to provide functionality and enhance user experience on mobile devices.

You can develop a mobile app about topics such as a fitness app or a rescue guide app

You can create a mobile app that links users with their gym trainers helping them stay fit despite their busy lives.

• Personalized Diet Plans
• Exercise Programs
• Track Your Progress
• Goal Setting
• Educational Content
• Community Support

A mobile app for first aid treatments in emergencies can be beneficial. The Rescue Guide app provides emergency assistance, safety tips, and real-time alerts for various crises.

• Emergency contact list
• Pre-Diagnosis First Aid Guidelines
• Real-time location sharing
• Location-based emergency services

Also Check: Top 10 Android Project Ideas With Source Code

Data science helps us understand and use big data to make smarter choices and boost various services. It has an impact on areas like healthcare, finance, and marketin g to predict trends and achieve the best outcomes.

Social media, music, and streaming apps analyze your data to suggest new content based on what you’ve watched before. So the next project idea is a Movie recommendation system.

Check Out: Top Data Science Projects with Source Code [2024]

A Movie Recommendation System picks films based on what users like and have watched before. It uses an algotithms to make personal suggestions and make users happier.

• Personalized Recommendations
• Rating and Review System
• Genre Filtering includes action, comedy, drama, horror, and science fiction movies.
• Watch History Tracking

Cloud computing projects use remote servers to store, manage, and process data online, allowing users to access and use applications and services from anywhere.

Blood banking through cloud computing tech can be well-managed making sure donors and hospitals stay connected. Such ideas are highly appreciated for improving accessibility and saving lives.

The “ Blood Banking Via Cloud Computing” project can create an online platform to manage blood donations, storage, and distribution by connecting donors , hospitals , and recipients for efficient and real-time access.

• Track blood availability in real-time
• Match donors with recipients quickly
• Send alerts for low inventory
• Access data from anywhere
• Analyze donation trends
• Connect with a mobile app

Check Out: 10 Best Cloud Computing Project Ideas

Natural Language Processing (NLP) allows computers to interpret and process human language, enabling them to derive meaningful insights. This data is crucial for understanding human behavior and preferences.

Data analysts and machine learning experts leverage NLP to train machines to better understand and predict human behavior.

Social media platforms like Twitter contain a huge amount of data . Sentiment analysis helps to spot and deal with harmful tweets. The Twitter sentiment analysis project aims to figure out if a piece of writing is positive, negative, or neutral.

Learn more: Twitter Sentiment Analysis using Python

• Real-time sentiment tracking
• Sentiment classification (positive, negative, neutral)
• Sarcasm and slang detection
• Multi-language support
• Hate speech identification
• Blocking and reporting user IDs

Must Check: Top 12 AI Tools for Natural Language Processing (NLP): 2024

Web development projects involve creating and optimizing websites or web applications to meet specific needs or solve problems. These projects require skills in coding, design, and user experience to build functional, user-friendly online platforms.

Creating an e-commerce website is the best way to showcase your web development skills

An e-commerce website facilitates online shopping, allowing businesses to sell products and services directly to customers. It provides a platform for secure transactions, product browsing, and customer engagement.

Using this project, you can showcase front-end and back-end development skills , database management , payment integration, responsive design , and security.

• User account management
• Product Review
• User-friendly Interface
• Product Filtering by category, price, and rating
• Add to Wishlist or Shopping cart
• Payment options
• Order tracking
• Return and exchange option

3D graphics and modeling projects related to Computer Graphics or 3D Design. This field involves creating and manipulating visual content in three dimensions. It is often used in video games, simulations, animations, and virtual reality.

With this concept, you can create a custom 3D Model Generator as mentioned below.

A custom 3D model generator application creates personalized 3D models based on user input. Users can define parameters to design unique items such as phone cases, keychains, or jewelry.

• User-defined customization parameters
• Real-time 3D model preview
• Export options for 3D printing
• Pre-set templates and design tools
• File format compatibility

IoT initiatives aim to connect physical things to the internet, allowing them to gather, share, and act on data. These projects showcase expertise in hardware integration, real-time data processing, and automation, highlighting the promise of smart technology.

A weather monitoring system collects and transmits temperature, humidity, and other environmental data via IoT sensor s. This system gives real-time weather information, allowing for accurate forecasts and timely alarms.

• Real-time data collecting and transmission.
• Remote monitoring with mobile or online applications.
• Alerts regarding extreme weather conditions
• Data logging and historical analysis.
• Integration with weather APIs improves accuracy.
• Automated Street Lighting using IoT

An automated street lighting system uses IoT technology to regulate street lights depending on environmental factors like daylight or motion detection. This system increases energy efficiency and lowers operating costs.

• Motion and light sensors for automatic control.
• Remote monitoring and control with an IoT platform.
• Energy usage monitoring and optimization
• Lighting control based on schedules or conditions.
• Integration with Smart City Infrastructure

Must Read: Best Project Development Tips for Every Computer Science Student 10 Famous Bugs in The Computer Science World 7 Best Computer Science Courses To Take in 2024

In short, final year CSE projects are a student’s chance to shine, blending classroom theory with real-world innovation . By observing your surroundings, you can discover various ideas for your final year projects . Instead of selecting these projects as they are, you can think creatively and innovate to add uniqueness and make your projects stand out.

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August 21, 2024

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fact-checked

reputable news agency

Australia green lights world's 'largest' solar hub

by Laura CHUNG

Australia on Wednesday approved plans for a massive solar and battery farm that would export energy to Singapore, a project it calls the "largest solar precinct in the world".

Authorities announced environmental approvals for SunCable's US$24 billion project in Australia's remote north that is slated to power three million homes.

The project, which will include an array of panels, batteries and, eventually, a cable linking Australia with Singapore, is backed by tech billionaire and green activist Mike Cannon-Brookes.

"It will be the largest solar precinct in the world –- and heralds Australia as the world leader in green energy ," said Environment Minister Tanya Plibersek.

It is hoped that energy production will begin in 2030.

The 12,000-hectare (29,650-acre) project will provide four gigawatts of energy per hour for domestic use.

Two more gigawatts sent to Singapore via undersea cable will supply about 15 percent of the city-state's needs.

Batteries would be able to store about 40 gigawatts of power.

SunCable Australia's managing director Cameron Garnsworthy said the approval was "a landmark moment in the project's journey".

Despite Wednesday's green light, numerous approval processes remain—including working with Singapore's energy market authority, Indonesia's government and Australian Indigenous communities.

"SunCable will now focus its efforts on the next stage of planning to advance the project towards a final investment decision targeted by 2027," said Garnsworthy.

'Clean energy powerhouse'

Australia is currently one of the world's leading exporters of coal and gas, but has also been ravaged by the effects of climate change—from intense heat to floods and bushfires.

Although Australians are among the world's most enthusiastic adopters of household solar panels , a string of governments have been slow to fully embrace renewables.

In 2022, renewables made up 32 percent of Australia's total electricity generation—compared to coal, which contributed 47 percent, according to the latest government data.

Director of the Energy Change Institute at the Australian National University Ken Baldwin said the project was a "world first" for exporting renewable electricity from solar and wind on such a scale.

"Australia has some of the best solar and wind resources of any country, and as a result, is installing solar and wind at one of the fastest rates of any country in the world on a per capita basis," he told AFP.

But this momentum must continue, particularly if Australia is to meet its net zero targets by 2050, Baldwin said.

"Australia has, over the last five years, invested heavily in solar and wind, but it needs to double and triple that investment in order to reach its climate trajectory towards a net zero future by 2050."

He added that by the 2030s, Australia will need about 100 gigawatts of solar and wind capacity—the SunCable project will only provide four gigawatts of that need.

Climate Council chief executive Amanda McKenzie said the new solar hub was a bold step in making Australia a "clean energy powerhouse" and that such projects were essential in "delivering affordable energy and slashing climate pollution".

"With the closure of coal-fired power stations on the horizon, Australia needs to accelerate the roll-out of solar and storage at every level—rooftops, large-scale projects, and everything in between," she said.

The project would also be a significant step for Cannon-Brookes, who has expanded his portfolio from software company Atlassian—which he co-founded—to the renewable energy space, including being the latest shareholder in AGL Energy.

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Carnegie Mellon University Africa

• Graduate degree programs

Master of Science in Electrical and Computer Engineering (MS ECE)

Master of Science in Electrical and Computer Engineering, Advanced Studies program (MS-AD in ECE)

The 16-20 month MS ECE Advanced Studies program is for students admitted into the MS ECE standard program. The Advanced Studies program allows students to take additional courses and go into greater depth and specialization than the standard MS ECE program. All students are admitted into the standard MS ECE program and can choose to transfer at the end of their first year into the MS ECE Advanced Studies program. Students who choose the Advanced degree have two options:

• Course option: Students are required to complete 133 units through coursework.
• Project option: Students are required to complete 97 units through coursework and an additional 36 units of project work.

For more details, see the Electrical and Computer Engineering website .

What is electrical and computer engineering?

Electrical and computer engineering integrates many disciplines from electrical engineering and computer science under a common umbrella.

Wherever the electrons or computers are, that is where electrical and computer engineers are.

The field permeates all aspects of society and the work done by electrical and computer engineers has a deep and broad impact on our lives. In the video below, Timothy Brown  talks about the electrical and computer engineering master’s degree.

CMU uses units instead of credits to indicate the average number of hours required per week for a full-semester course. A nine unit class requires nine hours of work a week on the course.

Requirements for the MS ECE Advanced degree

To complete the MS-AD in ECE degree, students must complete at least 133 units with a cumulative quality point average of at least 3.0 (i.e., a B grade in each course). Below is the breakdown of the required completion units.

Course option requirements

• 60 units of ECE core courses
• 36 units of College of Engineering elective courses
• 36 units of general technical elective courses
• 1 unit of Introduction to Graduate Studies

Project option requirements

• 48 units of ECE core courses
• 24 units of College of Engineering elective courses
• 24 units of general technical elective courses
• 36 units of MS graduate project coursework

Other degree programs

Master of Science in Information Technology (MSIT)

Master of Science in Engineering Artificial Intelligence (MS EAI)