computer engineering phd research 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.

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

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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
• Biomedical Image Computing
• Data and knowledge research group

Embedded systems

• Networked systems
• Programming Languages & Compilers
• Service Orientated Computing
• Trustworthy Systems
• Theoretical Computer Science.

Artificial intelligence

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 : 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.

A scholarship/stipend may be available.

For more information, contact [email protected] .

Biomedical image computing

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]

Supervisor team:  Professor Erik Meijering and Professor Arcot Sowmya

Project summary:  Current commercial 3D ultrasound systems for medical imaging studies often do not provide the ability to record volumes large enough to visualise entire organs. The first goal of this PhD project is to develop novel computational methods for fast and accurate image registration to digitally reconstruct whole organs from multiple ultrasound volumes. The second goal is to develop computer vision and deep learning methods for automated volumetric image segmentation and downstream statistical analysis. This project will be in collaboration with researchers from the UNSW School of Women’s and Children’s Health to improve monitoring organ development during pregnancy to support clinical diagnostics.

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

Supervisor team:  Prof. Arcot Sowmya, A/Prof. Lois Holloway (Ingham Medical Research Institute, Liverpool Hospital)

Project summary:  Decisions on the most appropriate treatment for diseases such as colorectal cancer and diverticulitis can be complex. Advanced imaging such as MRI and CT can provide information on the location of the disease compared to other anatomy and also functional information on the disease and surrounding organs. There is also the potential to gain additional information from these images using techniques such as radiomics. At Liverpool hospital, there is a database of previous patient histories, including outcome as well as imaging information which we can use in collaboration with medical specialists. This project will use machine learning and deep learning approaches to determine anatomical and disease boundaries and combine them with clinical and response data to model treatment response and develop treatment decision support tools. The incoming PhD student should ideally have a computer science qualification with research skills and an interest to develop deep learning and decision support techniques in the medical imaging field. Research in this area is subject to ethics approvals and institutional agreements.

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

Supervisor team:  Prof. Arcot Sowmya and Dr Simone Reppermund

Depression and self-harm represent substantial public health burdens in the older population. Depression is ranked by the WHO as the single largest contributor to global disability and is a major contributor to suicide. This project will use large linked administrative health datasets to examine health profiles, service use patterns and risk factors for suicide in older people with depression. Given the vast amount of data included in linked datasets, new ways of analysing the data are necessary to capture all relevant data signals. This project will generate a sound epidemiological and service evidence base that informs our understanding of health profiles and service system pathways in older people with depression and risk factors for trajectories into suicide.

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

Data & knowledge research group

Supervisory team:  Xuemin Lin, Wenjie Zhang 

Project summary:  Efficient processing of large scale multi-dimensional graphs.

This project aims to develop novel approaches to process large scale graphs such as social networks, road networks, financial networks, protein interaction networks, etc. The project will focus on the three most representative types of problems against graphs, namely cohesive subgraph computation, frequent subgraph mining and subgraph matching. The applications include anomaly detection, community search, fraud and crime detection.  

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

Supervisory team:  Wei Wang, Xin Cao 

Project summary:  The immense popularity of online social networks has resulted in a rich source of data useful for a wide range of applications such as marketing, advertisement, law enforcement, health and national security, to name a few. Ability to effectively and efficiently search required information from huge amounts of social network data is crucial for such applications. However, current search technology suffers from several limitations such as inability to provide geographically relevant results, inadequately handling uncertainty in data and failing to understand the data and queries, resulting in inferior search experience. This project aims to develop a next-generation search system for social network data by addressing all these issues. 

For more information contact:  [email protected]  or  [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. Sowmya Arcot

Project summary:  We’ve seen stunning results from modern RL in arenas such as playing games, where the environment is constrained, predictable and it is possible to simulate a huge number of experiences. Major limiting factors are that current technology is not able to learn from a few experiences (few-shot learning), and to learn new tasks without forgetting old ones (continual learning). Neither has been well addressed and are usually investigated separately. In stark contrast, they are trivial for animals, and common in even simple real world scenarios. To advance the state-of-the-art continual and few-shot RL within a single architecture. The project is inspired by evidence that in our brains, the Hippocampus constitutes a short term memory and it replays to the frontal cortex directly. It is likely used for world-model building, as opposed to the mainstream view in cognitive science and ML - where 'experience replay' ultimately improves policy.

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]

Service orientated computing

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:  Lina Yao and Defence Science & Technology Group

Project summary:  This research is supported by the Defence Science and Technology Group. It aims to develop intelligent methodologies to capture the environment in sufficient fidelity to evaluate (model and predict) what application/system changes need to occur to fulfil the requirements (goals) of the mission.

For more information contact:  [email protected]

Supervisory team:  Lina Yao, Boualem Benatallah and Quan Z. Sheng

Project summary:  The overall goal of this project is to develop novel machine learning and deep learning techniques that can accurately monitor and analyse human activities. These techniques will monitor and analyse daily living on a real-time basis and provide users with relevant, personalised recommendations, improving their lifestyle through relevant recommendations.

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

Supervisory team:  Lina Yao

Project summary : This project is supported by Office of Naval Research Global (US Department of Navy). The aim of this project is to develop a software package for resilient context-aware human intent prediction for human-machine cooperation.

Supervisory team:  Lina Yao and Xiwei Xu

Project summary:  The research is supported by our collaborative research project with Data61. The aim is to develop an integrated end-to-end framework for fostering trust in Federated/Distributed AI systems.

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 and Boualem Benatallah

Project summary:  All modern organisations use some form of analytics tools. Configuring, using and maintaining these tools can be very costly for an organisation. Analytics tools require expertise from a range of specialties, including business insight, state-of-the-art modelling approaches and tools such as AI and machine learning as well as efficient data management practices. A knowledge engineering approach can deliver flexible and custom data analytics applications that align with organisational objectives and existing IT infrastructures. This model uses existing resources and knowledge within the organisation. The project uses semantic-web based knowledge modelling techniques to build a comprehensive view related to an organisation’s analytics objectives while leveraging open knowledge and open data to expand its scope and reduce costs.

We aim to help organisations utilise and reuse public and organisational knowledge efficiently when conducting data analytics. Our work also involves the rapid development and deployment of analytics applications that suit emerging analytics needs, plugging new data and software on-demand using new approaches such as APIs and cloud services. The proposed techniques have already been piloted in the areas of house price prediction in collaboration with the NSW Government and portfolio management in collaboration with Ignition Wealth.

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

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]   

Trustworthy systems

Supervisory team:  Gernot Heiser, June Andronick 

Project summary:  seL4, the secure embedded L4 microkernel, is a key element of our research program. We developed seL4 to provide a reliable, secure, fast and verified foundation for building trustworthy systems. seL4 enforces security within componentised system architectures by ensuring isolation between trusted and untrusted system components and by carefully controlling software access to hardware devices in the system. 

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

Supervisory team: Dr Arash Shaghaghi, Prof Sanjay Jha, Dr Raymond K. Zhao, Dr Nazatul Sultan

Project summary : A PhD scholarship is available for applicants with outstanding research potential and an interest in quantum-safe security measures for IoT deployments. The successful applicant will join a group of researchers from the School of Computer Science and Engineering (CSE) of UNSW Sydney, the UNSW Institute for Cyber Security (IFCYBER), and CSIRO. The research team brings a complementary track record and expertise aimed to tackle a project of significant potential for impact addressing an emerging topic of research.

We particularly encourage applications from those interested in practical system research (i.e., applied cryptography), noting that our goal is to enhance the resiliency of IoT deployments within intelligent transportation against quantum-based attacks. The project will develop a systematic approach and devise a testbed for evaluating quantum-based attacks against IoT deployments in critical infrastructure. The project’s findings will inform quantum-safe migrations in intelligent transport systems in Australia and internationally.

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

• Dr Dong Wen ( [email protected] ) - primary
• Prof. Wenjie Zhang ( [email protected] ) 

Due to the graph’s strong expressive power, a host of researchers are turning to graph modelling to support real-world data analysis. Given the prevalence of graph structures with temporal information in user activities, temporal and dynamic graph processing is an important and growing field of computer science. Driven by a wide spectrum of applications, such as recommendation and fraud detection in e-commerce, and malicious software detection in cybersecurity, this project aims to develop novel techniques for scalable and efficient temporal graph processing. The specific focus is to tame the challenges brought by the large volume, the high velocity, the complex structure of big temporal and dynamic graphs. The project will lay theoretical foundations and deliver substantial outcomes including computing frameworks, novel indexes and incremental and approximate algorithms to process large-scale graphs.

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

PhD in Computer Engineering

Program sites.

• Electrical & Computer Engineering

Degree Details

As a computer engineering PhD student, you will pursue theoretical and empirical studies alongside our world-renowned faculty. Students can enter the program directly after completing a bachelors degree, and earn a masters degree along the way. Students can also enter the program after completing a masters degree.

Degree Type

Tremendous research opportunities.

Electrical & Computer Engineering (ECE) faculty members boast international reputations and provide students with  opportunities for research , for example, in such areas as:  Computer Networking and Distributed Systems, Computer Engineering, Software Engineering and Applications, Communication, Sensor Networks, and Cybersecurity.  You can learn more about research in these areas by visiting  Mobile/Cloud Computing and Cybersecurity  and  BioECE and Digital Health .

The ECE PhD Student Experience

As a computer engineering PhD student, you will pursue theoretical and empirical studies in a topic area determined by your interests and those of your faculty research advisor. As a student in Boston, you will be in the midst of a vibrant high-tech research community where external collaborations with industry, government, and other universities are common. However, your experience will likely not be limited to Boston; PhD students are supported by the Department to present their work at many key conferences around the world.

Program Requirements

Post Bachelors:  A total of 64 graduate-level credits, 32 of which must meet the MS-EE requirements. A successfully defended PhD prospectus may be used to satisfy the MS thesis/project requirement.

Post Masters:  A total of 32 graduate-level credits.

For all PhD students: PhD Qualifying Process and Achieving Candidacy, Prospectus and Final Defenses & Teaching/Residency/Math Requirements.

Note : In structured courses, only grades of B- or higher are accepted for fulfilling PhD credit requirements. In non-structured (P/F) courses, the P grade is acceptable for fulfilling PhD requirements. PhD students who receive grades of C+ or lower in 3 courses will be withdrawn from the program.

• PhD Qualifying Process and Achieving Candidacy
• Prospectus and Final Defenses
• Teaching/Residency/Math Requirements

Contact & Application Support

Thesis Ideas for Computer Engineering

In the computer engineering domain, various concepts are emerging in recent times that assist in conducting research. We stay updated every day on changes in technologies sew identify the correct research gaps and share with scholar proper ideas and trending topics for all areas of computer engineering. The following are some thesis topics and plans relevant to computer engineering field that we consider:

• Machine Learning and Artificial Intelligence: In machine learning and AI, we explore novel techniques or approaches. This can be based on building fresh frameworks for particular works, enhancing previous methods, or implementing AI to new regions of interest.
• Internet of Things (IoT) and Smart Devices: Here, our work is to concentrate on the combination and interaction of IoT devices. Topics related to this domain can incorporate energy performance in smart devices, improving safety in IoT, or the building of novel kinds of IoT approaches.
• Computer Vision and Image Processing: Specifically for object identification, image recognition, or 3D modeling, we aim to enhance or construct novel techniques.
• Robotics and Automation: Our project concentrates on the pattern and application of robotic frameworks and that comprise various topics such as drone innovations, self-driving vehicles, or robotic process automation.
• Networks and Distributed Systems: In networking, we intend to investigate latest subjects, including the construction of more robust protocols, enhancing network safety, or advancements in distributed computing.
• Cybersecurity and Cryptography: , In the cybersecurity field, our project researches innovative approaches such as building more efficient encryption techniques or novel tactics for preventing cyber assaults.
• Quantum Computing: We involve more into the evolving domain of quantum computing. This consists of quantum computer framework, technique creation, or quantum computing applications.
• Human-Computer Interaction (HCI): To enhance the communication among computers and humans, we explore novel approaches like augmented reality, virtual reality, and user interface design.
• Bioinformatics and Computational Biology: Our work intends to implement computer engineering norms to the biology domain, including creation of techniques for examining biological data or framing complicated biological models.
• Big Data and Data Analytics: For managing, processing and examining extensive datasets, we investigate new techniques. This can incorporate data visualization, machine learning algorithms, or the creation of novel data analysis tools.

Keep in mind that a proper thesis topic must be practical within the objective and accessible materials in addition to be creative and intriguing. Also it is significant to examine the possibility and importance of the topic in the latest trending approaches.

What is a good research question for thesis?

For a computer engineering-based thesis, the creation of an effective research query incorporates finding a particular issue or passionate region within the domain, and designing a query that must be achievable as well as crucial. Appropriate research key word plays a major role it acts a good research question as it attracts the readers. Get our thesis writing services done from subject professionals, we handle it perfectly that abides by your academic standards. Below, we suggest some well-defined research queries related to computer engineering across different regions where we provide major assistance:

• Machine Learning and AI: How can machine learning algorithms be optimized for energy efficiency in mobile devices without compromising performance?
• Internet of Things (IoT): How can IoT devices be made more secure against distributed denial-of-service (DDoS) attacks without significantly increasing their cost or complexity?
• Computer Vision and Image Processing: Can deep learning techniques improve the accuracy of real-time object detection in varied lighting and weather conditions?
• Robotics and Automation: What are the challenges and solutions for implementing collaborative robots (cobots) in small and medium-sized manufacturing enterprises?
• Networks and Distributed Systems: What new models of distributed computing can effectively leverage edge computing for faster data processing in IoT networks?
• Cybersecurity and Cryptography: What are the effective strategies to detect and mitigate advanced persistent threats in enterprise networks?
• Quantum Computing: What are the practical limitations of implementing Shor’s algorithm in current quantum computing models, and how can these limitations be overcome?
• Human-Computer Interaction (HCI): How does the integration of augmented reality in educational software impact the learning outcomes in STEM subjects for high school students?
• Big Data and Data Analytics: How can big data analytics be used to predict and mitigate traffic congestion in urban areas effectively?
• Bioinformatics and Computational Biology: How can computational models be improved to more accurately simulate protein folding in complex organisms?

It is essential to make sure that your research query is explicit, concentrated, and realistic within the research goal while structuring it. The query must be more particular to be solvable within your research but also sufficiently wide to be aligned with realistic and educational passion. It will direct your study to dedicate novel perceptions and expertise to the computer engineering domain.

Computer Engineering Thesis Writing Services

phdprojects.org offers best Computer Engineering Thesis Writing Services, article writing, journal manuscript done by PhD professionals. We are a huge team of 100+ experts you can get your programming, code and  simulation results with best explanation .More than 16+ programming languages ae well handled by our developers.

• Massive Multiple Access Based on Superposition Raptor Codes for Cellular M2M Communications
• Q-value Learning Automata (QvLA)-RACH Access Scheme for Cellular M2M Communications
• Class based dynamic priority scheduling for uplink to support M2M communications in LTE
• Modeling and Forecasting of Timescale Network Traffic Dynamics in M2M Communications
• Signal-Centric Predictive Medium Access Control for M2M Communications
• M2M meets D2D: Harnessing D2D interfaces for the aggregation of M2M data
• A lightweight framework for efficient M2M device management in oneM2M architecture
• Deriving Machine to Machine (M2M) Traffic Model from Communication Model
• Learning Automata Based Q-Learning RACH Access Scheme for Cellular M2M Communication
• System Behavior and Improvements for M2M Devices Using an Experimental Satellite Network
• Ad Hoc M2M communications and security based on 4G cellular system
• A dynamic access class barring scheme to balance massive access requests among base stations over the cellular M2M networks
• A GCICA Grant-Free Random Access Scheme for M2M Communications in Crowded Massive MIMO Systems
• Dimensioning approach to provision M2M services on legacy GPRS and UMTS Cellular Networks
• M2M communications: Enablement in 4G LTE, deployment considerations and evolution path to 5G
• A D2D cooperative relay scheme for machine-to-machine communication in the LTE-A cellular network
• Random Access Channel Management for Handling Massive Numbers of Machine-to-Machine Communication Device
• Energy Efficient Self-Reconfiguration Scheme for Visual Information Based M2M Communicatio
• Remote subscription management of M2M terminals in 4G cellular wireless networks
• A novel WiMAX ranging scheme for periodic M2M applications in smart grid
• Energy Efficient Resource Allocation for UAV-Served Energy Harvesting-Supported Cognitive Industrial M2M Networks
• Radio resource allocation in LTE-advanced cellular networks with M2M communications
• Signal-centric predictive polling for medium access control in M2M communications networks
• Low-Complexity Heuristic Algorithm for Power Allocation and Access Mode Selection in M2MNetworks
• Bit-interleaved polar-coded OFDM for low-latency M2M wireless communications
• A Novel Machine-to-Machine Communication Strategy Using Rateless Coding for the Internet of Things
• Regularity of movement based approach for M2M services discovery
• To random access or schedule? Optimum 3GPP licensed-assisted access for machine-to-machine communications
• Building communications system: Takenaka’s M2M platform for building management & application interface
• A Multi-Gbps, Energy Efficient, Contactless Data-Communication Link for Machine-to-Machine (M2M) Interaction with Rotational Freedom.
• Grouping Based Uplink Resource Allocation for Massive M2M Communications over LTE
• Exploiting Spatial and Temporal Correlations for Signal-Centric MAC in M2M Communication
• Power Control for Cognitive M2M Communications Underlaying Cellular With Fairness Concerns
• Wireless M2M Communication Networks for Smart Grid Application
• An energy-efficient scheme for WiFi-capable M2M devices in hybrid LTE network
• Service Characteristics-Oriented Joint ACB, Cell Selection, and Resource Allocation Scheme for Heterogeneous M2M Communication Networks
• Optimizing Data Aggregation for Uplink Machine-to-Machine Communication Networks
• Pattern Division Random Access (PDRA) for M2M Communications With Massive MIMO System
• Modeling and analysis for admission control of M2M communications using network calculus
• An energy efficient, fault tolerant and secure clustering scheme for M2M communication networks
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Doctoral Programs in Computational Science and Engineering

Doctor of philosophy in computational science and engineering, program requirements, programs offered by ccse in conjunction with select departments in the schools of engineering and science.

The interdisciplinary doctoral program in Computational Science and Engineering ( PhD in CSE + Engineering or Science ) offers students the opportunity to specialize at the doctoral level in a computation-related field of their choice via computationally-oriented coursework and a doctoral thesis with a disciplinary focus related to one of eight participating host departments, namely, Aeronautics and Astronautics; Chemical Engineering; Civil and Environmental Engineering; Earth, Atmospheric and Planetary Sciences; Materials Science and Engineering; Mathematics; Mechanical Engineering; or Nuclear Science and Engineering.

Doctoral thesis fields associated with each department are as follows:

• Aerospace Engineering and Computational Science
• Computational Science and Engineering (available only to students who matriculate in 2023–2024 or earlier)
• Chemical Engineering and Computation
• Civil Engineering and Computation
• Environmental Engineering and Computation
• Computational Materials Science and Engineering
• Mechanical Engineering and Computation
• Computational Nuclear Science and Engineering
• Nuclear Engineering and Computation
• Computational Earth, Science and Planetary Sciences
• Mathematics and Computational Science

As with the standalone CSE PhD program, the emphasis of thesis research activities is the development of new computational methods and/or the innovative application of state-of-the-art computational techniques to important problems in engineering and science. In contrast to the standalone PhD program, however, this research is expected to have a strong disciplinary component of interest to the host department.

The interdisciplinary CSE PhD program is administered jointly by CCSE and the host departments. Students must submit an application to the CSE PhD program, indicating the department in which they wish to be hosted. To gain admission, CSE program applicants must receive approval from both the host department graduate admission committee and the CSE graduate admission committee. See the website for more information about the application process, requirements, and relevant deadlines .

Once admitted, doctoral degree candidates are expected to complete the host department's degree requirements (including qualifying exam) with some deviations relating to coursework, thesis committee composition, and thesis submission that are specific to the CSE program and are discussed in more detail on the CSE website . The most notable coursework requirement associated with this CSE degree is a course of study comprising five graduate subjects in CSE (below).

Computational Concentration Subjects

Note: Students may not use more than 12 units of credit from a "meets with undergraduate" subject to fulfill the CSE curriculum requirements

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Research in Electrical and Computer Engineering covers an extremely broad range of topics. Whether in computer architecture, energy and power systems or in nanotechnology devices, the research conducted in ECE is at the cutting edge of technological and scientific developments. 

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Computer engineering concerns itself with the understanding and design of hardware needed to carry out computation, as well as the hardware-software interface. It is sometimes said that computer engineering is the nexus that connects electrical engineering and computer science. Research and teaching areas with a significant computer engineering component include digital logic and VLSI design, computer architecture and organization, embedded systems and Internet of things, virtualization and operating systems, code generation and optimization, computer networks and data centers, electronic design automation, or robotics.

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Robotics at Cornell spans various subareas, including perception, control, learning, planning, and human-robot interaction. We work with a variety of robots such as aerial robots, home and office assistant robots, autonomous cars, humanoids, evolutionary robots, legged robots, snake robots and more. The Collective Embodied Intelligence Lab  works to design and coordination of large robot collectives able to achieve complex behaviors beyond the reach of single robot systems, and corresponding studies on how social insects do so in nature. Major research topics include swarm intelligence, embodied intelligence, autonomous construction, bio-cyber physical systems, human-swarm interaction, and soft robots.

Visit the the  Cornell Engineering Robotics Website  for more.

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• Systems and Networking

• Information, Networks, and Decision Systems

This research area focuses on the advancement of research and education in the information, learning, network, and decision sciences. Our research is at the frontier of a wide range of fields and applications, including machine learning and signal processing, optimization and control theory, information theory and coding, power systems and electricity markets, network science, and game theory. The work encompasses theory and practice, with the overarching objective of developing the mathematical underpinnings and tools needed to address some of the most pressing challenges facing society today in energy and climate change, transportation, social networks, and human health. In particular, the Foundations of Information, Networks, and Decision Systems (FIND) group comprises a vibrant community of faculty, postdocs, and students dedicated to developing the mathematical underpinnings and tools needed to address the aforementioned challenges in a principled and theory-guided manner.

• Biotechnology
• Computational Science and Engineering
• Energy Systems
• Image Analysis
• Information Theory and Communications
• Optimization
• Remote Sensing

• Physical Electronics, Devices, and Plasma Science

Work in this area applies the physics of electromagnetism, quantum mechanics, and the solid state to implement devices and systems for applications including energy, quantum technologies, sensing, communication, and computation. Our efforts span theory and development of new electronic and optical devices and materials, micro-electromechanical systems, acoustic and optical sensing and imaging, quantum control of individual atoms near absolute zero temperature, and experiments on high-energy plasmas at temperatures close to those at the center of the sun.    At Cornell ECE, we work on diverse topics aimed at transforming the way we view the world. Our interdisciplinary research reveals fundamental similarities across problems and prompts new research into some of the most exciting and cutting-edge developments in the field.

• Advanced Materials Processing
• Astrophysics, Fusion and Plasma Physics
• High Energy Density, Plasma Physics and Electromagnetics
• Materials Synthesis and Processing
• Microfluidics and Microsystems
• Nanotechnology
• Photonics and Optoelectronics
• Semiconductor Physics and Devices
• Solid State, Electronics, Optoelectronics and MEMs

• Circuits and Electronic Systems

Integrated circuits are ubiquitous and integral to everyday devices, from cellular phones and home appliances to automobiles and satellites. Healthcare, communications, consumer electronics, high-performance scientific computing, and many other fields are creating tremendous new opportunities for innovation in circuits and electronic systems at every level. Research in this area spans topics including analog and mixed signal circuits, RF transceivers, low power interfaces, power electronics and wireless power transfer, and many others. 

• Micro Nano Systems
• Optical Physics and Quantum Information Science

• Bio-Electrical Engineering

Biological and Biomedical Electrical Engineering (B2E2) consists of both applied and fundamental work to understand the complexity of biological systems at different scales, e.g., from a single neuronal or cancer cell, all the way to the brain or malignant tumor. B2E2 aims to develop new hardware and computational tools to identify, characterize, and treat diseases. In the physical domain, electrical engineering approaches to integrated microsystems lead to new biological and medical sensors. These sensors consist of state-of-the-art ultrasonic, RF, optical, MRI, CT, electrical impedance transducers. 

The integration of sensors, electronics are used to develop implantable and wearable devices, with decreasing size, weight, and power and increased functionality. B2E2 microsystems can help create interfaces for sensing and actuation to help understand the physiological and pathological mechanisms of diseases, and enable advanced robotic interfaces in medicine. Medical devices can generate vast amounts of data, which require both real-time and post-acquisition processing. B2E2 faculty, sometimes in collaboration with medical researchers, develop advanced computational tools to learn from and exploit data and apply artificial intelligence approaches to impact medical practice by improving: early disease detection, disease diagnosis, response to therapy assessment, and guided surgical procedures.

• Biomedical Imaging and Instrumentation
• Complex Systems, Network Science and Technology
• Computer-Aided Diagnosis
• Nanobio Applications
• Neuroscience

Hardware That Protects Against Software Attacks

ECE's Ed Suh and Zhiru Zhang and CS's Andrew C. Myers aim to develop both hardware architecture and design tools to provide comprehensive and provable security assurance for future computing systems against software-level attacks that exploit seven common vulnerability classes.

Image credit Beatrice Jin

Re-architecting Next-Gen Computing Systems

Disaggregated architectures have the potential to increase resource capacity by 10 to 100 times server-centric architectures.

Re-imagining Computer System Memories

Interdisciplinary team will provide new insights and an entirely new paradigm for the semiconductor industry in the emerging era of big data.

Engineers to hack 50-year-old computing problem with new center

Cornell engineers are part of a national effort to reinvent computing by developing new solutions to the “von Neumann bottleneck,” a feature-turned-problem that is almost as old as the modern computer itself.

The Laboratory of Plasma Studies: Uncovering mysteries of high energy density plasma physics

In the basement of Grumman Hall, an x-ray pulse produced by a hot, dense plasma – an ionized gas – lasting only fractions of a microsecond both begins and ends an experiment. Hidden within that fraction of time lies a piece of a puzzle—data that graduate students and staff scientists at the Laboratory of Plasma Studies (LPS) will use to better understand the mysterious physics behind inertial confinement fusion.

Sophia Rocco: Hoping to make the world a better place through a potential renewable energy source

When she was looking at graduate schools, physics major Sophia Rocco thought she would be in a materials science program bridging her interests in electricity and magnetism and novel materials for solar cells. Chancing upon the School of Electrical and Computer Engineering at Cornell, she discovered the Laboratory of Plasma Studies (LPS).

Finding the Ultimate Energy Source: Cornell’s Lab of Plasma Studies

Plasma is one of the four fundamental states of matter, but it does not exist freely on the Earth’s surface. It must be artificially generated by heating or subjecting a neutral gas to a strong electromagnetic field. Located in the basement of Grumman Hall are two large pulse-power generators that create plasma by delivering extremely high currents to ordinary matter for short periods. These generators are part of the  Lab of Plasma Studies  at Cornell University.

Photo credit: Dave Burbank

Vertical gallium oxide transistor high in power, efficiency

The research group led by Grace Xing and Debdeep Jena presented research on a new gallium oxide field-effect transistor at a conference at the Massachusetts Institute of Technology May 29-June 1.

Molnar, Jena and Xing join national consortium to develop future cellular infrastructure

Three Cornell faculty will be part of the newly established $27.5 million ComSenTer, a center for converged terahertz communications and sensing.

Data on the Brain

The NSF has found a willing partner at Cornell University in this quest to create technologies that will allow researchers to image the brain and the nervous system.

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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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• Electrical Engineering
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• Artificial Intelligence + Decision-making
• AI and Society

AI for Healthcare and Life Sciences

Artificial intelligence and machine learning.

• Biological and Medical Devices and Systems

Communications Systems

• Computational Biology

Computational Fabrication and Manufacturing

Computer architecture, educational technology.

• Electronic, Magnetic, Optical and Quantum Materials and Devices

Graphics and Vision

Human-computer interaction.

• Information Science and Systems
• Integrated Circuits and Systems
• Nanoscale Materials, Devices, and Systems
• Natural Language and Speech Processing
• Optics + Photonics
• Optimization and Game Theory

Programming Languages and Software Engineering

Quantum computing, communication, and sensing, security and cryptography.

• Signal Processing

Systems and Networking

• Systems Theory, Control, and Autonomy

Theory of Computation

• Departmental History
• Departmental Organization
• Visiting Committee
• Explore all research areas

Computer science deals with the theory and practice of algorithms, from idealized mathematical procedures to the computer systems deployed by major tech companies to answer billions of user requests per day.

Primary subareas of this field include: theory, which uses rigorous math to test algorithms’ applicability to certain problems; systems, which develops the underlying hardware and software upon which applications can be implemented; and human-computer interaction, which studies how to make computer systems more effectively meet the needs of real people. The products of all three subareas are applied across science, engineering, medicine, and the social sciences. Computer science drives interdisciplinary collaboration both across MIT and beyond, helping users address the critical societal problems of our era, including opportunity access, climate change, disease, inequality and polarization.

Research areas

Our goal is to develop AI technologies that will change the landscape of healthcare. This includes early diagnostics, drug discovery, care personalization and management. Building on MIT’s pioneering history in artificial intelligence and life sciences, we are working on algorithms suitable for modeling biological and clinical data across a range of modalities including imaging, text and genomics.

Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, …), statistical learning (inference, graphical models, causal analysis, …), deep learning, reinforcement learning, symbolic reasoning ML systems, as well as diverse hardware implementations of ML.

We develop the next generation of wired and wireless communications systems, from new physical principles (e.g., light, terahertz waves) to coding and information theory, and everything in between.

We bring some of the most powerful tools in computation to bear on design problems, including modeling, simulation, processing and fabrication.

We design the next generation of computer systems. Working at the intersection of hardware and software, our research studies how to best implement computation in the physical world. We design processors that are faster, more efficient, easier to program, and secure. Our research covers systems of all scales, from tiny Internet-of-Things devices with ultra-low-power consumption to high-performance servers and datacenters that power planet-scale online services. We design both general-purpose processors and accelerators that are specialized to particular application domains, like machine learning and storage. We also design Electronic Design Automation (EDA) tools to facilitate the development of such systems.

Educational technology combines both hardware and software to enact global change, making education accessible in unprecedented ways to new audiences. We develop the technology that makes better understanding possible.

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

The focus of our research in Human-Computer Interaction (HCI) is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

We develop new approaches to programming, whether that takes the form of programming languages, tools, or methodologies to improve many aspects of applications and systems infrastructure.

Our work focuses on developing the next substrate of computing, communication and sensing. We work all the way from new materials to superconducting devices to quantum computers to theory.

Our research focuses on robotic hardware and algorithms, from sensing to control to perception to manipulation.

Our research is focused on making future computer systems more secure. We bring together a broad spectrum of cross-cutting techniques for security, from theoretical cryptography and programming-language ideas, to low-level hardware and operating-systems security, to overall system designs and empirical bug-finding. We apply these techniques to a wide range of application domains, such as blockchains, cloud systems, Internet privacy, machine learning, and IoT devices, reflecting the growing importance of security in many contexts.

From distributed systems and databases to wireless, the research conducted by the systems and networking group aims to improve the performance, robustness, and ease of management of networks and computing systems.

Theory of Computation (TOC) studies the fundamental strengths and limits of computation, how these strengths and limits interact with computer science and mathematics, and how they manifest themselves in society, biology, and the physical world.

Latest news

Engineering household robots to have a little common sense.

With help from a large language model, MIT engineers enabled robots to self-correct after missteps and carry on with their chores.

New software enables blind and low-vision users to create interactive, accessible charts

Screen-reader users can upload a dataset and create customized data representations that combine visualization, textual description, and sonification.

AI generates high-quality images 30 times faster in a single step

Novel method makes tools like Stable Diffusion and DALL-E-3 faster by simplifying the image-generating process to a single step while maintaining or enhancing image quality.

Optimizing nuclear fuels for next-generation reactors

While working to nurture scientific talent in his native Nigeria, Assistant Professor Ericmoore Jossou is setting his sights on using materials science and computation to design robust nuclear components.

2024 MacVicar Faculty Fellows named

Professors Berggren, Campbell, Pollock, and Vaikuntanathan are honored for exceptional undergraduate teaching.

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PhD Topics in Computer Engineering

Computer engineering is defined as the study of the integration process of computer sciences and electronic engineering to create and enhance computer systems and several technological devices. We guide research scholars to design latest phd topics in computer engineering with end to end assistance.

Introduction to Computer Engineering

Research professionals in computer science have to be experts in various research areas including the integration of software and hardware, software engineering design, and electronic engineering. The research field of computer engineering includes several research professionals involved in a number of research areas such as

Execution Environment for Computer Engineering

• Verilog and embedded C++
• Electronic circuits and logic design laboratory
• Data structures with C/C++
• Object-oriented programming with C++

In addition, our research professionals are being familiar with the following components based on computer engineering.

• Circuit design and interfaces
• Computability and complexity of algorithm computation
• Software design
• Programming languages
• Integration of software and hardware

Are you guys looking for high-quality and cost-effective research guidancefor latest phd topics in computer engineering ? then you can unquestionably check out our services. We provide the most specialized and intimate research support for the PhD research scholars. Let us now look into some of the recent and general research areas based on computer engineering in the following.

Research Areas in Computer Engineering

• Standard signal processing techniques
• Two-dimensionalal signal
• Copyright marking
• Steganography
• Covert channels
• Optimization
• Linear algebra
• Differential equations
• Numerical methods and software
• Networking and security
• Operating systems and virtualization
• High-performance computing
• Parallel processing
• Visual Analytics
• Computer-supported cooperative work (CSCW)
• Haptic, visual and multimodal interfaces
• Human-computer interaction (HCI)
• Analog, digital and hybrid systems
• Web databases
• Text mining
• Genomic analysis
• Data integration
• Business intelligence
• Visualization
• Robotics and haptic
• Natural language understanding and generation
• Machine learning
• Intelligent user interfaces
• Knowledge representation and reasoning
• Decision theory and game theory
• Computer vision

For add-on information, our research professionals have enlisted the types that are available in the computer engineering research field in the following and it is beneficial for the research scholars to select their PhD topics in computer engineering.

What are the Types of Computer Engineering?

• Medical image computing
• Embedded systems
• Distributed computing
• Hardware systems
• Robotics and cybernetics

Consequently, let us take a glance at the following passage in which we have addressed the significant research algorithms which are used to develop research projects based on computer science engineering.

Algorithms in Computer Engineering

• Binary pattern complexity (BPC)
• Least significant bit (LSB)
• Pixel value differencing (PVD)
• The multi-parent hybrid algorithm based on optimal selection and gene insertion is abbreviated as OSGI-MPH. It is the integration process of the intelligent optimization algorithms and heuristic search algorithms
• Bacterial foraging optimization algorithm
• Cuckoo search algorithm

In addition, the research field is being developed through the forthcoming research ideas by scholars. While selecting the PhD topics in computer engineering , the research scholars have to take a glance at the recent research trends in the research field . Thus, we have heightened the topical research trends in computing engineering.

Current Trends in Computer Engineering

• It is denoted as the system of recording information and it’s a complex task to hack, alter and cheat the system. It is required for digital ledger transactions. It includes various transactions and a novel transaction occurs in the blockchain process
• Cloud computing is deployed in this process to deliver the application to mobiles and it is referred to as the services, applications, and data based on the cloud for mobile devices. Internet connection is essential for the cloud-based mobile applications
• GPU power development commoditizes custom voice creation and machine learning technology is used to create the computer-generated voice
• In recent days, voice in mobile apps is being in trend. The applications based on voice-powered are supportive, overlaid menus, form filling, navigation, etc.
• It is denoted as the type of graphical user interface with the representation of the interactive environment, three dimensional and computer generated immersively. In addition, virtual reality is enabling the user to interact with the computer-generated environment
• Utilization of asynchronous and synchronous learning
• Web and mobile applications
• Leader boards
• Chat forums
• Email applications
• Virtual classrooms

To select the research topic, the research scholars have to know more about the research areas that are functional in the selected research field and they have to gather a lot about the research background of the research area. To make that process easy, we have listed out the topical research areas in computer engineering.

Research PhD Topics in Computer Engineering

• Mobile sensing
• Visual motion
• Wireless and sensor systems
• Systems and networking
• Clustering based on new data
• Data analysis
• Computer graphics
• Understanding natural language
• Information visualization
• Human and computer interaction
• Developmental distributed systems
• Crowd algorithms
• Bio-inspired computing
• Mobile computing
• Wearable computing
• Computer architecture and hardware
• Electronic circuits and logic design
• Compilers design
• Computer security
• Field programmable gate arrays (FPGAs)

PhD scholars may face various issues in the process of topic selection, thus our research experts have highlighted the list of research topics in the computer engineering research field.

What are the Research Topics in Computer Engineering?

• Virtual reality and motion tracking
• High-quality and real-time volume visualization
• Character animation from 2-D pictures and 3-D motion data
• Image completion
• Texture synthesis
• Radio wave propagation
• Photo realistic image synthesis
• Dynamic tiling display
• Efficient spectral watermarking of 3-D meshes
• Image-based localization
• Image-based 3D reconstruction
• Efficient geodesic distances on complex structures
• Hexahedral meshing
• Dynamic simulation of physical behavior
• 3-D model repair
• Multi-resolution modeling
• High-quality progressive surface splatting

For your quick reference, our research professionals have enlisted the PhD topics in computer engineering . In addition, we have several innovative research topics in this field so contact us to know more innovative research topics in computer engineering.

What are the Project Topics in Computer Engineering?

• Distributed systems and security
• Immersive computing
• Data mining, databases, and geographical information systems
• Bioinformatics and computational biology
• Architectures, compiler optimization, and embedded systems

In general, various requirements are used to implement the research projects . In those requirements, simulation tools have a significant phase so he has enlisted the simulation tools that are used in the implementation of projects based on computer engineering.

Simulation Tools in Computing Engineering

• It is used for the modeling and simulation of resource management techniques in the environments such as IoT, fog, and edge computing systems along with features of migration management and mobility support. It is capable of model and simulating the scenarios along with the strategies for the data placement optimization
• Provision of policies and user-defined allocation
• Insertion of simulation to regulate the simulation entities
• Message passing applications and data center network topologies
• Federated clouds and application containers
• Energy-aware computational resources
• Providing a host for the virtualized data centers, host, and servers
• Software carpentry course provides the primary skills for running Bootcamp and scientific computing
• IPython is deployed for parallel computing and visualizations
• Pandas are denoted as the modeling and data analysis library
• SciPy is the set of engineering, science, and mathematics
• It is easy for the utilization of socket interface
• FTP and IMAP
• Email processing
• It includes the supporting networking operations
• The classes for applets creation
• Classes are utilized with the data implements including the dictionary and linked list
• The output operations and input supports are used
• Math operations
• Primitive data types

Last but not least we have enlisted the significance of our research guidance in the following for your reference.

Our Guidance

We used to publish the completed PhD papers in reputed journals such as SCI, IEEE, Scopus, Elsevier, etc. In addition, our technical experts provide research papers with high quality and novelty using topical research methodologies, simulation tools, significant protocols, etc.

We are here to help the research scholars in developing their research works along with the utilization of algorithms and implementing tools in all the above-mentioned significant processes. The research professionals in computer engineering are providing the finest research assistance for research scholars to implement their PhD topics in computer engineering.  With more than 10000+ happy customers, we are providing the topmost research service as per their requirements. So, get in touch with us with more confidence and trust. Our research experts will make you guys reach better heights in your research career.

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Computer Engineering (PhD)

Program at a glance.

• In State Tuition
• Out of State Tuition

Learn more about the cost to attend UCF.

The Computer Engineering PhD prepares students for careers in research or academia with a potential focus in computer systems and VLSI design, software engineering and algorithms, intelligent systems and Machine Learning, computer networks and computer security, as well as simulation systems.

The doctoral program in Computer Engineering is primarily intended for students with a master's degree in Computer Engineering or a closely related discipline wishing to pursue a career in research or academia. Specializations include computer systems and VLSI design, software engineering and algorithms, intelligent systems and Machine Learning, computer networks and computer security and simulation systems.

Research interests of the Computer Engineering faculty include computer architecture, software engineering, artificial intelligence, expert systems, modeling and simulation, computer networking and ubiquitous computing, computer security, and very large-scale integration (VLSI) systems.

The specific research that each one of the ECE faculty conduct can be found at the Department of ECE website .

The Computer Engineering PhD degree requires a minimum of 72 credit hours beyond the bachelor's degree. Of these 72 hours, a minimum of 36 credit hours must be formal coursework, exclusive of independent study coursework. A minimum of 15 credit hours with up to a maximum of 24 credit hours of dissertation hours can be credited toward the degree. No more than 12 credit hours of Independent Study are allowed. The remaining hours can be a combination of formal coursework and/or pre-candidacy doctoral research. Details about this program can be found in the Computer Engineering PhD Handbook, linked in the Program Handbook Section of this page.

Total Credit Hours Required: 72 Credit Hours Minimum beyond the Bachelor's Degree

This program has potential ties to professional licensure or certification in the field. For more information on how this program may prepare you in that regard, please view the licensure disclosure for the Computer Engineering PhD program.

Application Deadlines

• International

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Program Prerequisites

Master's or bachelor's degree in Computer Engineering or a closely related discipline.

Articulation Courses

Undergraduate articulation courses are required to be completed prior to admission for students who do not hold a Bachelor of Science degree in Computer Engineering. In particular, the articulation courses specified below, plus all of the prerequisite string which any of them require, must be completed prior to admission. Grades of "B" or higher must be obtained in each articulation course specified below. Articulation courses are not eligible for inclusion on a graduate Program of Study.

• EEE 3342C Digital Systems
• EEL 3801 Computer Organization
• COP 3502 Computer Science I
• COP 3503 Computer Science II

In addition, choose one of the following:

• COP 4331 Processes for Object-Oriented Development
• EEL 4768C Computer Architecture
• EEL 4781 Computer Communications Networks

Degree Requirements

Required courses.

• The Computer Engineering Program supports a number of specialization areas. These specialization areas are (in alphabetical order): Computer Networks and Computer Security (CNCS), Computer Systems and Reconfigurable Hardware (CS/RH), Intelligent Systems and Machine Learning (ISML), and Software Systems and Algorithms (SSA). Please visit the department’s website (https://www.ece.ucf.edu/people/faculty-by-research-area/) for a list of faculty within each specialization area. For each one of these areas there is a suggested list of courses stated below. Students are also allowed to take courses from other specialization areas, but the majority of their courses should be chosen from courses in their specialization area. A majority of specialization classes is at least 19 credit hours, typically 7 graduate courses.
• CDA5106 - Advanced Computer Architecture (3)
• CDA5110 - Parallel Architecture and Algorithms (3)
• CDA6530 - Performance Models of Computers and Networks (3)
• CDA6938 - Special Topics (3)
• CGS5131 - Computer Forensics I: Seizure and Examination of Computer Systems (3)
• CNT5008 - Computer Communication Networks Architecture (3)
• CNT6418 - Computer Forensics II (3)
• CNT6519 - Wireless Security and Forensics (3)
• CNT6707 - Advanced Computer Networks (3)
• COP5537 - Network Optimization (3)
• COP5611 - Operating Systems Design Principles (3)
• CAP6133 - Advanced Topics in Computer Security and Computer Forensics (3)
• CAP6135 - Malware and Software Vulnerability Analysis (3)
• COT5405 - Design and Analysis of Algorithms (3)
• EEE5542 - Random Processes I (3)
• EEL5780 - Wireless Networks (3)
• EEL6762 - Performance Analysis of Computer and Communication Systems (3)
• EEL6785 - Computer Network Design (3)
• EEL6788 - Advanced Topics in Computer Networks (3)
• EEL6883 - Software Engineering II (3)
• CDA6107 - Parallel Computer Architecture (3)
• EEE5390C - Full-Custom VLSI Design (3)
• EEL5722C - Field-Programmable Gate Array (FPGA) Design (3)
• ECM6308 - Current Topics in Parallel Processing (3)
• CAP5055 - AI for Game Programming (3)
• CAP5512 - Evolutionary Computation (3)
• CAP5610 - Machine Learning (3)
• CAP5636 - Advanced Artificial Intelligence (3)
• CAP6545 - Machine Learning Methods for Biomedical Data (3)
• CAP6616 - Neuroevolution and Generative and Developmental Systems (3)
• CAP6640 - Computer Understanding of Natural Language (3)
• CAP6671 - Intelligent Systems: Robots, Agents, and Humans (3)
• CAP6675 - Complex Adaptive Systems (3)
• CAP6676 - Knowledge Representation (3)
• EEL5825 - Machine Learning and Pattern Recognition (3)
• EEL5874 - Expert Systems and Knowledge Engineering (3)
• EEL6812 - Introduction to Neural Networks and Deep Learning (3)
• EEL6875 - Autonomous Agents (3)
• EEL6878 - Modeling and Artificial Intelligence (3)
• CAP6515 - Algorithms in Computational Biology (3)
• CAP5510 - Bioinformatics (3)
• CEN5016 - Software Engineering (3)
• CEN6075 - Formal Specification of Software Systems (3)
• COP5021 - Program Analysis (3)
• COP5711 - Parallel and Distributed Database Systems (3)
• COP6730 - Transaction Processing (3)
• COP6731 - Advanced Database Systems (3)
• COT6410 - Computational Complexity (3)
• COT6417 - Algorithms on Strings and Sequences (3)
• COT5600 - Quantum Computing (3)
• COT6602 - Introduction to Quantum Information Theory (3)

Elective Courses

• Earn at least 21 credits from the following types of courses: Additional elective courses listed above. - May include formal coursework, directed research hours, doctoral research hours, dissertation research, and no more than 12 credit hours of Independent Study.

Dissertation

• Earn at least 15 credits from the following types of courses: XXX 7980 Dissertation Research (15 credit hours minimum). The program will only allow students to complete up to 24 hours of dissertation coursework in XXX 7980. The College of Engineering and Computer Science requires that all dissertation defense announcements are approved by the student's adviser and posted on the college's website at least two weeks before the defense date.

Qualifying Review

• The Qualifying Review relies on annual appraisals of the student's progress conducted by the student's research/academic adviser and advisory committee, once formed. The student's appraisal template that the adviser completes will assess the student's academic performance (course performance) and research performance. On an annual basis, and based on the completed PhD Student Annual Review template, as well as additional student documentation attached with approval of the adviser, the ECE Graduate Committee will rate the student's performance as "Above Expectation," "At Expectation," or "Below Expectation" toward the completion of the PhD degree. Students must pass the Qualifying Review no later than the deadline, which is the semester in which they complete 24 credit hours after admission or within two calendar years after admission, whichever occurs later. If a student has passed the Qualifying Review, then the student is eligible to continue PhD studies. However, a student who does not pass the Qualifying Review by the deadline will be dismissed from the degree program and will be given the opportunity to complete a master's degree (if applicable).

Dissertation Committee

• PhD dissertation committees for this degree program must have all of the below characteristics: - consist of at least five committee members including the committee chair - the committee chair must be either a Regular Appointment faculty member in ECE or a Secondary-Joint Appointment faculty member in ECE - at least 50% of committee members (when tabulated including the chair) must be ECE regular faculty - the majority of committee members must vote in favor of passing for the student to Pass - in addition to the above, all college and university requirements (such as one member outside of ECE) must be met. Joint faculty members may serve as committee chairs, but graduate faculty scholars may not serve as committee chairs.

Candidacy Examination

• After passing the Qualifying Review, students are required to successfully complete the candidacy examination in order to demonstrate readiness for preliminary research in a chosen field of study. This exam is administered by the student's dissertation advisory committee. Preparedness for taking the candidacy examination requires that the student must demonstrate his/her readiness for the PhD program in Computer Engineering by authoring an accepted journal article or high-quality conference paper. The student must be the first author on this paper and the research advisor must also be an author on this paper to be used for Candidacy. The publication should reflect the work related to the student's PhD research. Candidacy is normally attempted at the completion of required coursework and must be passed before registering for doctoral dissertation hours (EEL 7980). Continuous enrollment in at least 3 hours of doctoral dissertation hours is required once a student starts taking dissertation credits.

Admission to Candidacy

• The following are required to be admitted to candidacy and enroll in dissertation hours. - Completion of all required formal coursework, except for dissertation hours. - Successful completion of the candidacy examination. - The dissertation advisory committee is formed, consisting of approved graduate faculty and graduate faculty scholars. - Submittal of an approved program of study. Signed and well-formed Doctoral Committee Candidacy Status form and associated paperwork must be submitted to the Electrical and Computer Engineering Graduate Office for processing on or before the last day to defend Dissertation during the semester prior to enrolling in dissertation credits.

Dissertation Proposal Exam

• After passing the candidacy examination, the student will write a dissertation proposal and present it to the dissertation advisory committee for approval. The proposal must include a description of the research performed to date and the research planned to be completed for the dissertation. The presentation of a written dissertation proposal must be deemed as passing requirements by the majority of the dissertation committee.

Grand Total Credits: 72

Application requirements, financial information.

Graduate students may receive financial assistance through fellowships, assistantships, tuition support, or loans. For more information, see the College of Graduate Studies Funding website, which describes the types of financial assistance available at UCF and provides general guidance in planning your graduate finances. The Financial Information section of the Graduate Catalog is another key resource.

Fellowship Information

Fellowships are awarded based on academic merit to highly qualified students. They are paid to students through the Office of Student Financial Assistance, based on instructions provided by the College of Graduate Studies. Fellowships are given to support a student's graduate study and do not have a work obligation. For more information, see UCF Graduate Fellowships, which includes descriptions of university fellowships and what you should do to be considered for a fellowship.

Formal coursework required is 36 credit hours, exclusive of independent study and research. A minimum of 15 credit hours of dissertation are required. All other credit hours will be determined with a faculty adviser. Students admitted with an earned master's degree may request to have up to 30 of those credit hours counted toward their doctoral program. The student's doctoral adviser in conjunction with the graduate office will determine the precise number of hours to be counted subject to Graduate Studies regulations.

The Program of Study (POS) form must be approved by an adviser in the selected specialization area no later than the end of the second semester after admission. The program of study must meet all the university requirements specified in the graduate catalog.

Students in the Computer Engineering PhD program pay a $28 equipment fee each semester that they are enrolled. Part-time students pay $14 per semester.

The Independent Learning requirement is met by successful completion of the student's candidacy and dissertation defense examinations.

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Latest Computer Science Research Topics for 2024

Home Blog Programming Latest Computer Science Research Topics for 2024

Everybody sees a dream—aspiring to become a doctor, astronaut, or anything that fits your imagination. If you were someone who had a keen interest in looking for answers and knowing the “why” behind things, you might be a good fit for research. Further, if this interest revolved around computers and tech, you would be an excellent computer researcher!

As a tech enthusiast, you must know how technology is making our life easy and comfortable. With a single click, Google can get you answers to your silliest query or let you know the best restaurants around you. Do you know what generates that answer? Want to learn about the science going on behind these gadgets and the internet?

For this, you will have to do a bit of research. Here we will learn about top computer science thesis topics and computer science thesis ideas.

Why is Research in Computer Science Important?

Computers and technology are becoming an integral part of our lives. We are dependent on them for most of our work. With the changing lifestyle and needs of the people, continuous research in this sector is required to ease human work. However, you need to be a certified researcher to contribute to the field of computers. You can check out Advance Computer Programming certification to learn and advance in the versatile language and get hands-on experience with all the topics of C# application development.

1. Innovation in Technology

Research in computer science contributes to technological advancement and innovations. We end up discovering new things and introducing them to the world. Through research, scientists and engineers can create new hardware, software, and algorithms that improve the functionality, performance, and usability of computers and other digital devices.

2. Problem-Solving Capabilities

From disease outbreaks to climate change, solving complex problems requires the use of advanced computer models and algorithms. Computer science research enables scholars to create methods and tools that can help in resolving these challenging issues in a blink of an eye.

3. Enhancing Human Life

Computer science research has the potential to significantly enhance human life in a variety of ways. For instance, researchers can produce educational software that enhances student learning or new healthcare technology that improves clinical results. If you wish to do Ph.D., these can become interesting computer science research topics for a PhD.

4. Security Assurance

As more sensitive data is being transmitted and kept online, security is our main concern. Computer science research is crucial for creating new security systems and tactics that defend against online threats.

Top Computer Science Research Topics

Before starting with the research, knowing the trendy research paper ideas for computer science exploration is important. It is not so easy to get your hands on the best research topics for computer science; spend some time and read about the following mind-boggling ideas before selecting one.

1. Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues, and Challenges

Welcome to the era of seamless connectivity and unparalleled efficiency! Blockchain and edge computing are two cutting-edge technologies that have the potential to revolutionize numerous sectors. Blockchain is a distributed ledger technology that is decentralized and offers a safe and transparent method of storing and transferring data.

As a young researcher, you can pave the way for a more secure, efficient, and scalable architecture that integrates blockchain and edge computing systems. So, let's roll up our sleeves and get ready to push the boundaries of technology with this exciting innovation!

Blockchain helps to reduce latency and boost speed. Edge computing, on the other hand, entails processing data close to the generation source, such as sensors and IoT devices. Integrating edge computing with blockchain technologies can help to achieve safer, more effective, and scalable architecture.

Moreover, this research title for computer science might open doors of opportunities for you in the financial sector.

2. A Survey on Edge Computing Systems and Tools

With the rise in population, the data is multiplying by manifolds each day. It's high time we find efficient technology to store it. However, more research is required for the same.

Say hello to the future of computing with edge computing! The edge computing system can store vast amounts of data to retrieve in the future. It also provides fast access to information in need. It maintains computing resources from the cloud and data centers while processing.

Edge computing systems bring processing power closer to the data source, resulting in faster and more efficient computing. But what tools are available to help us harness the power of edge computing?

As a part of this research, you will look at the newest edge computing tools and technologies to see how they can improve your computing experience. Here are some of the tools you might get familiar with upon completion of this research:

• Apache NiFi:  A framework for data processing that enables users to gather, transform, and transfer data from edge devices to cloud computing infrastructure.
• Microsoft Azure IoT Edge: A platform in the cloud that enables the creation and deployment of cutting-edge intelligent applications.
• OpenFog Consortium:  An organization that supports the advancement of fog computing technologies and architectures is the OpenFog Consortium.

3. Machine Learning: Algorithms, Real-world Applications, and Research Directions

Machine learning is the superset of Artificial Intelligence; a ground-breaking technology used to train machines to mimic human action and work. ML is used in everything from virtual assistants to self-driving cars and is revolutionizing the way we interact with computers. But what is machine learning exactly, and what are some of its practical uses and future research directions?

To find answers to such questions, it can be a wonderful choice to pick from the pool of various computer science dissertation ideas.

You will discover how computers learn several actions without explicit programming and see how they perform beyond their current capabilities. However, to understand better, having some basic programming knowledge always helps. KnowledgeHut’s Programming course for beginners will help you learn the most in-demand programming languages and technologies with hands-on projects.

During the research, you will work on and study

• Algorithm: Machine learning includes many algorithms, from decision trees to neural networks.
• Applications in the Real-world: You can see the usage of ML in many places. It can early detect and diagnose diseases like cancer. It can detect fraud when you are making payments. You can also use it for personalized advertising.
• Research Trend:  The most recent developments in machine learning research, include explainable AI, reinforcement learning, and federated learning.

While a single research paper is not enough to bring the light on an entire domain as vast as machine learning; it can help you witness how applicable it is in numerous fields, like engineering, data science & analysis, business intelligence, and many more.

Whether you are a data scientist with years of experience or a curious tech enthusiast, machine learning is an intriguing and vital field that's influencing the direction of technology. So why not dig deeper?

4. Evolutionary Algorithms and their Applications to Engineering Problems

Imagine a system that can solve most of your complex queries. Are you interested to know how these systems work? It is because of some algorithms. But what are they, and how do they work? Evolutionary algorithms use genetic operators like mutation and crossover to build new generations of solutions rather than starting from scratch.

This research topic can be a choice of interest for someone who wants to learn more about algorithms and their vitality in engineering.

Evolutionary algorithms are transforming the way we approach engineering challenges by allowing us to explore enormous solution areas and optimize complex systems.

The possibilities are infinite as long as this technology is developed further. Get ready to explore the fascinating world of evolutionary algorithms and their applications in addressing engineering issues.

5. The Role of Big Data Analytics in the Industrial Internet of Things

Datasets can have answers to most of your questions. With good research and approach, analyzing this data can bring magical results. Welcome to the world of data-driven insights! Big Data Analytics is the transformative process of extracting valuable knowledge and patterns from vast and complex datasets, boosting innovation and informed decision-making.

This field allows you to transform the enormous amounts of data produced by IoT devices into insightful knowledge that has the potential to change how large-scale industries work. It's like having a crystal ball that can foretell.

Big data analytics is being utilized to address some of the most critical issues, from supply chain optimization to predictive maintenance. Using it, you can find patterns, spot abnormalities, and make data-driven decisions that increase effectiveness and lower costs for several industrial operations by analyzing data from sensors and other IoT devices.

The area is so vast that you'll need proper research to use and interpret all this information. Choose this as your computer research topic to discover big data analytics' most compelling applications and benefits. You will see that a significant portion of industrial IoT technology demands the study of interconnected systems, and there's nothing more suitable than extensive data analysis.

6. An Efficient Lightweight Integrated Blockchain (ELIB) Model for IoT Security and Privacy

Are you concerned about the security and privacy of your Internet of Things (IoT) devices? As more and more devices become connected, it is more important than ever to protect the security and privacy of data. If you are interested in cyber security and want to find new ways of strengthening it, this is the field for you.

ELIB is a cutting-edge solution that offers private and secure communication between IoT devices by fusing the strength of blockchain with lightweight cryptography. This architecture stores encrypted data on a distributed ledger so only parties with permission can access it.

But why is ELIB so practical and portable? ELIB uses lightweight cryptography to provide quick and effective communication between devices, unlike conventional blockchain models that need complicated and resource-intensive computations.

Due to its increasing vitality, it is gaining popularity as a research topic as someone aware that this framework works and helps reinstate data security is highly demanded in financial and banking.

7. Natural Language Processing Techniques to Reveal Human-Computer Interaction for Development Research Topics

Welcome to the world where machines decode the beauty of the human language. With natural language processing (NLP) techniques, we can analyze the interactions between humans and computers to reveal valuable insights for development research topics. It is also one of the most crucial PhD topics in computer science as NLP-based applications are gaining more and more traction.

Etymologically, natural language processing (NLP) is a potential technique that enables us to examine and comprehend natural language data, such as discussions between people and machines. Insights on user behaviour, preferences, and pain areas can be gleaned from these encounters utilizing NLP approaches.

But which specific areas should we leverage on using NLP methods? This is precisely what you’ll discover while doing this computer science research.

Gear up to learn more about the fascinating field of NLP and how it can change how we design and interact with technology, whether you are a UX designer, a data scientist, or just a curious tech lover and linguist.

8. All One Needs to Know About Fog Computing and Related Edge Computing Paradigms: A Complete Survey

If you are an IoT expert or a keen lover of the Internet of Things, you should leap and move forward to discovering Fog Computing. With the rise of connected devices and the Internet of Things (IoT), traditional cloud computing models are no longer enough. That's where fog computing and related edge computing paradigms come in.

Fog computing is a distributed approach that brings processing and data storage closer to the devices that generate and consume data by extending cloud computing to the network's edge.

As computing technologies are significantly used today, the area has become a hub for researchers to delve deeper into the underlying concepts and devise more and more fog computing frameworks. You can also contribute to and master this architecture by opting for this stand-out topic for your research.

Tips and Tricks to Write Computer Research Topics

Before starting to explore these hot research topics in computer science you may have to know about some tips and tricks that can easily help you.

• Know your interest.
• Choose the topic wisely.
• Make proper research about the demand of the topic.
• Get proper references.
• Discuss with experts.

By following these tips and tricks, you can write a compelling and impactful computer research topic that contributes to the field's advancement and addresses important research gaps.

From machine learning and artificial intelligence to blockchain, edge computing, and big data analytics, numerous trending computer research topics exist to explore.

One of the most important trends is using cutting-edge technology to address current issues. For instance, new IIoT security and privacy opportunities are emerging by integrating blockchain and edge computing. Similarly, the application of natural language processing methods is assisting in revealing human-computer interaction and guiding the creation of new technologies.

Another trend is the growing emphasis on sustainability and moral considerations in technological development. Researchers are looking into how computer science might help in innovation.

With the latest developments and leveraging cutting-edge tools and techniques, researchers can make meaningful contributions to the field and help shape the future of technology. Going for Full-stack Developer online training will help you master the latest tools and technologies. 

Frequently Asked Questions (FAQs)

Research in computer science is mainly focused on different niches. It can be theoretical or technical as well. It completely depends upon the candidate and his focused area. They may do research for inventing new algorithms or many more to get advanced responses in that field.  

Yes, moreover it would be a very good opportunity for the candidate. Because computer science students may have a piece of knowledge about the topic previously. They may find Easy thesis topics for computer science to fulfill their research through KnowledgeHut. 

 There are several scopes available for computer science. A candidate can choose different subjects such as AI, database management, software design, graphics, and many more. 

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PhD Research/Project Topics For Electrical and Computer Engineering

Looking for a PhD research or project topic in Electrical and Computer Engineering? Here are some exciting and cutting-edge ideas that can help you get started.

1. Design and Optimization of Power Systems for Renewable Energy Sources

With the world moving towards a greener future, the need for sustainable and renewable energy sources has never been more crucial. PhD research in the design and optimization of power systems for renewable energy sources can explore ways to improve the efficiency, reliability, and cost-effectiveness of renewable energy systems.

2. Artificial Intelligence and Machine Learning Applications for Robotics and Automation

Advancements in Artificial Intelligence and Machine Learning have revolutionized the field of Robotics and Automation. PhD research in this field can focus on developing intelligent systems for autonomous robots and machines that can learn and adapt to their environment.

3. Development of Advanced Signal and Image Processing Techniques for Real-time Applications

Signal and Image Processing techniques are used extensively in a wide range of applications, including healthcare, security, and entertainment. PhD research in this field can focus on developing advanced techniques for real-time processing of signals and images, such as video and audio streams.

4. Analysis and Optimization of Next-gen Wireless Communication Networks

Wireless communication networks are constantly evolving, and the next-generation networks are expected to offer higher data rates, improved reliability, and enhanced security. PhD research in this field can explore ways to analyze and optimize next-gen wireless communication networks for improved performance and efficiency.

5. Development of Efficient Algorithms for High-Performance Computing and Big Data Processing

With the increasing amount of data generated every day, there is a growing need for efficient algorithms for High-Performance Computing and Big Data processing. PhD research in this field can focus on developing advanced algorithms that can process large amounts of data efficiently, while maintaining accuracy and reliability.

6. Design of Advanced Control Systems for Autonomous Vehicles and Drones

The field of Autonomous Vehicles and Drones is rapidly evolving, with a wide range of applications in transportation, surveillance, and entertainment. PhD research in this field can focus on designing advanced control systems for autonomous vehicles and drones that can operate safely and efficiently in complex environments.

In conclusion

These are just a few ideas for PhD research or project topics in Electrical and Computer Engineering. These fields are constantly evolving, and there is a wealth of opportunities for research and innovation. Choose a topic that interests you and can make a significant contribution to the field.

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PhD in Computer Science Topics 2023: Top Research Ideas

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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How to Contact Faculty for IW/Thesis Advising

Send the professor an e-mail. When you write a professor, be clear that you want a meeting regarding a senior thesis or one-on-one IW project, and briefly describe the topic or idea that you want to work on. Check the faculty listing for email addresses.

Parastoo Abtahi, Room 419

Available for single-semester IW and senior thesis advising, 2023-2024

• Research Areas: Human-Computer Interaction (HCI), Augmented Reality (AR), and Spatial Computing
• Input techniques for on-the-go interaction (e.g., eye-gaze, microgestures, voice) with a focus on uncertainty, disambiguation, and privacy.
• Minimal and timely multisensory output (e.g., spatial audio, haptics) that enables users to attend to their physical environment and the people around them, instead of a 2D screen.
• Interaction with intelligent systems (e.g., IoT, robots) situated in physical spaces with a focus on updating users’ mental model despite the complexity and dynamicity of these systems.

Ryan Adams, Room 411

Research areas:

• Machine learning driven design
• Generative models for structured discrete objects
• Approximate inference in probabilistic models
• Accelerating solutions to partial differential equations
• Innovative uses of automatic differentiation
• Modeling and optimizing 3d printing and CNC machining

Andrew Appel, Room 209

• Research Areas: Formal methods, programming languages, compilers, computer security.
• Software verification (for which taking COS 326 / COS 510 is helpful preparation)
• Game theory of poker or other games (for which COS 217 / 226 are helpful)
• Computer game-playing programs (for which COS 217 / 226)
•  Risk-limiting audits of elections (for which ORF 245 or other knowledge of probability is useful)

Sanjeev Arora, Room 407

• Theoretical machine learning, deep learning and its analysis, natural language processing. My advisees would typically have taken a course in algorithms (COS423 or COS 521 or equivalent) and a course in machine learning.
• Show that finding approximate solutions to NP-complete problems is also NP-complete (i.e., come up with NP-completeness reductions a la COS 487). 
• Experimental Algorithms: Implementing and Evaluating Algorithms using existing software packages. 
• Studying/designing provable algorithms for machine learning and implementions using packages like scipy and MATLAB, including applications in Natural language processing and deep learning.
• Any topic in theoretical computer science.

David August, Room 221

• Research Areas: Computer Architecture, Compilers, Parallelism
• Containment-based approaches to security:  We have designed and tested a simple hardware+software containment mechanism that stops incorrect communication resulting from faults, bugs, or exploits from leaving the system.   Let's explore ways to use containment to solve real problems.  Expect to work with corporate security and technology decision-makers.
• Parallelism: Studies show much more parallelism than is currently realized in compilers and architectures.  Let's find ways to realize this parallelism.
• Any other interesting topic in computer architecture or compilers. 

Mark Braverman, 194 Nassau St., Room 231

Available for Spring 2024 single-semester IW, only

• Research Areas: computational complexity, algorithms, applied probability, computability over the real numbers, game theory and mechanism design, information theory.
• Topics in computational and communication complexity.
• Applications of information theory in complexity theory.
• Algorithms for problems under real-life assumptions.
• Game theory, network effects
• Mechanism design (could be on a problem proposed by the student)

Sebastian Caldas, 221 Nassau Street, Room 105

• Research Areas: collaborative learning, machine learning for healthcare. Typically, I will work with students that have taken COS324.
• Methods for collaborative and continual learning.
• Machine learning for healthcare applications.

Bernard Chazelle, 194 Nassau St., Room 301

• Research Areas: Natural Algorithms, Computational Geometry, Sublinear Algorithms. 
• Natural algorithms (flocking, swarming, social networks, etc).
• Sublinear algorithms
• Self-improving algorithms
• Markov data structures

Danqi Chen, Room 412

Not available for IW or thesis advising, 2023-2024

• My advisees would be expected to have taken a course in machine learning and ideally have taken COS484 or an NLP graduate seminar.
• Representation learning for text and knowledge bases
• Pre-training and transfer learning
• Question answering and reading comprehension
• Information extraction
• Text summarization
• Any other interesting topics related to natural language understanding/generation

Marcel Dall'Agnol, Corwin 034

Available for single-semester and senior thesis advising, 2023-2024

• Research Areas: Theoretical computer science. (Specifically, quantum computation, sublinear algorithms, complexity theory, interactive proofs and cryptography)

Jia Deng, Room 423

Available for Fall 2023 single-semester IW, only

•  Research Areas: Computer Vision, Machine Learning.
• Object recognition and action recognition
• Deep Learning, autoML, meta-learning
• Geometric reasoning, logical reasoning

Adji Bousso Dieng, Room 406

• Research areas: Vertaix is a research lab at Princeton University led by Professor Adji Bousso Dieng. We work at the intersection of artificial intelligence (AI) and the natural sciences. The models and algorithms we develop are motivated by problems in those domains and contribute to advancing methodological research in AI. We leverage tools in statistical machine learning and deep learning in developing methods for learning with the data, of various modalities, arising from the natural sciences.

Robert Dondero, Corwin Hall, Room 038

• Research Areas:  Software engineering; software engineering education.
• Develop or evaluate tools to facilitate student learning in undergraduate computer science courses at Princeton, and beyond.
• In particular, can code critiquing tools help students learn about software quality?

Zeev Dvir, 194 Nassau St., Room 250

Not available for IW or thesis advising, 2023-2024.

• Research Areas: computational complexity, pseudo-randomness, coding theory and discrete mathematics.
• Independent Research: I have various research problems related to Pseudorandomness, Coding theory, Complexity and Discrete mathematics - all of which require strong mathematical background. A project could also be based on writing a survey paper describing results from a few theory papers revolving around some particular subject.

Benjamin Eysenbach, Room 416

• Research areas: reinforcement learning, machine learning. My advisees would typically have taken COS324.
• Using RL algorithms to applications in science and engineering.
• Emergent behavior of RL algorithms on high-fidelity robotic simulators.
• Studying how architectures and representations can facilitate generalization.

Christiane Fellbaum, 1-S-14 Green

No longer available for single-term IW and senior thesis advising, 2023-2024

• Research Areas: theoretical and computational linguistics, word sense disambiguation, lexical resource construction, English and multilingual WordNet(s), ontology
• Anything having to do with natural language--come and see me with/for ideas suitable to your background and interests. Some topics students have worked on in the past:
• Developing parsers, part-of-speech taggers, morphological analyzers for underrepresented languages (you don't have to know the language to develop such tools!)
• Quantitative approaches to theoretical linguistics questions
• Extensions and interfaces for WordNet (English and WN in other languages),
• Applications of WordNet(s), including:
• Foreign language tutoring systems,
• Spelling correction software,
• Word-finding/suggestion software for ordinary users and people with memory problems,
• Machine Translation 
• Sentiment and Opinion detection
• Automatic reasoning and inferencing
• Collaboration with professors in the social sciences and humanities ("Digital Humanities")

Adam Finkelstein, Room 424 

• Research Areas: computer graphics, audio.

Robert S. Fish, Corwin Hall, Room 037

No longer available for single-semester IW and senior thesis advising, 2023-2024

• Networking and telecommunications
• Learning, perception, and intelligence, artificial and otherwise;
• Human-computer interaction and computer-supported cooperative work
• Online education, especially in Computer Science Education
• Topics in research and development innovation methodologies including standards, open-source, and entrepreneurship
• Distributed autonomous organizations and related blockchain technologies

Michael Freedman, Room 308 

• Research Areas: Distributed systems, security, networking
• Projects related to streaming data analysis, datacenter systems and networks, untrusted cloud storage and applications. Please see my group website at http://sns.cs.princeton.edu/ for current research projects.

Ruth Fong, Room 032

• Research Areas: computer vision, machine learning, deep learning, interpretability, explainable AI, fairness and bias in AI
• Develop a technique for understanding AI models
• Design a AI model that is interpretable by design
• Build a paradigm for detecting and/or correcting failure points in an AI model
• Analyze an existing AI model and/or dataset to better understand its failure points
• Build a computer vision system for another domain (e.g., medical imaging, satellite data, etc.)
• Develop a software package for explainable AI
• Adapt explainable AI research to a consumer-facing problem

Note: I am happy to advise any project if there's a sufficient overlap in interest and/or expertise; please reach out via email to chat about project ideas.

Tom Griffiths, Room 405

Research areas: computational cognitive science, computational social science, machine learning and artificial intelligence

Note: I am open to projects that apply ideas from computer science to understanding aspects of human cognition in a wide range of areas, from decision-making to cultural evolution and everything in between. For example, we have current projects analyzing chess game data and magic tricks, both of which give us clues about how human minds work. Students who have expertise or access to data related to games, magic, strategic sports like fencing, or other quantifiable domains of human behavior feel free to get in touch.

Aarti Gupta, Room 220

• Research Areas: Formal methods, program analysis, logic decision procedures
• Finding bugs in open source software using automatic verification tools
• Software verification (program analysis, model checking, test generation)
• Decision procedures for logical reasoning (SAT solvers, SMT solvers)

Elad Hazan, Room 409  

• Research interests: machine learning methods and algorithms, efficient methods for mathematical optimization, regret minimization in games, reinforcement learning, control theory and practice
• Machine learning, efficient methods for mathematical optimization, statistical and computational learning theory, regret minimization in games.
• Implementation and algorithm engineering for control, reinforcement learning and robotics
• Implementation and algorithm engineering for time series prediction

Felix Heide, Room 410

• Research Areas: Computational Imaging, Computer Vision, Machine Learning (focus on Optimization and Approximate Inference).
• Optical Neural Networks
• Hardware-in-the-loop Holography
• Zero-shot and Simulation-only Learning
• Object recognition in extreme conditions
• 3D Scene Representations for View Generation and Inverse Problems
• Long-range Imaging in Scattering Media
• Hardware-in-the-loop Illumination and Sensor Optimization
• Inverse Lidar Design
• Phase Retrieval Algorithms
• Proximal Algorithms for Learning and Inference
• Domain-Specific Language for Optics Design

Kyle Jamieson, Room 306

• Research areas: Wireless and mobile networking; indoor radar and indoor localization; Internet of Things
• See other topics on my independent work  ideas page  (campus IP and CS dept. login req'd)

Alan Kaplan, 221 Nassau Street, Room 105

Research Areas:

• Random apps of kindness - mobile application/technology frameworks used to help individuals or communities; topic areas include, but are not limited to: first response, accessibility, environment, sustainability, social activism, civic computing, tele-health, remote learning, crowdsourcing, etc.
• Tools automating programming language interoperability - Java/C++, React Native/Java, etc.
• Software visualization tools for education
• Connected consumer devices, applications and protocols

Brian Kernighan, Room 311

• Research Areas: application-specific languages, document preparation, user interfaces, software tools, programming methodology
• Application-oriented languages, scripting languages.
• Tools; user interfaces
• Digital humanities

Zachary Kincaid, Room 219

• Research areas: programming languages, program analysis, program verification, automated reasoning
• Independent Research Topics:
• Develop a practical algorithm for an intractable problem (e.g., by developing practical search heuristics, or by reducing to, or by identifying a tractable sub-problem, ...).
• Design a domain-specific programming language, or prototype a new feature for an existing language.
• Any interesting project related to programming languages or logic.

Gillat Kol, Room 316

Aleksandra korolova, 309 sherrerd hall.

Available for single-term IW and senior thesis advising, 2023-2024

• Research areas: Societal impacts of algorithms and AI; privacy; fair and privacy-preserving machine learning; algorithm auditing.

Advisees typically have taken one or more of COS 226, COS 324, COS 423, COS 424 or COS 445.

Amit Levy, Room 307

• Research Areas: Operating Systems, Distributed Systems, Embedded Systems, Internet of Things
• Distributed hardware testing infrastructure
• Second factor security tokens
• Low-power wireless network protocol implementation
• USB device driver implementation

Kai Li, Room 321

• Research Areas: Distributed systems; storage systems; content-based search and data analysis of large datasets.
• Fast communication mechanisms for heterogeneous clusters.
• Approximate nearest-neighbor search for high dimensional data.
• Data analysis and prediction of in-patient medical data.
• Optimized implementation of classification algorithms on manycore processors.

Xiaoyan Li, 221 Nassau Street, Room 104

• Research areas: Information retrieval, novelty detection, question answering, AI, machine learning and data analysis.
• Explore new statistical retrieval models for document retrieval and question answering.
• Apply AI in various fields.
• Apply supervised or unsupervised learning in health, education, finance, and social networks, etc.
• Any interesting project related to AI, machine learning, and data analysis.

Wyatt Lloyd, Room 323

• Research areas: Distributed Systems
• Caching algorithms and implementations
• Storage systems
• Distributed transaction algorithms and implementations

Margaret Martonosi, Room 208

• Quantum Computing research, particularly related to architecture and compiler issues for QC.
• Computer architectures specialized for modern workloads (e.g., graph analytics, machine learning algorithms, mobile applications
• Investigating security and privacy vulnerabilities in computer systems, particularly IoT devices.
• Other topics in computer architecture or mobile / IoT systems also possible.

Jonathan Mayer, Sherrerd Hall, Room 307 

• Research areas: Technology law and policy, with emphasis on national security, criminal procedure, consumer privacy, network management, and online speech.
• Assessing the effects of government policies, both in the public and private sectors.
• Collecting new data that relates to government decision making, including surveying current business practices and studying user behavior.
• Developing new tools to improve government processes and offer policy alternatives.

Andrés Monroy-Hernández, Room 405

• Research Areas: Human-Computer Interaction, Social Computing, Public-Interest Technology, Augmented Reality, Urban Computing
• Research interests:developing public-interest socio-technical systems.  We are currently creating alternatives to gig work platforms that are more equitable for all stakeholders. For instance, we are investigating the socio-technical affordances necessary to support a co-op food delivery network owned and managed by workers and restaurants. We are exploring novel system designs that support self-governance, decentralized/federated models, community-centered data ownership, and portable reputation systems.  We have opportunities for students interested in human-centered computing, UI/UX design, full-stack software development, and qualitative/quantitative user research.
• Beyond our core projects, we are open to working on research projects that explore the use of emerging technologies, such as AR, wearables, NFTs, and DAOs, for creative and out-of-the-box applications.

Christopher Moretti, Corwin Hall, Room 036

• Research areas: Distributed systems, high-throughput computing, computer science/engineering education
• Expansion, improvement, and evaluation of open-source distributed computing software.
• Applications of distributed computing for "big science" (e.g. biometrics, data mining, bioinformatics)
• Software and best practices for computer science education and study, especially Princeton's 126/217/226 sequence or MOOCs development
• Sports analytics and/or crowd-sourced computing

Radhika Nagpal, F316 Engineering Quadrangle

• Research areas: control, robotics and dynamical systems

Karthik Narasimhan, Room 422

• Research areas: Natural Language Processing, Reinforcement Learning
• Autonomous agents for text-based games ( https://www.microsoft.com/en-us/research/project/textworld/ )
• Transfer learning/generalization in NLP
• Techniques for generating natural language
• Model-based reinforcement learning

Arvind Narayanan, 308 Sherrerd Hall 

Research Areas: fair machine learning (and AI ethics more broadly), the social impact of algorithmic systems, tech policy

Pedro Paredes, Corwin Hall, Room 041

My primary research work is in Theoretical Computer Science.

 * Research Interest: Spectral Graph theory, Pseudorandomness, Complexity theory, Coding Theory, Quantum Information Theory, Combinatorics.

The IW projects I am interested in advising can be divided into three categories:

 1. Theoretical research

I am open to advise work on research projects in any topic in one of my research areas of interest. A project could also be based on writing a survey given results from a few papers. Students should have a solid background in math (e.g., elementary combinatorics, graph theory, discrete probability, basic algebra/calculus) and theoretical computer science (226 and 240 material, like big-O/Omega/Theta, basic complexity theory, basic fundamental algorithms). Mathematical maturity is a must.

A (non exhaustive) list of topics of projects I'm interested in:   * Explicit constructions of better vertex expanders and/or unique neighbor expanders.   * Construction deterministic or random high dimensional expanders.   * Pseudorandom generators for different problems.   * Topics around the quantum PCP conjecture.   * Topics around quantum error correcting codes and locally testable codes, including constructions, encoding and decoding algorithms.

 2. Theory informed practical implementations of algorithms   Very often the great advances in theoretical research are either not tested in practice or not even feasible to be implemented in practice. Thus, I am interested in any project that consists in trying to make theoretical ideas applicable in practice. This includes coming up with new algorithms that trade some theoretical guarantees for feasible implementation yet trying to retain the soul of the original idea; implementing new algorithms in a suitable programming language; and empirically testing practical implementations and comparing them with benchmarks / theoretical expectations. A project in this area doesn't have to be in my main areas of research, any theoretical result could be suitable for such a project.

Some examples of areas of interest:   * Streaming algorithms.   * Numeric linear algebra.   * Property testing.   * Parallel / Distributed algorithms.   * Online algorithms.    3. Machine learning with a theoretical foundation

I am interested in projects in machine learning that have some mathematical/theoretical, even if most of the project is applied. This includes topics like mathematical optimization, statistical learning, fairness and privacy.

One particular area I have been recently interested in is in the area of rating systems (e.g., Chess elo) and applications of this to experts problems.

Final Note: I am also willing to advise any project with any mathematical/theoretical component, even if it's not the main one; please reach out via email to chat about project ideas.

Iasonas Petras, Corwin Hall, Room 033

• Research Areas: Information Based Complexity, Numerical Analysis, Quantum Computation.
• Prerequisites: Reasonable mathematical maturity. In case of a project related to Quantum Computation a certain familiarity with quantum mechanics is required (related courses: ELE 396/PHY 208).
• Possible research topics include:

1.   Quantum algorithms and circuits:

• i. Design or simulation quantum circuits implementing quantum algorithms.
• ii. Design of quantum algorithms solving/approximating continuous problems (such as Eigenvalue problems for Partial Differential Equations).

2.   Information Based Complexity:

• i. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems in various settings (for example worst case or average case). 
• ii. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems under new tractability and error criteria.
• iii. Necessary and sufficient conditions for tractability of Weighted problems.
• iv. Necessary and sufficient conditions for tractability of Weighted Problems under new tractability and error criteria.

3. Topics in Scientific Computation:

• i. Randomness, Pseudorandomness, MC and QMC methods and their applications (Finance, etc)

Yuri Pritykin, 245 Carl Icahn Lab

• Research interests: Computational biology; Cancer immunology; Regulation of gene expression; Functional genomics; Single-cell technologies.
• Potential research projects: Development, implementation, assessment and/or application of algorithms for analysis, integration, interpretation and visualization of multi-dimensional data in molecular biology, particularly single-cell and spatial genomics data.

Benjamin Raphael, Room 309  

• Research interests: Computational biology and bioinformatics; Cancer genomics; Algorithms and machine learning approaches for analysis of large-scale datasets
• Implementation and application of algorithms to infer evolutionary processes in cancer
• Identifying correlations between combinations of genomic mutations in human and cancer genomes
• Design and implementation of algorithms for genome sequencing from new DNA sequencing technologies
• Graph clustering and network anomaly detection, particularly using diffusion processes and methods from spectral graph theory

Vikram Ramaswamy, 035 Corwin Hall

• Research areas: Interpretability of AI systems, Fairness in AI systems, Computer vision.
• Constructing a new method to explain a model / create an interpretable by design model
• Analyzing a current model / dataset to understand bias within the model/dataset
• Proposing new fairness evaluations
• Proposing new methods to train to improve fairness
• Developing synthetic datasets for fairness / interpretability benchmarks
• Understanding robustness of models

Ran Raz, Room 240

• Research Area: Computational Complexity
• Independent Research Topics: Computational Complexity, Information Theory, Quantum Computation, Theoretical Computer Science

Szymon Rusinkiewicz, Room 406

• Research Areas: computer graphics; computer vision; 3D scanning; 3D printing; robotics; documentation and visualization of cultural heritage artifacts
• Research ways of incorporating rotation invariance into computer visiontasks such as feature matching and classification
• Investigate approaches to robust 3D scan matching
• Model and compensate for imperfections in 3D printing
• Given a collection of small mobile robots, apply control policies learned in simulation to the real robots.

Olga Russakovsky, Room 408

• Research Areas: computer vision, machine learning, deep learning, crowdsourcing, fairness&bias in AI
• Design a semantic segmentation deep learning model that can operate in a zero-shot setting (i.e., recognize and segment objects not seen during training)
• Develop a deep learning classifier that is impervious to protected attributes (such as gender or race) that may be erroneously correlated with target classes
• Build a computer vision system for the novel task of inferring what object (or part of an object) a human is referring to when pointing to a single pixel in the image. This includes both collecting an appropriate dataset using crowdsourcing on Amazon Mechanical Turk, creating a new deep learning formulation for this task, and running extensive analysis of both the data and the model

Sebastian Seung, Princeton Neuroscience Institute, Room 153

• Research Areas: computational neuroscience, connectomics, "deep learning" neural networks, social computing, crowdsourcing, citizen science
• Gamification of neuroscience (EyeWire  2.0)
• Semantic segmentation and object detection in brain images from microscopy
• Computational analysis of brain structure and function
• Neural network theories of brain function

Jaswinder Pal Singh, Room 324

• Research Areas: Boundary of technology and business/applications; building and scaling technology companies with special focus at that boundary; parallel computing systems and applications: parallel and distributed applications and their implications for software and architectural design; system software and programming environments for multiprocessors.
• Develop a startup company idea, and build a plan/prototype for it.
• Explore tradeoffs at the boundary of technology/product and business/applications in a chosen area.
• Study and develop methods to infer insights from data in different application areas, from science to search to finance to others. 
• Design and implement a parallel application. Possible areas include graphics, compression, biology, among many others. Analyze performance bottlenecks using existing tools, and compare programming models/languages.
• Design and implement a scalable distributed algorithm.

Mona Singh, Room 420

• Research Areas: computational molecular biology, as well as its interface with machine learning and algorithms.
• Whole and cross-genome methods for predicting protein function and protein-protein interactions.
• Analysis and prediction of biological networks.
• Computational methods for inferring specific aspects of protein structure from protein sequence data.
• Any other interesting project in computational molecular biology.

Robert Tarjan, 194 Nassau St., Room 308

Available for single-semester IW and senior thesis advising, 2022-2023

• Research Areas: Data structures; graph algorithms; combinatorial optimization; computational complexity; computational geometry; parallel algorithms.
• Implement one or more data structures or combinatorial algorithms to provide insight into their empirical behavior.
• Design and/or analyze various data structures and combinatorial algorithms.

Olga Troyanskaya, Room 320

• Research Areas: Bioinformatics; analysis of large-scale biological data sets (genomics, gene expression, proteomics, biological networks); algorithms for integration of data from multiple data sources; visualization of biological data; machine learning methods in bioinformatics.
• Implement and evaluate one or more gene expression analysis algorithm.
• Develop algorithms for assessment of performance of genomic analysis methods.
• Develop, implement, and evaluate visualization tools for heterogeneous biological data.

David Walker, Room 211

• Research Areas: Programming languages, type systems, compilers, domain-specific languages, software-defined networking and security
• Independent Research Topics:  Any other interesting project that involves humanitarian hacking, functional programming, domain-specific programming languages, type systems, compilers, software-defined networking, fault tolerance, language-based security, theorem proving, logic or logical frameworks.

Shengyi Wang, Postdoctoral Research Associate, Room 216

• Independent Research topics: Explore Escher-style tilings using (introductory) group theory and automata theory to produce beautiful pictures.

Kevin Wayne, Corwin Hall, Room 040

• Research Areas: design, analysis, and implementation of algorithms; data structures; combinatorial optimization; graphs and networks.
• Design and implement computer visualizations of algorithms or data structures.
• Develop pedagogical tools or programming assignments for the computer science curriculum at Princeton and beyond.
• Develop assessment infrastructure and assessments for MOOCs.

Matt Weinberg, 194 Nassau St., Room 222

• Research Areas: algorithms, algorithmic game theory, mechanism design, game theoretical problems in {Bitcoin, networking, healthcare}.
• Theoretical questions related to COS 445 topics such as matching theory, voting theory, auction design, etc. 
• Theoretical questions related to incentives in applications like Bitcoin, the Internet, health care, etc. In a little bit more detail: protocols for these systems are often designed assuming that users will follow them. But often, users will actually be strictly happier to deviate from the intended protocol. How should we reason about user behavior in these protocols? How should we design protocols in these settings?

Huacheng Yu, Room 310

• data structures
• streaming algorithms
• design and analyze data structures / streaming algorithms
• prove impossibility results (lower bounds)
• implement and evaluate data structures / streaming algorithms

Ellen Zhong, Room 314

No longer available for single-term IW  and senior thesis advising, 2023-2024

Opportunities outside the department

We encourage students to look in to doing interdisciplinary computer science research and to work with professors in departments other than computer science.  However, every CS independent work project must have a strong computer science element (even if it has other scientific or artistic elements as well.)  To do a project with an adviser outside of computer science you must have permission of the department.  This can be accomplished by having a second co-adviser within the computer science department or by contacting the independent work supervisor about the project and having he or she sign the independent work proposal form.

Here is a list of professors outside the computer science department who are eager to work with computer science undergraduates.

Maria Apostolaki, Engineering Quadrangle, C330

• Research areas: Computing & Networking, Data & Information Science, Security & Privacy

Branko Glisic, Engineering Quadrangle, Room E330

• Documentation of historic structures
• Cyber physical systems for structural health monitoring
• Developing virtual and augmented reality applications for documenting structures
• Applying machine learning techniques to generate 3D models from 2D plans of buildings
•  Contact : Rebecca Napolitano, rkn2 (@princeton.edu)

Mihir Kshirsagar, Sherrerd Hall, Room 315

Center for Information Technology Policy.

• Consumer protection
• Content regulation
• Competition law
• Economic development
• Surveillance and discrimination

Sharad Malik, Engineering Quadrangle, Room B224

Select a Senior Thesis Adviser for the 2020-21 Academic Year.

• Design of reliable hardware systems
• Verifying complex software and hardware systems

Prateek Mittal, Engineering Quadrangle, Room B236

• Internet security and privacy 
• Social Networks
• Privacy technologies, anonymous communication
• Network Science
• Internet security and privacy: The insecurity of Internet protocols and services threatens the safety of our critical network infrastructure and billions of end users. How can we defend end users as well as our critical network infrastructure from attacks?
• Trustworthy social systems: Online social networks (OSNs) such as Facebook, Google+, and Twitter have revolutionized the way our society communicates. How can we leverage social connections between users to design the next generation of communication systems?
• Privacy Technologies: Privacy on the Internet is eroding rapidly, with businesses and governments mining sensitive user information. How can we protect the privacy of our online communications? The Tor project (https://www.torproject.org/) is a potential application of interest.

Ken Norman,  Psychology Dept, PNI 137

• Research Areas: Memory, the brain and computation 
• Lab:  Princeton Computational Memory Lab

Potential research topics

• Methods for decoding cognitive state information from neuroimaging data (fMRI and EEG) 
• Neural network simulations of learning and memory

Caroline Savage

Office of Sustainability, Phone:(609)258-7513, Email: cs35 (@princeton.edu)

The  Campus as Lab  program supports students using the Princeton campus as a living laboratory to solve sustainability challenges. The Office of Sustainability has created a list of campus as lab research questions, filterable by discipline and topic, on its  website .

An example from Computer Science could include using  TigerEnergy , a platform which provides real-time data on campus energy generation and consumption, to study one of the many energy systems or buildings on campus. Three CS students used TigerEnergy to create a  live energy heatmap of campus .

Other potential projects include:

• Apply game theory to sustainability challenges
• Develop a tool to help visualize interactions between complex campus systems, e.g. energy and water use, transportation and storm water runoff, purchasing and waste, etc.
• How can we learn (in aggregate) about individuals’ waste, energy, transportation, and other behaviors without impinging on privacy?

Janet Vertesi, Sociology Dept, Wallace Hall, Room 122

• Research areas: Sociology of technology; Human-computer interaction; Ubiquitous computing.
• Possible projects: At the intersection of computer science and social science, my students have built mixed reality games, produced artistic and interactive installations, and studied mixed human-robot teams, among other projects.

David Wentzlaff, Engineering Quadrangle, Room 228

Computing, Operating Systems, Sustainable Computing.

• Instrument Princeton's Green (HPCRC) data center
• Investigate power utilization on an processor core implemented in an FPGA
• Dismantle and document all of the components in modern electronics. Invent new ways to build computers that can be recycled easier.
• Other topics in parallel computer architecture or operating systems

ACS Meetings & Expos

Acs fall 2024.

Hybrid event

Aug 18–22, 2024

Join us in Denver, Colorado from August 18-22 to get the latest research in chemistry, network, and attend career events. The theme of this meeting is "Elevating Chemistry" and will explore a variety of topics such as:

• Elevating chemistry performance
• Elevating chemistry for the public good
• Elevating safety for graduate students
• Elevating chemical education
• Chemistry in space
• Elevating the practice of sustainable chemistry
• Metals, minerals and molecules and more

Choose from thousands of oral presentations covering every area of chemistry, attend the poster session, and visit the expo hall to meet vendors and learn about new tools and processes.

Don’t miss the Keynote Events, including the Plenary and the Kavli Lecture Series, exploring the theme of the meeting, and featuring emerging leaders and innovations in chemistry.

In addition to the technical papers, ACS Fall 2024 offers several events to advance your career, explore career paths, and hone your leadership skills.

Our undergraduate student program prepares students for their next step, whether it’s exploring various careers or getting ready for graduate school. The educator events offer tips on effective teaching practices.

Explore all that ACS Fall 2024 has to offer!

Related Events:

28th annual green chemistry & engineering conference, acs africa regional conference on green and sustainable chemistry, 2024 southwest regional meeting, acs institute.

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Explore the ACS Institute

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