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Latest Computer Science Research Topics for 2024

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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.

Top 12 Computer Science Research Topics for 2024 

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.

9. Artificial Intelligence (AI)

The field of artificial intelligence studies how to build machines with human-like cognitive abilities and it is one of the  trending research topics in computer science . Unlike humans, AI technology can handle massive amounts of data in many ways. Some important areas of AI where more research is needed include:  

• Deep learning: Within the field of Machine Learning, Deep Learning mimics the inner workings of the human brain to process and apply judgements based on input.   
• Reinforcement learning:  With artificial intelligence, a machine can learn things in a manner akin to human learning through a process called reinforcement learning.  
• Natural Language processing (NLP):  While it is evident that humans are capable of vocal communication, machines are also capable of doing so now! This is referred to as "natural language processing," in which computers interpret and analyse spoken words.  

10. Digital Image Processing

Digital image processing is the process of processing digital images using computer algorithms.  Recent research topics in computer science  around digital image processing are grounded in these techniques. Digital image processing, a subset of digital signal processing, is superior to analogue image processing and has numerous advantages. It allows several algorithms to be applied to the input data and avoids issues like noise accumulation and signal distortion during processing. Digital image processing comes in a variety of forms for research. The most recent thesis and research topics in digital image processing are listed below:  

• Image Acquisition  
• Image Enhancement  
• Image Restoration  
• Color Image Processing  
• Wavelets and Multi Resolution Processing  
• Compression  
• Morphological Processing  

11. Data Mining

The method by which valuable information is taken out of the raw data is called data mining. Using various data mining tools and techniques, data mining is used to complete many tasks, including association rule development, prediction analysis, and clustering. The most effective method for extracting valuable information from unprocessed data in data mining technologies is clustering. The clustering process allows for the analysis of relevant information from a dataset by grouping similar and dissimilar types of data. Data mining offers a wide range of trending  computer science research topics for undergraduates :  

• Data Spectroscopic Clustering  
• Asymmetric spectral clustering  
• Model-based Text Clustering  
• Parallel Spectral Clustering in Distributed System  
• Self-Tuning Spectral Clustering  

12. Robotics

We explore how robots interact with their environments, surrounding objects, other robots, and humans they are assisting through the research, design, and construction of a wide range of robot systems in the field of robotics. Numerous academic fields, including mathematics, physics, biology, and computer science, are used in robotics. Artificial intelligence (AI), physics simulation, and advanced sensor processing (such as computer vision) are some of the key technologies from computer science.  Msc computer science project topic s focus on below mentioned areas around Robotics:  

• Human Robot collaboration  
• Swarm Robotics  
• Robot learning and adaptation  
• Soft Robotics  
• Ethical considerations in Robotics  

How to Choose the Right Computer Science Research Topics?  

Choosing the  research areas in computer science  could be overwhelming. You can follow the below mentioned tips in your pursuit:  

• Chase Your Curiosity:  Think about what in the tech world keeps you up at night, in a good way. If it makes you go "hmm," that's the stuff to dive into.  
• Tech Trouble Hunt: Hunt for the tech troubles that bug you. You know, those things that make you mutter, "There's gotta be a better way!" That's your golden research nugget.  
• Interact with Nerds: Grab a coffee (or your beverage of choice) and have a laid-back chat with the tech geeks around you. They might spill the beans on cool problems or untapped areas in computer science.  
• Resource Reality Check: Before diving in, do a quick reality check. Make sure your chosen topic isn't a resource-hungry beast. You want something you can tackle without summoning a tech army.  
• Tech Time Travel: Imagine you have a time machine. What future tech would blow your mind? Research that takes you on a journey to the future is like a time travel adventure.  
• Dream Big, Start Small:  Your topic doesn't have to change the world on day one. Dream big, but start small. The best research often grows from tiny, curious seeds.  
• Be the Tech Rebel: Don't be afraid to be a bit rebellious. If everyone's zigging, you might want to zag. The most exciting discoveries often happen off the beaten path.  
• Make it Fun: Lastly, make sure it's fun. If you're going to spend time on it, might as well enjoy the ride. Fun research is the best research.  

Tips and Tricks to Write Computer Science 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.

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.

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 IoT 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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Research Topics & Ideas: CompSci & IT

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

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

NB – This is just the start…

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

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

Overview: CompSci Research Topics

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

Topics/Ideas: Algorithms & Data Structures

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

Topics & Ideas: Artificial Intelligence (AI)

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

Topics & Ideas: Networking

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

Topics & Ideas: Database Systems

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

Topics & Ideas: Human-Computer Interaction

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

Topics & Ideas: Information Security

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

Topics & Ideas: Software Engineering

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

CompSci & IT Dissertations/Theses

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

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

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

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

Fast-Track Your Research Topic

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

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

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Information Technology -MSc program

It’s really interesting but how can I have access to the materials to guide me through my work?

That’s my problem also.

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

BLOCKCHAIN TECHNOLOGY

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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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Computer Science Ph.D. Program

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

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

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

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

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

FAQ with more information about the two campuses .

Ph.D. Program Structure

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

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

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

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

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

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

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

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Phd program, find your passion for research.

Duke Computer Science gives incoming students an opportunity to investigate a range of topics, research problems, and research groups before committing to an advisor in the first year. Funding from the department and Duke makes it possible to attend group meetings, seminars, classes and colloquia. Students may work on multiple problems simultaneously while finding the topic that will motivate them through their first project. Sharing this time of learning and investigation with others in the cohort helps create lasting collaborators and friends.

Write a research proposal the first year and finish the research the second under the supervision of the chosen advisor and committee; present the research results to the committee and peers. Many students turn their RIP work into a conference paper and travel to present it.

Course work requirements are written to support the department's research philosophy. Pass up to four of the required six courses in the first two years to give time and space for immersing oneself in the chosen area.

Years three through five continue as the students go deeper and deeper into a research area and their intellectual community broadens to include collaborators from around the world. Starting in year three, the advisor funds the student's work, usually through research grants. The Preliminary exam that year is the opportunity for the student to present their research to date, to share work done by others on the topic, and to get feedback and direction for the Ph.D. from the committee, other faculty, and peers.

Most Ph.D students defend in years five and six. While Duke and the department guarantee funding through the fifth year, advisors and the department work with students to continue support for work that takes longer.

Teaching is a vital part of the Ph.D. experience. Students are required to TA for two semesters, although faculty are ready to work with students who want more involvement. The Graduate School's Certificate in College Teaching offers coursework, peer review, and evaluation of a teaching portfolio for those who want to teach. In addition, the Department awards a Certificates of Distinction in Teaching for graduating PhD students who have demonstrated excellence in and commitment to teaching and mentoring.

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Computer Science, PhD

Computer science phd degree.

In the Computer Science program, you will learn both the fundamentals of computation and computation’s interaction with the world. Your work will involve a wide range of areas including theoretical computer science, artificial intelligence and machine learning, economics and computer science, privacy and security, data-management systems, intelligent interfaces, operating systems, computer graphics, computational linguistics, robotics, networks, architectures, program languages, and visualization.

You will be involved with researchers in several interdisciplinary initiatives across the University, such as the Center for Research on Computation and Society , the Data Science Initiative , and the Berkman Klein Center for Internet and Society .

Examples of projects current and past students have worked on include leveraging machine learning to solve real-world sequential decision-making problems and using artificial intelligence to help conservation and anti-poaching efforts around the world.

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Computer Science Degree

Harvard School of Engineering offers a  Doctor of Philosophy (Ph.D) degree in Computer Science , conferred through the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences. Prospective students apply through Harvard Griffin GSAS; in the online application, select “Engineering and Applied Sciences” as your program choice and select "PhD Computer Science" in the Area of Study menu.

In addition to the Ph.D. in Computer Science, the Harvard School of Engineering also offers master’s degrees in  Computational Science and Engineering as well as in Data Science which may be of interest to applicants who wish to apply directly to a master’s program.

Computer Science Career Paths

Graduates of the program have gone on to a range of careers in industry in companies like Riot Games as game director and Lead Scientist at Raytheon. Others have positions in academia at University of Pittsburgh, Columbia, and Stony Brook. More generally, common career paths for individuals with a PhD in computer science include: academic researcher/professor, industry leadership roles, industry research scientist, data scientist, entrepreneur/startup founder, product developer, and more.

Admissions & Academic Requirements

Prospective students apply through the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences (Harvard Griffin GSAS). In the online application, select  “Engineering and Applied Sciences” as your program choice and select "PhD Engineering Sciences: Electrical Engineering​." Please review the  admissions requirements and other information  before applying. Our website also provides  admissions guidance ,  program-specific requirements , and a  PhD program academic timeline . In the application for admission, select “Engineering and Applied Sciences” as your degree program choice and your degree and area of interest from the “Area of Study“ drop-down. PhD applicants must complete the Supplemental SEAS Application Form as part of the online application process.

Academic Background

Applicants typically have bachelor’s degrees in the natural sciences, mathematics, computer science, or engineering.

Standardized Tests

GRE General: Not Accepted

Computer Science Faculty & Research Areas

View a list of our computer science faculty  and  computer science affiliated research areas . Please note that faculty members listed as “Affiliates" or "Lecturers" cannot serve as the primary research advisor.

Computer Science Centers & Initiatives

View a list of the research centers & initiatives  at SEAS and the computer science faculty engagement with these entities .

Graduate Student Clubs

Graduate student clubs and organizations bring students together to share topics of mutual interest. These clubs often serve as an important adjunct to course work by sponsoring social events and lectures. Graduate student clubs are supported by the Harvard Kenneth C. Griffin School of Arts and Sciences. Explore the list of active clubs and organizations .

Funding and Scholarship

Learn more about financial support for PhD students.

• How to Apply

Learn more about how to apply  or review frequently asked questions for prospective graduate students.

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Academics   /   Graduate PhD in Computer Science

The doctor of philosophy in computer science program at Northwestern University primarily prepares students to become expert independent researchers. PhD students conduct original transformational research in extant and emerging computer science topics. Students work alongside top researchers to advance the core CS fields from Theory to AI and Systems and Networking . In addition, PhD students have the opportunity to collaborate with CS+X faculty who are jointly appointed between CS and disciplines including business, law, economics, journalism, and medicine.

Joining a Track

Doctor of philosophy in computer science students follow the course requirements, qualifying exam structure, and thesis process specific to one of five tracks :

• Artificial Intelligence and Machine Learning
• Computer Engineering

Within each track, students explore many areas of interest, including programming languages , security and privacy and human-computer interaction .

Learn more about computer science research areas

Curriculum and Requirements

The focus of the CS PhD program is learning how to do research by doing research, and students are expected to spend at least 50% of their time on research. Students complete ten graduate curriculum requirements (including COMP_SCI 496: Introduction to Graduate Studies in Computer Science ), and additional course selection is tailored based on individual experience, research track, and interests. Students must also successfully complete a qualifying exam to be admitted to candidacy.

CS PhD Manual Apply now

Request More Information

Download a PDF program guide about your program of interest and get in contact with our graduate admissions staff.

Request info about the PhD degree

Opportunities for PhD Students

Cognitive science certificate.

Computer science PhD students may earn a specialization in cognitive science by taking six cognitive science courses. In addition to broadening a student’s area of study and improving their resume, students attend cognitive science events and lectures, they can receive conference travel support, and they are exposed to cross-disciplinary exchanges.

The Crown Family Graduate Internship Program

PhD candidates may elect to participate in the Crown Family Graduate Internship Program. This opportunity allows the doctoral candidate to gain practical experience in industry or in national research laboratories in areas closely related to their research.

Management for Scientists and Engineers Certificate Program

The certificate program — jointly offered by The Graduate School and Kellogg School of Management — provides post-candidacy doctoral students with a basic understanding of strategy, finance, risk and uncertainty, marketing, accounting and leadership. Students are introduced to business concepts and specific frameworks for effective management relevant to both for-profit and nonprofit sectors.

Career Paths

Recent graduates of the computer science PhD program are pursuing careers in industry & research labs, academia, and startups.

• Georgia Institute of Technology
• Illinois Institute of Technology
• Northeastern
• University of Pittsburgh
• University of Rochester
• University of Washington
• Naval Research Laboratory
• Northwestern University

Industry & Research Labs

• Adobe Research
• Narrative Science
• Oak Ridge National Laboratory

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What Students Are Saying

"One great benefit of Northwestern is the collaborative effort of the CS department that enabled me to work on projects involving multiple faculty, each with their own diverse set of expertise.

Northwestern maintains a great balance: you will work on leading research at a top-tier institution, and you won't get lost in the mix."

— Brian Suchy, PhD Candidate, Computer Systems

What Alumni Are Saying

"In the early stage of my PhD program, I took several courses from the Department of Economics and the Kellogg School of Management and, later, I started collaborating with researchers in those areas. The experience taught me how to have an open mind to embrace and work with people with different backgrounds."

— Yiding Feng (PhD '21), postdoctoral researcher, Microsoft Research Lab – New England

Read an alumni profile of Yiding Feng

"My work at IBM Research involves bringing together symbolic and deep learning techniques to solve problems in interpretable, effective ways, which means I must draw upon the research I did at Northwestern quite frequently."

— Maxwell Crouse (PhD '21), AI Research Scientist, IBM Research

Read an alumni profile of Maxwell Crouse

The theory group here is very warm and close-knit. Starting a PhD is daunting, and it is comforting to have a community I can lean on.

— Vaidehi Srinivas, PhD Candidate, CS Theory

Potential PhD projects

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

Artificial intelligence

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

• Networked systems

Service oriented computing

Software engineering and software security, trustworthy systems.

Supervisory team : Professor Claude Sammut 

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

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

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

A scholarship/stipend may be available. 

For more information contact:  Prof. Claude Sammut

Supervisory team : Wenjie Zhang, Dong Wen, Xiaoyang Wang

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

A scholarship/stipend may be available.

For more information contact: [email protected]          

Supervisory team : Dr Raymond Louie

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

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

For more information contact:  Dr. Raymond Louie

Supervisory team: Dr. Aditya Joshi

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

For more information, contact [email protected] .

Supervisory team: Wenjie Zhang, Dong Wen, Xiaoyang Wang

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

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

Supervisory team: Dr Sasha Vassar

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

For more information contact: [email protected]

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

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

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

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

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

For more information contact:  Dr. Gelareh Mohammadi

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

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

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

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

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

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

Supervisory team: Dr Raymond Louie, Dr Sara Ballouz

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

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

Supervisory team:  Dr Yang Song

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

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

For more information contact:  Dr Yang Song

Supervisor team:  Professor Erik Meijering and Dr John Lock

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

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

Supervisory team:  Dong Wen, Wenjie Zhang

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

For more information contact: [email protected]

Supervisory team:  Sri Parameswaran 

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

For more information contact:  [email protected]

Human-Centred computing

Supervisory team: Dr Gelareh Mohammadi ,  Prof. Wenjie Zhang

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

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

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

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

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

Networked systems and security

Supervisory team:  Sanjay Jha, Salil Kanhere 

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

For more information contact:  [email protected]

Supervisory team:  Mahbub Hanssan 

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

For more information contact:  [email protected]   

Supervisory team:  Wen Hu  

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

For more information contact:  [email protected]

Supervisory team:  Boualem Benatallah, Lina Yao, Fabio Casati

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

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

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

Supervisory team:  Helen Paik

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

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

For more information contact:  [email protected]

Supervisory team: Fethi Rabhi

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

For more information contact [email protected]

Supervisory team: A/Prof. Yulei Sui

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

For more information contact: [email protected]

Supervisory team:  Gernot Heiser

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

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

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

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

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

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

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

Eligibility: Domestic Candidates only, PhD only

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

Theoretical computer science

Supervisory team:  Ron van der Meyden 

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

For more information contact:  [email protected]   

Projects with top up scholarship for domestic students

Supervisors:

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

Project description:

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

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

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

Supervisor:  Dr Rahat Masood ( [email protected] )

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

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

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

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

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

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

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

Supervision team

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

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

Research problems

• Dynamic threat risk/exposure score

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

• Customised/targeted newsfeed

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

Proposed approaches

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

We have 912 Computer Science PhD Research Projects PhD Projects, Programmes & Scholarships

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Computer Science PhD Research Projects PhD Projects, Programmes & Scholarships

Computer science: fully funded epsrc dtp phd scholarship: vertical multi-purpose farming robotic system, phd research project.

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

Funded PhD Project (UK Students Only)

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

Quality of Experience (QoE) in Object-Based Media

Phd on the quantification of the impact of natural variability and possible volcanic futures on climate projections across the irish and british isles, funded phd project (students worldwide).

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

Large Language Models for Biologics Manufacturing Challenges

Phd informatics scholarship: applied machine learning for chronic pain physical rehabilitation, a machine learning enhanced digital twin toward sustainable pharmaceutical tablet manufacturing, competition funded phd project (students worldwide).

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

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

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

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

The Architecture of Future Healthcare Environments

Self-funded phd students only.

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

Investigating Opportunities and Challenges of Crowdsourcing for Sustainable Energy Solutions in Conflict-Affected Areas

Using system-wide integrated data to investigate inequities in patient safety, automated analysis of qualitative data using ai for patient safety, phd in engineering - life cycle assessment of new semiconductor technologies, cognitive science phd, color science ph.d..

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Latest PhD Topics in Computer Science

Computer science is denoted as the study based on computer technology about both the software and hardware. In addition, computer science includes various fields with the fundamental skills that are appropriate and that are functional over the recent technologies and the interconnected world. We guide research scholars to design latest phd topics in computer science.

Introduction to Computer Science

In general, the computer science field is categorized into a range of sub-disciplines and developed disciplines . The computer science field has the extension of some notable areas such as.

• Scientific computing
• Software system
• Hardware system
• Computer Theory

We have an updated technical team to provide novel research ideas with the appropriate theorems, proofs, source code, and data about tools. So, the research scholars can communicate with our research experts in computer science for your requirements. Now, let us discuss the significant research areas that are used to select the latest PhD topics in computer science in the following.

Research Area in Computer Science

• Internet-based mobile ad hoc network (iMANET)
• Smartphone ad hoc network (SPANET)
• Mobile cloud computing
• Soft computing
• Context-aware computing
• Systems and cybernetics
• Learning technologies
• Internet computing
• Information forensics and security
• Dependable and secure computing
• Brain-computer interface
• Audio and language processing
• Wireless sensor networks
• Wireless body area network
• Visual cryptography
• Video streaming
• Vehicular network
• Ad hoc network
• Text mining
• Telecommunication engineering
• Software-defined networking
• Software reengineering
• Service computing (web service)
• Social sensor networks
• Network security and routing
• Cloud computing
• Computer vision and image processing
• Bioinformatics and biotechnology
• Big data and databases
• Cyber security
• Natural language processing
• Embedded systems
• Human-computer interaction
• Networks and security

Frequently, all the research areas in computer science are quite innovative. In addition, we focus on innovative computer science projects and examine all the sections of research works through the models, techniques, algorithms, mechanisms , etc. Now, it’s time to pay equal attention to the consequence of research protocols. So, let us take a glance over the notable protocols that are used in computer science-based projects along with their specifications.

Protocols in Computer Science

• Ad hoc on-demand distance vector is abbreviated as AODV and it is based on the loop-free routing protocol for the ad hoc networks. It is created for the self-starting environment with the mobile nodes along with various network features that include packet loss, link failure, and node mobility
• It is denoted as the reactive and proactive routing protocol in which the routes are revealed as per the necessity
• Dynamic source routing abbreviated as DSR is one of the routing protocols that is used for the functions of wireless mesh networks and it is parallel to the AODV in transmitting the node requests

The above-mentioned are the substantial research protocols along with their descriptions . Thus, you can just contact us to get the finest and latest PhD topics in computer science. Our research experts can help you in all aspects of your research. Now, you can refer to the following to know about the research trends in computer science.

Current Trends in Computer Science

• It is deployed in the process of detecting and segregating the zombie attack based on cloud computing
• Stenography technique is applied in the cloud computing process to develop the security in cloud data
• In the network process, the reduction of fault occurs through the enhancement of green cloud computing
• In cloud computing, the issues are based on load balancing through the usage of a weight-based scheme
• Homomorphic encryption is developed for key sharing and management
• It is deployed in the cloud computing to segregate the virtual side-channel attack
• It is used to develop the cloud data security and watermarking technique in the cloud computing

The following is about the guidelines for research scholars to prepare the finest research work provided by our experienced research professionals.

How to do Good Research in Computer Science?

• Initially, select the research area that you are interested in computer science
• After selecting an area, the researcher has to find an innovative research topic in computer science
• Select good ideas to enhance the state of art
• The real-time implementations are applied
• Possessions based on the selected approach have to be proved and that should be the enhancement of the existing process
• Software tools have to be developed to support the system
• Have to describe the systematic comparison with the other approaches which has the same issue and discuss the advantages and disadvantages of the research notion
• Results based on some research papers have to be accessible

Applications in Computer Science

Manet is deployed to identify some applications in the research areas that are highlighted in the following.

• Detecting the selective forwarding attack in the mobile as hoc networks
• Avoidance of congestion in the mobile ad hoc networks
• It is used in the trust and security-based mechanism of wormhole attack isolation based on Manet
• Scheme is evaluated with the recovery of mobile as hoc network
• Road safety
• Vehicular ad hoc communication
• Environment sensors

The following is the list of research applications in the field of image processing .

• Video processing
• Pattern recognition
• Color processing
• Robot vision
• Encoding and transmission
• Medical field
• Gamma-rayay imaging

In addition, we have highlighted some applications that are related to the bioinformatics research field.

• Modeling and simulation based on proteins, RNA, and DNA are created through tools based on bioinformatics
• It is used to compare the genetic data along with the assistance of bioinformatics tools
• It is deployed in the study of various aspects including protein regulation and expression
• Organization of biological data and text mining has a significant phase in the process
• It is used in the field of genetics for the mutation observation

More than above, the utmost research applications are available in real-time. In overall, it increases the inclusive efficiency in all aspects of the research features. In addition, our research experts have listed down the prominent research topics based on computer science.

• Network and security
• Distributed system
• High-performance computing
• Visualization and graphics
• Geographical information system
• Databases and data mining
• Architectures and compiler optimization

List of Few Latest and Trending Research Topics in Big Data

• The parallel multi-classification algorithm for big data using the extreme learning machine
• Disease prediction through machine learning through big data from the healthcare communities
• Nearest neighbor classification for high-speed big data streams using spark
• Privacy preserving big data publishing: A scalable k-anonymization approach using MapReduce
• Efficient and rapid machine learning algorithms for big data and dynamic varying systems

Software Engineering-Based Topics in Computer Science

• It is used to support team awareness and collaboration, distributed software development, open source communities, and software as the service
• Software modeling and reasoning
• The reasoning and modeling based on software along with the reasoning specifications in security and safety, analysis of model-driven software development, analysis of requirements modifications, and product timeline
• Dependencies of stakeholders
• Enterprise contexts
• Modeling and analysis of software requirements

Latest Computer Networking Topics for Research

• Data security in the local network through the distributed firewalls
• Efficient peer-to-peer keyword searching
• Tolerant routing on mobile ad hoc network
• Hybrid global-local indexing for efficient peer-to-peer information retrieval
• Application of genetic algorithms in network routing
• Bluetooth-based smart sensor networks
• ISO layering model
• Distributed processing and networks
• Delay tolerant network
• Wireless intelligent networking
• Network security and cryptography

The abovementioned are the contemporary and topical research topics based on the computer science research field. In addition, the research experts have highlighted the latest phd topics in computer science domain detailed in the following.

Area-Based Topics Process

• Human-robot interaction
• Digital fabrication
• Critical computing
• UI technologies
• Information visualization
• Information and communication technology and development (ICTD)
• Computer-supported cooperative work
• Computer-supported cooperative learning
• Augmented and virtual reality
• Shape modeling
• Geometry processing
• Computational imaging
• Computing fabrication
• Translating computational tools
• NLP and speech for healthcare and medicine
• Satisfiability in reasoning
• Sequential decision making
• Multi-agentnt system
• Cognitive robotics
• Knowledge representation
• Human motion analysis
• Computational photography
• Object recognition
• Physics-based modeling of shape and appearance
• Cognitive modeling of language acquisition and processing
• Applications of NLP in healthcare and medicine
• Formal perspectives on language
• Applications of NLP in social sciences and humanities
• Machine translation
• Speech processing

Now, let’s have a glance over the list of research tools that are used in the implementation of research in computer science.

Simulation Tools in Computer Science

For your information, our technical professionals from computer science backgrounds have given you some foremost research questions with answers, to what the researchers are looking for.

Research Questions Computer Science

How to implement ad hoc routing protocols using omnet++.

Oment++ environment is implemented through the adaptations and it is enabling for the contrast simulation results with the designs of the Manet application. The routing protocols such as DSR and AODV are used in the process and as the open source code.

How is Hadoop used in big data?

In general, Hadoop is considered as the java and open source framework that is deployed in the process of big data storing. Mapreduce programming model is deployed in Hadoop for the speed process of data storage.

What are the trending technologies in computer science?

• Artificial intelligence (AI)
• Everything as a service
• Human augmentation
• Big data analytics
• Intelligent process automation (IPA)
• Internet of behaviors (IoB)
• 5G technology

What are the major areas in the field of computer science?

• Theory of computing
• Bioinformatics
• Software engineering
• Programming languages
• Numerical analysis
• Vision and Graphics
• Human-computerer interaction
• Database systems
• Computer systems and network security

How to implement artificial intelligence in python?

Generally, this process includes four significant steps and they are highlighted in the following.

• Organizational and AI capabilities that are essential for digital transformation are apprehended
• Business ecosystem role, the potential for BMI, and current BM are comprehended
• Capabilities are enhanced and cultivated for the AI execution
• Internal is developed and organizational acceptance is reached
• Tensor flow

Taking everything into account, the research scholars can grasp any innovative and latest PhD topics in computer science from our research experts. Consequently, we guide research scholars in all stages. In the same way, we make discussions with you at all stages of the research work. So, scholars can closely track the research work from everywhere in the world. Additionally, our well-experienced research professionals will provide significant assistance throughout your research process.

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Tips to Become a Better (Computer Science) Ph.D. Student

Why does the world need another blog post.

There are already a lot of great blogs posts about the computer science Ph.D. experience, each approaching it from a different angle (the whole process of a Ph.D., how to choose your research topic, etc.). However, the ideas presented in most of these blog post come from the experience of one person while this blog is a condensed summary of in-depth talks with more than five professors and three Ph.D. student during the YArch workshop at HPCA’19. During these conversations, we discussed topics that are important for early year computer science Ph.D. students . We chose ten ideas we found most impactful to us, and explain five of them in detail and present the other five as short tips.

Research > Courses

Be professional, read a lot and read broadly, impact humankind, don’t give up on your research topic easily, aim for top-tier conferences.

• Use existing resources in your groups

You are powerful!

Focus on publishing.

If you have more ideas, please comment at the bottom of this post!

Other amazing blogs out there:

• The Ph.D. Grind
• Tips: How to Do Research
• So long, and thanks for the Ph.D.!
• Graduate School Survival Guide
• Tips for a New Computer Architecture PhD Student

Young Ph.D. students tend to spend too much time on courses. However, research outweighs courses.

Take courses with a grain of salt

Courses are not as important as they seem to be. The priority of a Ph.D. student is to do research – the earlier you start your research, the better off you’ll be in the long run.

However, don’t go to extremes ! A poor grade can also be a huge problem. You should always be familiar with the requirement of qualification exams or generals and meet all the standards about the courses.

Remember the main ideas of courses

Trapping ourselves in trivial details of a course is easy. However, most of the specifics are not important to our research even if the topic is related to our area.

A good approach is to use what you’ve learned from one course and apply it to a different field (e.g., taking an analysis tool from a compiler course and applying it in computer networks).

Treat your Ph.D. as a job. You get paid (albeit not much) for being a Ph.D. candidate, so make your work worth the money. This professional mindset should also be apparent to your advisor. Some advisors take on a more hands-off approach, for instance letting you work from home, but this is no reason for slacking; you should be responsible for your research schedule, such as reminding your advisor of plans from previous group meetings. Your status is not that of a student but rather that of a peer in the research community.

Though it can be very daunting starting out, reading papers is an essential part of the Ph.D. life. Previously, you may have read papers when it was necessary for a class or a project. However, you should put reading papers in your daily routine. Doing so allows you to draw inspiration from a sea of knowledge and prevents yourself from reinventing the wheel. Besides, it’s a great way to be productive on a slow day.

Make a plan to read

When scheduling your day, assign one period just for reading papers. You can read one paper in depth or compare several papers; regardless of your choice, allotting time to this task is the key.

Read broadly

Reading papers from different subfields of computer science is a great way to learn the jargon, the method, and the mindset of researchers in each field. This can be the first step towards discovering opportunities for collaboration.

It is not uncommon for a Ph.D. student to spend several years building a system that turns out to be fundamentally flawed or not as applicable as expected. Don’t worry! There is nothing wrong with failing, and perhaps we should even expect failure to be part of the journey. But we should aim to fail early in order to have time to work on another project (and graduate!).

Perform a limit study

Perform a quick limit study before sticking with a project. A limit study includes in-depth analyses of implicit assumptions we make when coming up with an idea, a related works search, and the potential of the work if everything goes well. A great limit study can itself be a publishable paper. An example can be found here .

Hacky implementation can be useful

Being a researcher, your work is to develop proof-of-concepts. Nevertheless, you need to demonstrate that your concept is sound for the simplest of cases before continuing to the full-blown system. Hack in the minimum set to show that your idea is possible while resisting the temptation to build a robust infrastructure – if your idea fails, you will know to stop earlier.

Impacting humankind may sound too ambitious, but it should be the ultimate reason why we embark on this journey.

Choose an impactful research topic

In terms of how our Ph.D. research could impact human knowledge, I would like to refer to The Illustrated Guide to a Ph.D. by Matt Might. All we will do in five years is pushing the boundary of human knowledge by a minute margin. Choose a topic that you are able to contribute to, feel passionate about, and can explain the importance of to a layman in a 3-min talk.

Check out why Matt Might changed his research focus from programming languages to precise medicine.

How can our research actually impact people from other fields?

A survey paper by the Liberty Research Group sheds light on how the improvement of programming tools impacts ( computational scientists ) all scientists. Thinking about how your research affects people from other fields can help you define the scope of your contribution.

At some point, we will get bored with our research topic and find something else interesting. Think twice before switching topics. You must differentiate between your project heading nowhere and you getting tired of being stuck.

You should focus on publishing at only top-tier conferences. Don’t consider second-tier venues unless the work has been rejected several times by top-tier conferences. This can prevent you from doing incremental work to make your publication list look better.

Use existing resources in your group

For many fields in computer science, a mature infrastructure requires several years of development by multiple graduate students. Think about how to make use of the infrastructure and resources in the group to boost your research progress.

Even though we are just junior graduate students, we can have a massive impact on ourselves, our group, and even our department. For example, if there is no reading group for your field in your department, start one!

Needless to say, publications are essential since those are what people look at once we graduate.

Acknowledgment

All the ideas in this blog originate from the talks with mentors of the YArch’19 workshop. Thanks to Prof. Boris Grot from the University of Edinburgh, Prof. Thomas Wenisch from the University of Michigan, Prof. Vijay Janapa Reddi from Harvard University, Prof. Luis Ceze from the University of Washington, and Prof. Kevin Skadron from the University of Virginia.

Thanks to two chairs of the YArch’19 workshop, Shaizeen Aga from AMD Research and Prof. Aasheesh Kolli from Pennsylvania State University, for making this possible.

Greg Chan and Bhargav Godala from the Liberty Research Group were at most of these talks and helped me write down some ideas.

6th year Ph.D. student @ Liberty Research Group, Princeton University

Graduated Master @ Liberty Research Group, Princeton University

Email forwarding for @cs.stanford.edu is changing. Updates and details here .

Academics | PhD Program

Main navigation.

The PhD degree is intended primarily for students who desire a career in research, advanced development, or teaching. A broad Computer Science, Engineering, Science background, intensive study, and research experience in a specialized area are the necessary requisites.

The degree of Doctor of Philosophy (PhD) is conferred on candidates who have demonstrated to the satisfaction of our Department in the following areas:

• high attainment in a particular field of knowledge, and
• the ability to do independent investigation and present the results of such research.

They must satisfy the general requirements for advanced degrees, and the program requirements specified by our Department.

Program Requirements

On average, the program is completed in five to six years, depending on the student’s research and progress.

Progress Guidelines

Students should consider the progress guidelines to ensure that they are making reasonable progress.

Monitoring Progress

Annual reviews only apply to PhD students in their second year or later; yearly meetings are held for all PhD students.

PhD Assistance

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

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

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

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

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

Computational complexity

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

Polynomial versus non-polynomial time for specific algorithmic problems

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

Algorithmic problems

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

Natural Language Processing algorithms

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

Programming language theory

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

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

Conclusion:  

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

Example Research Topics in Technology and Computer Science    

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

About PhD Assistance  

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

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

Your passion for an area of research

Appositeness of the topic

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

Providing a solution to a practical problem.

Topic selection help for computer science students  

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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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The PDF includes all information on this page and its related tabs. Subject (course) information includes any changes approved for the current academic year.

Research guidance, Research Journals, Top Universities

Ph.D. Topics in Computer Science

While there are many topics, you should choose the research topic according to your personal interest. However, the topic should also be chosen on market demand. The topic must address the common people’s problems.

In this blog post, we are listing important and popular Ph.D. (Research) topics in Computer Science .

PhD in Computer Science 2023: Admission, Eligibility

Page Contents

The hottest topics in computer science

• Artificial Intelligence.
• Machine Learning Algorithms.
• Deep Learning.
• Computer Vision.
• Natural Language Processing.
• Blockchain.
• Various applications of ML range: Healthcare, Urban Transportation, Smart Environments, Social Networks, etc.
• Autonomous systems.
• Data Privacy and Security.
• Lightweight and Battery efficient Communication Protocols.
• Sensor Networks
• 5G and its protocols.
• Quantum Computing.
• Cryptography.

Cybersecurity

• Bioinformatics/Biotechnology
• Computer Vision/Image Processing
• Cloud Computing

Other good research topics for Ph.D. in computer science

Bioinformatics.

• Modeling Biological systems.
• Analysis of protein expressions.
• computational evolutionary biology.
• Genome annotation.
• sequence Analysis.

Internet of things

• adaptive systems and model at runtime.
• machine-to-machine communications and IoT.
• Routing and control protocols.
• 5G Network and internet of things.
• Body sensors networks, smart portable devices.

Cloud computing

• How to negotiate service level platform.
• backup options for the cloud.
• Secure data management, within and across data centers.
• Cloud access control and key management.
• secure computation outsourcing.
• most enormous data breach in the 21st century.
• understanding authorization infrastructures.
• cybersecurity while downloading files.
• social engineering and its importance.
• Big data adoption and analytics of a cloud computing platform.
• Identify fake news in real-time.
• neural machine translation to the local language.
• lightweight big data analytics as a service.
• automated deployment of spark clusters.

Machine learning

• The classification technique for face spoof detection in an artificial neural network.
• Neuromorphic computing computer vision.
• online fraud detection.
• the purpose technique for prediction analysis in data mining.
• virtual personal assistant’s predictions.

More posts to read :

• How to start a Ph.D. research program in India?
• Best tools, and websites for Ph.D. students/ researchers/ graduates
• Ph.D. Six-Month Progress Report Sample/ Format
• UGC guidelines for Ph.D. thesis submission 2021

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PhD Topics in Computer Science for Real-World Applications

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

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

1 . AI-Powered Healthcare Diagnostics:

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

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

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

2. Sustainable Smart Cities:

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

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

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

3. Cybersecurity for Critical Infrastructures :

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

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

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

4. Autonomous Systems for Disaster Response:

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

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

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

5. Natural Language Processing for Multilingual Communication :

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Final Thoughts

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

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• Green cloud computing
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Computer Science Research Topics for PhD is a full research team to discover your work. It is a desire for the up-and-coming scholars to attain the best. Without a doubt, you can know the depth of your work.To fix this issue, we bring our Computer science research topics for PhD services.

In computer science, we will explore 145+ areas and 100000+ topics in the current trend. Seeing that, research topic selection is not the long term process for PhD students. On this page, we will offer you the latest topics in computer science. It is more useful for you in the topic selection process.

Computer science research topics for PhD

• Software-defined cloud computing
• Virtualized cloud environment
• Multi-dimensional, multi-resolution imaging techniques
• Virtual and augmented reality
• Content-based internet computing
• Novel biometrics methods
• Cloud RAN, Fog RAN, Edge RAN designs

Earlier topics afford merely for your reference. To know more or get the topics, you simply email us at our business time. With our support, more than 5000+ scholars have achieved their goal promptly!!!

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All these problems will not impact your research when you are under our service, so that you can feel free to clear all your doubts directly with our experts online/offline.

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Inbox us your intent domain to get your topics index, Get you within a working day from Computer science research topics for PhD . On the whole, your aim without a plan is just a wish. Your strategy without execution is just an idea. Your execution without us is just an end, but not a feat.

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We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

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How should someone choose a PhD topic so that they don't fail?

One of my close relatives went to Australia to get a Ph.D. in computer science. PhDs in Australia are 3 years long. She couldn't complete her Ph.D. in 3 years. Then she applied for an extension and got 1 year more. However, ultimately she failed. I asked her about the issue and she preferred to stay silent.

As far as I guess, she chose a topic that was destined for failure. I.e. her hypothesis was incorrect.

How should someone choose a Ph.D. topic so that they don't fail?

• computer-science

• 90 Failing the PhD because the topic was destined to failure is an advisor's failure. –  Massimo Ortolano Commented Oct 11, 2020 at 7:55
• 52 A failing hypothesis is not an ultimate reason for a Phd failure. Sure, papers on a hypothesis that proves to be false are harder to publish, but a Phds purpose is to certify that you can do sound scientific research. Proving that something plausible is not true is part of that. So while a failing hypothesis can be a hindrance it is not sufficient to cause failure in a Phd. –  Frank Hopkins Commented Oct 11, 2020 at 15:39
• 45 If you could predict "success" in "proving" an hypothesis three years in advance, you wouldn't be doing research. Research is exploring the unknown, not giving reasons for things known to be true. Alternatively, you would be doing some trivial exercise, going through motions to no useful end. –  Buffy Commented Oct 11, 2020 at 15:45
• 11 I'm not sure if any of my Aussie PhD friends finished within 3 years, and even taking longer than 4 years is very common. Did they actually fail, or have they just run out of funding? Do they need more funding? Is it now just time to finish writing up what they did? –  curiousdannii Commented Oct 12, 2020 at 4:52
• 37 This whole question seems very strange — the asker’s assumptions are wrong, and their motivation is unclear. It’s like asking “My friend got married, but then got divorced. I presume this is because her spouse was a bad cook. How do you make sure that your spouse is a good cook?” Sure, we can answer the specific question — “Have them cook for you earlier in the relationship.” — but is that really what you wanted to know, or was it “how can I make sure that my marriage will be good”, or “how can I tell my friend what she should have done differently”, or something else? –  PLL Commented Oct 12, 2020 at 12:00

9 Answers 9

Make sure your advisor has a good track record of graduating students in time.

Anyone just entering or outside the field won't be able to assess PhD topics with good judgement, so it's unfair when advisors fail their students by giving them bad projects. Your best bet at avoiding this is finding an advisor who is unlikely to fail students in this way.

If you find yourself in this position, a good bet is to reach out to other professors and tell them what's going on. It can feel shameful, but I've seen many success stories of people getting a new project and spinning it into enough for a PhD when things aren't working out with their initial advisor.

• 2 we do not know that the cases of the OP was that the project was bad. –  lalala Commented Oct 11, 2020 at 15:22
• 9 @lalala OP literally asked the question of how to avoid choosing a bad project. I never weighed in on the hypothesis about OPs relative, I just answered the question OP posed. –  Well... Commented Oct 12, 2020 at 5:59
• 4 This is it, choose your supervisor not your subject. A good research subject will be trashed by a poor advisor, and in any case your approach will be completely dominated by them. Look at what happened to their previous PhDs including their initial years post-doctorate, because they too are typically dominated by the PhD and supervisor. Younger supervisors are more cutting edge, driven and empathetic (by temporal proximity) to the PhD's situation. Older supervisors are more experienced and connected/senior in their field. Either kind may be abusive or destructive because academia allows though –  benxyzzy Commented Oct 13, 2020 at 6:30
• 1 Yes, the advisor's track record is the dominant feature. Sure, other things have some effect... but nothing else as much as this. –  paul garrett Commented Oct 13, 2020 at 17:42
• This is by far the best advise. That said, it's in practice sometimes hard to follow since the number of drop-outs is usually not easily accessible. I know people that graduate lots of students, some of the excellent, but I still would strongly advise against starting a PhD with them since they also have a really high dropout rate (i.e., the shotgun approach of research supervision). –  xLeitix Commented Aug 15, 2022 at 7:52

A PhD is awarded following submission of a thesis. It is extremely rare for a student who submits a thesis to fail. It is quite common for a student to never submit a thesis.

If the goal is simply to pass, then the key questions should be:

• What are the expectations for a thesis in my discipline? Expectations vary, but usually originality is expected.
• Will this thesis topic allow me to meet those expectations?

The one situation where a choice of topic would be likely to directly cause failure would be if the topic is blatantly not original. For example, it is found in well-known textbooks. It is much more common for a student to stop working on their thesis because they do not like the topic.

Financial and health factors are common causes of PhD non-completion.

• 3 usually originality is expected? There are exceptions to that? 8-( –  einpoklum Commented Oct 12, 2020 at 8:12
• 1 There are absolutely thesis topics with little failure potential. These are usually also recognizable from afar for anyone in the scientific community, so they are mostly useful if you need an academic title for political reasons but have no other academic interest. –  Simon Richter Commented Oct 12, 2020 at 17:00

Speaking as someone currently in the trenches, I’d advise the following general strategies for a doctoral student to maximize their chance for completion. At the very least, all these points should be considered. Also, as others have said, you won't fail a dissertation for having a hypothesis that yields a negative result – a dissertation is very much about the process not the scientific result per se.

• Develop your dissertation to play largely to your strengths, not address your weaknesses. For example, if you’re really strong at biological research but have only just learnt to code, it might not be a good idea to have a dissertation that is centered on building a software platform – even if it does target biological research as its domain.
• Choose a topic for which you’ll have expert guidance. That means your advisor and members of your committee can understand the concepts, methodology, and novelty of your work. Their advice will also be that much more helpful; they’ll be better equipped to help you navigate the roadblocks that’ll inevitably crop up.
• Do the background to make sure you’re addressing a real gap in the current literature. It pays to be a bit future thinking and aspirational; as a PhD student, one of the advantages you have is a multi-year timeframe where you can largely focus on one thing. Don't be afraid to think big and then narrow down your focus – doing so can help give you a larger sense of purpose; it can help you remember how the little thing you're working on in the moment factors into your larger vision.
• Discretize and make independent the goals of your project. This can be tough to do but is well worth it. Having each goal build on the other can amplify the risk that your entire project fails. For example, this could happen if you hit an insurmountable roadblock well past the timeframe where you can reasonably pivot your research direction. As a bonus, this strategy can also improve the odds that one of your research projects will have a meaningful impact.
• Be wary of situations and research designs that will precipitate bureaucratic delays. IRBs, data access committees, awaiting approval from distant stakeholders (timezone delays can add up!), and long duration data generation are examples of this. If at all possible, design your project to at least have a primary endpoint that won’t require more than one of these. Note that not all of these potential roadblocks are created equal. In my experience, the order of the above delays looks something like this: Long duration data generation > IRB > data access committees > distant stakeholders.
• Document communications and decisions with your committee and administration in writing. For example, when seeking input on a larger project decision from your committee members via email, be sure to state (in a friendly way) when you need a response by and the default action that will occur if no response is received by that date. Send a friendly reminder 48 hours before the date if you haven't received a response. For big decisions and reviews, allow your committee 2 weeks of lead time.
• Have an insurance policy. This is something I often setup before making a big career decision – ultimately, failure is always a possibility. What I mean by this is to have something to fallback on if your primary focus (i.e. your doctorate) ends in failure. As an example, I completed an MS prior to pursuing a doctorate and have a software side project and associated business plan that I believe are together legitimately valuable and actionable – at the very least, both would help me land a job that I would enjoy and keep me stable. Having 'insurance' can help give you peace of mind and sustained focus when pursuing something that might be inherently risky, and in some respects doctoral degrees are.

This isn't an exhaustive list; there are other considerations as discussed in other answers. That said, in my personal experience (and observing others at my institution) I’d recommend being mindful and discussing all of these aspects of your dissertation, possibly throughout your doctoral research, with your advisor and/or committee, though the latter may be best discussed with your peers.

• 6 "Go big" is a very dangerous suggestion -- it's very easy to overdo it and end up with something that is not realistically doable on a ph.d. timeframe. Again having the guidance of an advisor is of course crucial. –  Denis Nardin Commented Oct 11, 2020 at 17:43
• 4 "Scooped" isn't really relevant for a thesis. For a paper yes, but no committee would fail you because someone published something similar or the same. The point is you put in the work, got the result, and wrote it up in good faith. –  Azor Ahai -him- Commented Oct 11, 2020 at 23:07
• 1 @DenisNardin I agree – I misspoke, I've updated my answer to better communicate what I inteneded. –  Greenstick Commented Oct 12, 2020 at 0:06
• 3 True - just clarifying because the title question was about dissertations, specifically. –  Azor Ahai -him- Commented Oct 12, 2020 at 0:20
• 3 Many of these are great suggestions for research in general and could also benefit postdocs and early career professors. –  WaterMolecule Commented Oct 12, 2020 at 15:43

It is an empirical fact that the percentage of graduate students who fail to complete their PhDs is quite high.

It follows that there does not exist a simple algorithm for choosing your PhD topic that guarantees success - certainly not one that fits in the space of an academia.se answer. If it did exist, everyone would know it, and we wouldn’t see the numbers of people who start a PhD and don’t finish it that we do end up seeing.

Finishing a PhD is a matter of talent, a lot of hard work, and in some cases a bit of luck. It’s good to do some advance research on best practices for choosing an advisor and a topic, but no amount of preparation can save you the need to have some combination of those three things.

• 2 Do you have any statistics for the high rate of PhD dropout? In my experience, everyone I've known who started has completed, so that is quite surprising to me. But I do say this from the point of view of a student, rather than faculty. –  Bamboo Commented Oct 13, 2020 at 4:11
• 1 @Phill I don’t have statistics, sorry. –  Dan Romik Commented Oct 13, 2020 at 5:11
• 1 In all the grad math programs I've seen in the U.S., less than 10% of students do not complete their PhD, and most often they discover within a year or two of beginning that they don't want to do math... not that there's some obstacle otherwise. –  paul garrett Commented Oct 21, 2020 at 0:07

I want to reassure you that don't fail a PhD dissertation because your hypothesis was incorrect. If you are stressed about this on your own behalf- don't be . Your result is outside of your control.

To reassure you, null results are published all the time . For example, "The Ineffectiveness of using Generic Deep Learning approaches on Problems of Type XYZ" can be published in a great journal, so long as your readers still learn something important from your article. Here's what reviewers look for, in general:

• The methodology was sound.
• The argument that one could have expected your approach to work was clear and agreeable to your audience.
• The paper was well written and clearly outlays the conclusions and implications for the field that practitioners care about.

Having an interesting and successful thesis helps, no doubt, but it is not the sole issue here. The demands of 1,2,3 are very high.

That said, if your friend doesn't want to discuss why they did not complete their PhD, I would avoid poking at them. There are innumerable reasons why they might not have finished it, and it's best not to speculate . You may easily arrive at an incorrect conclusion. Heck, perhaps they dropped out because they got a great job offer as ABD.

As far as I guess, she chose a topic that was destined for failure. I.e. her hypothesis was incorrect. How should someone choose a Ph.D. topic so that she doesn't fail?

I think your hypothesis here might be incorrect. You could in theory write an entire PhD thesis based on an incorrect hypothesis. The entire point of the thesis would become disproving the hypothesis.

It's obviously not as satisfying as proving something is true, but it's valid science. If the original hypothesis was reasonably plausible, it means others won't have to repeat your mistakes.

And in any case, the point of a PhD is not so much to produce useful new science, as to produce a new scientist . I.e. someone who can demonstrate, through their thesis, that they understand the scientific process well enough to produce original results. It doesn't really matter, that most of the time, these original results are pretty useless! The originality is just a way to prove that the science came from them, and not someone else. It's only purpose is to demonstrate the following hypothesis: "Dr X is, indeed, a scientist"

Possible reasons for failure:

• lack of support from the advisor (a good advisor would advise how to turn that failing hypothesis into a successful thesis)
• mental breakdown of the student (it can be soul-crushing to spend so much time trying to get something to work, and failing)
• lack of time (if the people involved realise too late that "this isn't working", and lack the "narrative" skills to quickly turn that apparent failure into a success)

Note: people doing research in computer science can get a bit confused about what they are actually doing. Science is about asking questions, finding answers, and writing about them. So it can't really "not work". A negative result is still a result (unless you entire experimental set up got destroyed and your data corrupted, as long as you follow proper methods, you can't really fail) However, engineering would be about using science to produce a workable solution to a problem. Now this can very much fail. This is not what a PhD is about. But people can get misguided. Computer scientists ("I must write about computer science") who think that what they're doing is software engineering ("I must deliver working software"), are very much at risk of failing. And sometimes the way a PhD thesis is funded (e.g. industry grant) can fuel that misconception.

Note 2: re "originality", a very plausible cause of failure, is if you start your PhD on a valid original topic, but then someone else basically writes your thesis before you've finished it. This happens all the time... And is incredibly stressful/frustrating! Same problem with publishing papers. Some topics are popular, and great minds think alike... So it's really not that unusual for different people to be unknowingly working on the same hypothesis in parallel! And I honestly don't know what's the best way to avoid that situation, and to salvage your hard work, when someone else beats you to the finish line... (I guess try and publish anyway, even if originality takes a hit... E.g. introduce a small variation, etc. But all the extra testing and writing can really screw things up in term of timing, when grants are running out)

When one fails or is about to fail a Ph.D., it is worth understanding what requirements are not fulfilled. This may vary from a field to field but generally, there are four sets of requirements:

Formal criteria required by law. These are usually vague and the easiest to fulfill. They dictate the number of course points, seminars, and some generic requirements like "contribution to knowledge" etc. you have to fulfill to get a Ph.D.

Requirements by the university. These may specify the thesis format, specific courses to attend, teaching work, funding, etc.

Requirements by the community determine the level of quality that is considered good and worthy of publication by other researchers in the field.

Requirements by your supervisor. These are tricky because they are implicit. Inadvertently, you may get a very demanding or difficult supervisor, or, alternatively, you can have a very supportive one.

The exact thesis topic is largely irrelevant. As long as it broadly falls within CS (or any other study area) you are fine.

What matters is that a student knows the formal criteria. There should be quarterly/yearly evaluations and the supervisor/university should facilitate the student in attaining them.

Having publications of thesis work is a good sign that the work is of reasonable quality. Maintaining a good relationship with the supervisor help with understanding his/her expectations.

From my anecdotal and very limited experience, students fail PhDs for two reasons:

Difficult relationship with the supervisor due to misunderstood expectations, mismatch of personalities, inability to receive critical feedback, unwillingness to put in hard work, leading to..

Difficulties in publishing their results either due to preparing manuscripts taking forever or being repeatedly rejected from peer-reviewed venues. Lack of progress exacerbates #1

To conclude, the advice to anyone starting a PhD is to pick the supervisor carefully.

I am a professor, I have been on more than 20 doctoral committees. Most of the answers here are focused on, or call attention to picking a topic. IMHO - by itself this is not a good strategy.

In my experience, all dissertation decisions hang on one thing: the candidate's ability to understand how gatekeeping works. That is to say the classic error is the doc candidate who thinks they want their work to be great so they find the smartest people on campus to be on their committee. Translation: the four biggest egos in that discipline on campus are now on your committee. Good luck with that. Applying such a belief system (get the best and brightest) has the potential to inspire Intra-committee disagreements. That's risky. The worse-case output is the dissertation never gets done and it's not the candidate's fault.

IMHO if you want to create the most favorable conditions for graduating, research your potential committee chairs. 1) are they well-liked, respected? 2) research potential chair's doctoral committee history and records of how many successful/not successful dissertations 3) information interview your potential chair. 4) once chosen, ask your committee chair who should be on the committee.

The chair will likely recommend people who are agreeable with their ideas. Your committee meetings will be friendly. Don't get me wrong, you still have to find a good topic, be clever, and write well. A good advisor will steer you away from rough seas, heal weaknesses in your work, or advise strategies to keep your work relevant. IF you don't have that in your corner, you can still finish, it's just a lot more work to figure that stuff out on your own.

IMHO when it comes to topic and writing, buy or otherwise acquire a doctoral candidate or 'dissertation' handbook. Most universities have them in some form, usually found at the department level. Get one, read it, follow the guidelines laid out by your department -- and keep a journal of your committee meetings. Where you can, use the rules (and your notes) to your advantage.

The bottom line is that earning a phd requires you to pass through an institutionalized system. Such systems have rules and structures that can be learned and used to create pathways to success.

My experience on doctoral committees -- 20% of the dissertation ideas are not (and never will be) well conceived, 20% are exciting and interesting, The middle 60% are well-written -- or technically well-executed (and not so well-written), but otherwise good. Prolly 10% of candidates are rejected, and we always attempt to counsel our candidates to bail out early if we think they won't make it.

Good luck with your ambition. It's worth the effort. I was 20 years owner of a software company, now 20 years as professor.

My answer is basically for US as that's where I am from, and also where I got my Ph.D.

Advisor is key. Work with your advisor to pick an approved topic. The advisor will typically know what will work.

It is important that the PhD candidate's research have original research, but it also needs to be related to and compared to past research, so extending past research is important. For example, creating a new algorithm would obviously be original research. Finding statistical equations for existing algorithms would be extending past research.

It also helps to finish the work in a timely manner. Most PhD candidates have done enough reading, so it cannot be emphasized enough to write up the research. If it is possible to publish it or present it in a conference (these days, likely to be a virtual conference), this will also help.

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