phd projects in computer science

Potential PhD projects

Two students involved in a robotics engineering competition

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

Artificial intelligence

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

Networked systems

Service oriented computing

Software engineering and software security, trustworthy systems.

Supervisory team : Professor Claude Sammut

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

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

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

A scholarship/stipend may be available.

For more information contact: Prof. Claude Sammut

Supervisory team : Wenjie Zhang, Dong Wen, Xiaoyang Wang

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

A scholarship/stipend may be available.

For more information contact: [email protected]

Supervisory team : Dr Raymond Louie

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

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

For more information contact: Dr. Raymond Louie

Supervisory team: Dr. Aditya Joshi

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

For more information, contact [email protected] .

Supervisory team: Wenjie Zhang, Dong Wen, Xiaoyang Wang

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

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

Supervisory team: Dr Sasha Vassar

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

For more information contact: [email protected]

Eligibility Criteria:

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

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

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

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

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

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

For more information contact: Dr. Gelareh Mohammadi

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

The aim of this project is to extend the 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.

with first or upper-second-class Honours, or an equivalent qualification

Project Summary:

This research project aims to enhance biosecurity measures by developing advanced computer vision algorithms for real-time image recognition and automated bait dispensing systems. Leveraging machine learning (ML) and deep learning (DL) methodologies, we will create a robust system capable of identifying invasive species and pests in agricultural settings with high accuracy. The system will enable the real-time recognition of target organisms.

By integrating these cutting-edge technologies, the project seeks to significantly improve biosecurity and productivity in agricultural operations. Automated real-time monitoring and targeted bait dispensing will reduce the reliance on manual labour, lower the use of pesticides, and minimise crop damage, leading to higher yields and more sustainable farming practices. This innovative approach not only addresses critical biosecurity challenges but also contributes to the overall efficiency and resilience of agricultural systems.

This project will be conducted in close collaboration with Intersect Australia , a not-for-profit organisation established to enhance research productivity, across all disciplines, through the application of advanced information technology and skills.

For more information contact: Dr Gelareh Mohammadi

This research project aims to implement cutting-edge Data Operations, Machine Learning Operations as well as computer vision and AI methods for land use classification and change detection through remote sensing. Utilising satellite imagery and aerial photographs, we will develop machine learning (ML) and deep learning (DL) models to accurately classify different land use types and monitor changes over time.

This project not only advances environmental monitoring capabilities but also enhances productivity in land management and planning. By automating the analysis of vast amounts of remote sensing data, we aim to provide timely and actionable insights for policy-making and resource allocation. This will lead to more efficient land use, improved conservation efforts, and better management of natural resources. Furthermore, the project will support climate change adaptation strategies by providing detailed data on habitat shifts and ecosystem alterations, enabling more informed decision-making.

This project will be conducted in close collaboration with Intersect Australia , a not-for-profit organisation established to enhance research productivity across all disciplines through the application of advanced information technology and skills.

For more information, contact: Dr Gelareh Mohammadi

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 offer a range of services to help you

[email protected].

0091.11.4951 3011

Home / Blog

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.

CALL US : +0091.11.4951 3011

+91 9629863243

[email protected].

Output Screenshots
IEEE ANDROID PROJECTS
IEEE Cloudsim Projects
IEEE Communication Projects
IEEE Computer Science Projects
IEEE Dotnet Projects
IEEE Hadoop Projects
IEEE Java Projects
IEEE Matlab Projects
IEEE Networking Projects
Ns2 Projects code
IEEE PHD Projects
IEEE ARM Projects
IEEE GPS Projects
IEEE GSM Projects
IEEE LabView Projects
IEEE RFID Projects
IEEE Robotics Projects
IEEE VLSI Projects
IEEE Zigbee Projects
IEEE MATLAB Simulink Projects
IEEE Power Electronics Projects
PhD Guidance
PHD Computer Science Projects

PHD Computer Science Projects , Computer science is the study of computers includes data base management, data structure, and computer architecture and computer communication. We offer PhD computer science projects with advanced research in computer technology we implement IEEE transaction and other articles are selected for PhD computer science academic projects. We developed more than 100+ projects in various domains such as cloud computing, natural language processing, networking, information security, multimedia and forensics in PhD computer science projects .

image processing projects:

We ensure multimedia data in the form of image, text, audio and video format. We support PhD researchers doing research in multimedia data. We implement gross model retrieval process give input as multimedia data and get another form of data. We establish image processing projects with more focused in PhD research. We propose bio medical classification, digital image normalization and remote sensor image segmentation are essential projects in image processing. We develop image processing projects with open source tool to retrieve accurate smoothing, preprocessing and segmentation we use matlab, imagej, openCV and scilab are open source tools required in image processing PhD work.

PHD COMPUTER SCIENCE PROJECTS

Computer networking projects:

We describe computing network as input data which processed and stored in various location to enhance scalability and interoperable service we provide computing networks as grid computing, utility computing, cloud computing, and distributed computing network. We afford distributed computing network with hardware & software system include one or more processing element or storage element.

We guide and support PhD researchers to do research in distributed computing network. We attained network based and cost effective based development in PhD computing network research. We propose Cloudsim, gridsim; Peersim and Globus are simulation tool in distributed computing projects. We handle computer network security projects issues and resolved in PhD projects by our team.

Mining projects:

We describe mining is the process of extracting information from database based on user input queries.

We implement data mining; text mining and web mining are various kind of mining process in computer science. For text mining we provide Euclidian distance and clustering algorithm to retrieve accurate result.

To extract back of feature information, we need image based mining process. To group similar feature pixel, we perform k-means or other clustering algorithm. We execute some matching process to analyze similar cluster value objects in database. We implement web service projects to aid the goals which easily identify service provider location for ensure robust service to requester. We utilize weka, rapid miner and R programming tool in mining related PhD research projects.

Networking projects:

We propose NS3, NS2, Opnet, QUALNET and OMNET++ are event based simulation tools. We utilize these tools in PhD computer science projects to obtain realistic network model, performance analysis of various algorithm, protocol and evaluate QOS factor as throughput, delay & packet delivery ratio.

Computer network security projects
IEEE Wireless sensor network PROJECTS
B.TECH IEEE PROJECTS 2016
M.Tech IEEE Projects 2016
Wireless sensor network ns2 code
Cloud computing IEEE project papers
IEEE papers on computer science
FINAL YEAR PROJECT FOR CSE
CSE projects for final year
Projects for CSE final year students
IEEE xplore projects 2016 for IT
2016 IEEE projects for cse
CSE projects 2016
IEEE 2016 projects for cse
IEEE projects 2016 for cse
IEEE projects for cse 2016
Computer science engineering projects
Projects for computer science students
Computer science project topics 2015
Project for computer science
Computer engineering projects
Projects for computer science
computer science project topics
Projects for it students
IT projects for students
final year projects for CSE topics
IEEE cloud computing projects 2016
IEEE cloud projects 2016
Cloud computing projects 2016
Cloud computing IEEE projects 2016
IEEE projects on cloud computing 2016
Cloud computing projects for final year
Final year computer science projects
Software Projects for Engineering Students
Software engineering projects for students
Final year project ideas for software engineering
IEEE Projects in Networking
IEEE Project for CSE
CSE Software Project Topics
IEEE Project Topics for Computer Science
IEEE Projects for Computer Science
IEEE Software Projects
IEEE Projects for IT
Qualnet Simulation Projects
Standard NLP Projects
Qualnet Projects
Projects For CSE
Peersim Projects
Optisystem Projects
Opnet Projects
OMNET++ Projects
NS3 Projects
NS2 Projects
Network Simulator Projects
Networking Projects For Final Year Students
Multimedia Based Projects
MS Computer Engineering Projects
Mininet Projects
M.E CSE Projects
Knowledge & Data Mining Projects In Java
IEEE Projects On Cloud Computing
IEEE Projects For CSE
IEEE OMNET++ Projects
IEEE NS3 Projects
IEEE NS2 Simulation Projects
IEEE NS2 Projects
HADOOP PROJECTS for students
IEEE CSE Projects
IEEE Computer Communication Projects
Grid Computing Projects
CSE Final Year Projects
PHD CSE Projects
MATLAB Image Processing Projects
Projects On Cloudsim
Projects In Cloud Computing
Project Topics On Cloud Computing
NS2 Code For TCP
IEEE Projects On Wireless Communication
IEEE Projects On Networking
IEEE Projects On Network Security
IEEE Projects On Data Mining
IEEE Projects For Information Technology
Cloud Projects For Students
Cloud Computing Projects For cse Students
Cloud Computing Projects For Engineering Students
Cloud Computing Project For Students
Cloud Computing Based Projects
Cloud Based Projects For Engineering Students
IEEE Projects CSE
IEEE Projects
IEEE Projects ECE
IEEE Projects EEE

ScholarWorks

Home > Engineering > Computer Science > Computer Science Graduate Projects

Computer Science Graduate Projects and Theses

Theses/dissertations from 2023 2023.

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

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

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

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

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

Fake News Detection Using Narrative Content and Discourse , Hongmin Kim

Anomaly Detection Using Graph Neural Network , Bishal Lakha

Robust Digital Nucleic Acid Memory , Golam Md Mortuza

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

Sparse Format Conversion and Code Synthesis , Tobi Goodness Popoola

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

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

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

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

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

Theses/Dissertations from 2022 2022

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

Meshfree Methods for PDEs on Surfaces , Andrew Michael Jones

Deep Learning of Microstructures , Amir Abbas Kazemzadeh Farizhandi

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

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

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

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

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

Hate Speech Detection Using Textual and User Features , Rohan Raut

Automated Detection of Sockpuppet Accounts in Wikipedia , Mostofa Najmus Sakib

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

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

Theses/Dissertations from 2021 2021

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

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

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

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

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

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

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

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

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

Actors for the Internet of Things , Arjun Shukla

Theses/Dissertations from 2020 2020

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

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

Polyhedral+Dataflow Graphs , Eddie C. Davis

Improving Spellchecking for Children: Correction and Design , Brody Downs

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

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

Unsupervised Structural Graph Node Representation Learning , Mikel Joaristi

Detecting Undisclosed Paid Editing in Wikipedia , Nikesh Joshi

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

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

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

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

Theses/Dissertations from 2019 2019

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

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

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

Querying Over Encrypted Databases in a Cloud Environment , Jake Douglas

A Hybrid Model to Detect Fake News , Indhumathi Gurunathan

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

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

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

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

Application-Specific Memory Subsystem Benchmarking , Mahesh Lakshminarasimhan

Multilingual Information Retrieval: A Representation Building Perspective , Ion Madrazo

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

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

BullyNet: Unmasking Cyberbullies on Social Networks , Aparna Sankaran

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

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

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

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

Theses/Dissertations from 2018 2018

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

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

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

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

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

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

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

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

Theses/Dissertations from 2017 2017

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

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

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

Predicting Friendship Strength in Facebook , Nitish Dhakal

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

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

Uncovering New Links Through Interaction Duration , Laxmi Amulya Gundala

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

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

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

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

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

Towards Automatic Repair of XACML Policies , Shuai Peng

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

Hand Gesture Recognition for Sign Language Transcription , Iker Vazquez Lopez

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

Theses/Dissertations from 2016 2016

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

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

Security Testing with Misuse Case Modeling , Samer Yousef Khamaiseh

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

Towards Multipurpose Readability Assessment , Ion Madrazo

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

CEST: City Event Summarization using Twitter , Deepa Mallela

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

Phoenix and Hive as Alternatives to RDBMS , Diana Ornelas

Collections
Disciplines
SelectedWorks Gallery
Albertsons Library
Division of Research
Graduate College

Advanced Search

Notify me via email or RSS

Author Corner

Home | About | FAQ | My Account | Accessibility Statement

Privacy Copyright

Recent PhD Topics in Computer Science

Get original and specialized topics on computer science area. We share brainstorming ideas scatter to your needs all our writer are trained PhD experts who come up with novel ideas. Conducting a PhD research in computer science mainly demands a powerful topic, which directs our research in an attainable path. We are here to distribute modern topics in computer science domain for managing efficient research:

Advanced Machine Learning and AI Algorithms : For reinforcement learning, unsupervised learning and AI transparency and deep learning, the innovative or emerging techniques are analyzed.
Quantum Computing and Information : Quantum machine learning, quantum algorithms, quantum cryptography and the synthesization of quantum and classical computing systems are intensely investigated in this research.
Cybersecurity and Privacy : Considering cyber security and privacy, it mainly specifies on improved cryptographic techniques, privacy-preserving data analytics, and cybersecurity in IoT and blockchain technology.
Human-Computer Interaction (HCI) : Virtual and Augmented reality, user experience (UX) structure, adaptive user interfaces and brain-computer interfaces are required to be considered.
Data Science and Big Data Analytics : Here, it is advisable to evaluate the implementation of AI (Artificial Intelligence) in big data, data visualization, real-time analytics and dealing large-scale data processing.
Computational Biology and Bioinformatics : The biological areas like genomics, proteomics, drug discovery by employing analytical methods and customized medicine are included in conducting the research.
Robotics and Autonomous Systems : This research area involves modernized robotics, automated vehicles, human-robot interaction and drone technology.
Sustainable Computing and Green Technologies : Environment-friendly digital technologies, maintainable data centres and powers saving computing systems are the important areas, as this research highly concentrates.
Internet of Things (IoT) and Edge Computing : For the purpose of operating IoT-generated data by utilizing edge computing, Iot and security in IoT systems, the innovative infrastructure is designed.
Software Engineering and Systems : Cloud computing techniques, software reliability and examination, advanced software technologies and Devops are necessarily reviewed here.
Networks and Communication Technologies : This study includes examining network security, satellite communication, software-defined networks and 5G/6G networks.
Theoretical Computer Science : It includes research topics like computability concept, model-based design, geometric computing and techniques.
Augmented and Virtual Reality : In healthcare, industry and gaming fields, the original applications and the improvement of AR/VR software and hardware are investigated.
Ethics and Social Implications of Computing : Encompassing the problem of unfairness, data gap and partiality, the consequences of AI (Artificial Intelligence) and other techniques on society are deeply explored.

How do you write a review paper in computer science?

Review paper generally elaborates a fair summary based on the specific topic and it is easily interpretable among readers. To write a compelling review paper on computer science, analyze our systematic guide:

Choose Your Topic : Depending on your passion, choose a topic and it must be prevalent in the domain of computer science. For handling the project in a simple manner, the topic has to be sufficiently brief as well as it must be wide to offer the considerable body of literature.
Define the Scope : The range of your review is required to be stated explicitly. You must highlight the perspectives of the topic, what it would bind and mention the constraints involved in the study. For example, Time bound and kind of publication.
Conduct a Comprehensive Literature Search :
Regarding bioinformatics and computational biology, utilize educational databases such as PubMed, Google Scholar, ACM Digital Library, arXiv and IEEE Xplore.
In accordance with your paper, try to find significant academic journals, books, magazines and educational papers.
To acquire a whole image of the topic progression, incorporate preliminary works as well as latest research.
Read and Evaluate Sources : For verifying the capacity, significance and dedication for the domain, analyze every source as you interpret. Note down the specific aspects and conceptually structure them.
Identify Trends and Gaps : Patterns, trends and gaps are required to be detected in the research. While identifying the gaps, consider key accomplishments, methods, popular concepts and areas which need further examination.
Develop a Thesis or Purpose Statement : An explicit objective or thesis statement needs to be provided by your review. In the process of structuring and highlighting your review, this thesis statement assists you.
Organize the Review : Verify your paper, whether it is organized in a sequential manner which flows effortlessly from one segment to another. It is a general procedure for determining the structure, which might be logical or conceptual.
Write the Paper :
Introduction : The introduction section incorporates the following sections- displaying the topic, detailing its relevance, summarizing the scope and exhibiting your thesis or objective statement.
Body : The literature should be addressed. In an extensive manner, exhibit every subject or area. You should contrast and differentiate various studies, examine the methods and emphasize the main result in the body segment.
Critical Analysis : Evaluate the research crucially, simply don’t offer the outline. Merits, Demerits and conflicts need to be considered. Regarding the productiveness and consequences of your study, serve your aspects.
Discussion/Conclusion : Give a brief outline of your result and address the impacts of your analysis. Mention the gap which you solve in research and for upcoming tasks, recommend some areas.
References : According to the suitable format, register all the sources which are mentioned in your paper. The formats like MLA, IEEE and API.
Revise and Edit : In the motive of attaining clearance, consistency and sequential structure in your review paper, revise your draft once again. Grammatical mistakes and typographical errors should be rectified. Make sure of the sources in your paper, whether it is mentioned properly.
Get Feedback : Acquire feedback on your paper from guides or nobles, if it is required. Beneficial information is provided by them as well as they assist you in detecting the areas, where it needs sufficient exploration.
Finalize the Paper : You should include the obtained feedback and conduct revisions as it is required. Get ready to provide a finalized version of your review paper.

What factors should be considered when designing a proposed system in computer science research?

Security, scalability, testability and maintainability are the essential components that we consider while designing a proposed system for your computer science research. Don’t worry we have well trained developers who guide your work on the right path.

Outage performance improvement using relay in device-to-device underlying cellular networks
Performance Evaluation of Wireless Cellular Networks with Mixed Channel Holding Times
Bandwidth Allocation Scheme and Call Admission Control for Multi-Services with Voice Priority and Degradation Policy in Wireless Cellular Networks
Handover performance of dynamic load balancing schemes in cellular networks
Downlink soft handover and power allocation for CDMA heterogeneous cellular networks
Mobile ad-hoc mesh for cellular networks: Mobile ad-hoc and mesh routing solutions based on OFDMA technologies for public safety & defence applications
Big Data Analysis Based Network Behavior Insight of Cellular Networks for Industry 4.0 Applications
Further Validation of an Electromagnetic Macro Model for Analysis of Propagation Path Loss in Cellular Networks Using Measured Driving-Test Data
On designing next generation MAC for cellular networks using the FLAVIA paradigm
Poster paper: A green transmission power control scheme for cellular networks in smart grid
A Game-Theoretic Framework for Coexistence of WiFi and Cellular Networks in the 6-GHz Unlicensed Spectrum
Asymptotic behavior of ultra-dense cellular networks and its economic impact
QoS-oriented channel assignment strategy for hierarchical cellular networks
A normalization model for analyzing multi-tier millimeter wave cellular networks
Power control in multihop cellular networks with multiple radio access technologies
Complementing Vehicular Connectivity Coverage through Cellular Networks
Modeling and Identification of Nonlinear Dynamics for Freeway Traffic by Using Information From a Mobile Cellular Network
Coexistence and Performance Limits for the Cognitive Broadband Satellite System and mmWave Cellular Network
Tradeoffs between handover performance and coverage range of relay stations in multihop cellular networks
Interference Constrained D2D Communication with Relay Underlaying Cellular Networks
PHD Guidance
PHD PROJECTS UK
PHD ASSISTANCE IN BANGALORE
PHD Assistance
PHD In 3 Months
PHD Dissertation Help
PHD IN JAVA PROGRAMMING
PHD PROJECTS IN MATLAB
PHD PROJECTS IN RTOOL
PHD PROJECTS IN WEKA
PhD projects in computer networking
COMPUTER SCIENCE THESIS TOPICS FOR UNDERGRADUATES
PHD PROJECTS AUSTRALIA
PHD COMPANY
PhD THESIS STRUCTURE
PHD GUIDANCE HELP
PHD PROJECTS IN HADOOP
PHD PROJECTS IN OPENCV
PHD PROJECTS IN SCILAB
PHD PROJECTS IN WORDNET
NETWORKING PROJECTS FOR PHD
THESIS TOPICS FOR COMPUTER SCIENCE STUDENTS
IEEE JOURNALS IN COMPUTER SCIENCE
OPEN ACCESS JOURNALS IN COMPUTER SCIENCE
SCIENCE CITATION INDEX COMPUTER SCIENCE JOURNALS
SPRINGER JOURNALS IN COMPUTER SCIENCE
ELSEVIER JOURNALS IN COMPUTER SCIENCE
ACM JOURNALS IN COMPUTER SCIENCE
INTERNATIONAL JOURNALS FOR COMPUTER SCIENCE AND ENGINEERING
COMPUTER SCIENCE JOURNALS WITHOUT PUBLICATION FEE
SCIENCE CITATION INDEX EXPANDED JOURNALS LIST
THOMSON REUTERS INDEXED JOURNALS
DOAJ COMPUTER SCIENCE JOURNALS
SCOPUS INDEXED COMPUTER SCIENCE JOURNALS
SCI INDEXED COMPUTER SCIENCE JOURNALS
SPRINGER JOURNALS IN COMPUTER SCIENCE AND TECHNOLOGY
ISI INDEXED JOURNALS IN COMPUTER SCIENCE
PAID JOURNALS IN COMPUTER SCIENCE
NATIONAL JOURNALS IN COMPUTER SCIENCE AND ENGINEERING
MONTHLY JOURNALS IN COMPUTER SCIENCE
SCIMAGO JOURNALS LIST
THOMSON REUTERS INDEXED COMPUTER SCIENCE JOURNALS
RESEARCH PAPER FOR SALE
CHEAP PAPER WRITING SERVICE
RESEARCH PAPER ASSISTANCE
THESIS BUILDER
WRITING YOUR JOURNAL ARTICLE IN 12 WEEKS
WRITE MY PAPER FOR ME
PHD PAPER WRITING SERVICE
THESIS MAKER
THESIS HELPER
DISSERTATION HELP UK
DISSERTATION WRITERS UK
BUY DISSERTATION ONLINE
PHD THESIS WRITING SERVICES
DISSERTATION WRITING SERVICES UK
DISSERTATION WRITING HELP
PHD PROJECTS IN COMPUTER SCIENCE
DISSERTATION ASSISTANCE

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

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.

PhD Projects in Computer Science (Sponsored/Self-Funded)

The School of Computing at Newcastle University is advertising a number of PhD projects in the areas of data science and computer vision, cybersecurity, human computer interaction, internet of things, distributed systems, and many more.

COMPSF01 : Understanding how Quantu m Weirdness Could Solve Hard Problems - Supervisors:  Nick Chancellor  and  Jonte Hance

COMPSF03 : Applications of Graph Width Parameters to Computational Complexity - Supervisor:  Konrad Dabrowski

COMPSF04 : Longitudinal brain imaging and modelling of ageing and disease processes - Supervisor:  Yujiang Wang

COMPSF05 : Biomedical Explainable AI - Supervisor:  Jaume Bacardit

COMPSF06 : A nalysing and Visualizing Brain-Heart Interactions in Health and Disease - Supervisor: Alaa Alahmadi

COMPSF07 : AI transparency - Supervisor:  Pawel Widera

COMPSF08 : Advancing Edge AI: PhD Research Opportunities at the EPSRC National Edge Artificial Intelligence Hub - Supervisor: Ellis Solaiman

COMPSF09 : Large Language Model for Robotics - Supervisor:  Bo Wei

COMPSF10 : Adaptive and Trusted Execution in the Cloud-Edge Continuum - Supervisor: Devki Jha

COMPSF11 : TinyML for the Internet of Things - Supervisor:  Tomasz Szydlo

COMPSF12 : Interfacing Between the Smart Home and the Street - Supervisor:  Nick Taylor

COMPSF13 : Advancing Human-Centred AI: PhD Research Opportunities in the Haii Lab – Supervisor:  Lei Shi

COMPSF14 : Performance and security in VANETs – Supervisor:  Nigel Thomas

COMPSF15 : Event-Based Visualization of Temporal Networks – Supervisor:  Daniel Archambault

COMPSF16 : Data Visualization for Human-AI Collaboration - Supervisor:  Xinhuan Shu

COMPSF17 : Data visualization for explainability and data-driven decision-making in health and life sciences – Supervisor:  Sara Fernstad

COMPSF18 : Supervisor: Aydin Abadi

Project 1: Dealing with Financial Fraud through Privacy-Enhancing Technologies

Project 2: Incentivizing Participation in Privacy-Enhancing Technologies

Project 3: Enhancing Federated Learning Efficiency and Scalability with Advanced Cryptographic Protocols

Project 4: Mitigating Insider Threats in Financial Sectors through the Development and Use of Privacy-Enhancing Technologies

Project 5: Improving Federated Learning Model Quality with Privacy-Preserving Data Cleaning Techniques

COMPSF19 : Privacy Enhancing Technologies for Cyber-Physical Systems – Supervisor:  Shishir Nagaraja

COMPSF20 : AI and Cyber Security for Cyber Physical Systems – Supervisor:  Mujeeb Ahmed

COMPSF21 :   Supervisor: Phil Lord

Project 1: Hyperscale Graphs Models of Life

Project 2: A Computational representation of Gender

COMPSF22 :   Operations intelligence for decarbonisation – Supervisor: Wanqing Zhao

COMPSF23 : Trustworthy and Secure Artificial Intelligence – Supervisor: Varun Ojha

COMPSF24 : Deep Learning Models for Predicting Microbial Protein Expression – Supervisor: Gizem Buldum

COMPSF25 : DNA Nanotechnology and Molecular Computing – Supervisor: Harold Fellerman n

Prospective Students are encouraged to contact project supervisors directly for more information on specific projects or research topics.

Start dates are January, April or September and we recommend allowing 2-3 months for the application process.

There are no formal deadlines to apply for a project, projects will be withdrawn once a suitable student has been accepted.

Eligibility

Please note that these projects are not funded and are available only for applicants who have their own funding or who plan to apply for funding for their studies.

PhD studies are available to all applicants holding, or expecting to receive, a good (2:1 minimum or equivalent) bachelor's degree in Computer Science or related discipline, or an international equivalent. Applicants should have a strong background in Computer Science or related area. High motivation for independent theoretical/computational work is essential. Skills and interest in project specific areas are also desirable. Newcastle University values individual differences and the diversity that this brings. We want to ensure that no-one is at a disadvantage because of who they are. We encourage applications from under-represented groups, and requests for flexible study (e.g., part-time) are welcomed.

Applicants whose first language is not English require an IELTS score of 6.5 overall with a minimum of 5.5 in each subsection.

International applicants may require an ATAS ( Academic Technology Approval Scheme ) clearance certificate prior to obtaining their visa and to study on this programme.

How to apply

You should apply through the University's Apply to Newcastle Portal

Once registered select ‘Create a Postgraduate Application’.

Use ‘Course Search’ to identify your programme of study:

Search for the ‘Course Title’ using programme code: 8050F
Research Area: Computing Science
Select PhD Computer Science as the programme of study

You need to provide the following in ‘Further Questions’ section:

‘Personal Statement’ - (this is a mandatory field) upload a document or write a statement statement directly into application form
When prompted - select ‘Write Proposal’. Type the title of the research project from this advert that you want to apply for. You don’t need to upload a research proposal.
The code for the project that you want to apply for e.g. COMPSF01 in the 'Studentship/Partnership Reference'

In the ‘Supporting Documentation’ section you should upload:

A covering letter and your CV
Degree transcripts and certificates
If English is not your first language, a copy of your English language qualification if already completed.

You must submit one application per studentship, you cannot apply for multiple studentships on one application.

IMAGES

PhD Projects in Computer Science Engineering (Research CSE PhD Thesis)
Final Year Projects for Computer Science
PhD Ideas in Computer Science
PHD PROJECTS IN COMPUTER SCIENCE
PhD Projects in Computer Science Engineering (Help)
Best Project Topics for Computer Science Student

COMMENTS

Potential PhD projects
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. ... In this PhD project, we'll investigate various computer vision, machine learning (especially deep learning) and ...
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 ...
PHD Computer Science Projects
PHD Computer Science Projects, Computer science is the study of computers includes data base management, data structure, and computer architecture and computer communication. We offer PhD computer science projects with advanced research in computer technology we implement IEEE transaction and other articles are selected for PhD computer science academic projects. We developed more than 100+ […]
Phd Projects in Computer Science
PHD PROJECTS IN COMPUTER SCIENCE PHD PROJECTS IN COMPUTER SCIENCE, is a broad area as it contains many practical and theoretical concepts. Computer science deals with all concepts that are used in many recent technologies. Research scope in computer science is enormous as its fundamental domains like networking, cloud computing, image ...
Computer Science Graduate Projects and Theses
The Department of Computer Science is a discipline concerned with the study of computing, which includes programming, automating tasks, creating tools to enhance productivity, and the understanding of the foundations of computation. The Computer Science program provides the breadth and depth needed to succeed in this rapidly changing field. One of the more recent fields of academic study ...
Recent PhD Ideas in Computer Science with Simulation
Recent PhD Topics in Computer Science. Get original and specialized topics on computer science area. We share brainstorming ideas scatter to your needs all our writer are trained PhD experts who come up with novel ideas. Conducting a PhD research in computer science mainly demands a powerful topic, which directs our research in an attainable path.
Computer Science PhD Research Projects PhD Projects ...
We have 1,615 Computer Science PhD Research Projects PhD Projects, Programmes & Scholarships Auckland Bioengineering Institute The Auckland Bioengineering Institute (ABI) does world-leading research that enhances the diagnosis and treatment of a range of medical conditions, as well as helping to improve the lives of people with disabilities or ...
Ph.D. Topics in Computer Science
PhD in Computer Science: Admission, Eligibility Date January 30, 2021; Tips to choose good PhD research topics/ problems Date January 7, 2022; PhD Six Month Progress Report Sample/ Format Date May 17, 2021; Free to publish SCI Open access journals; DOAJ indexed medical journals with fast publication; Top Entomology Journals for Insect Science ...
PhD Projects Computer Science
PhD Projects in Computer Science (Sponsored/Self-Funded) The School of Computing at Newcastle University is advertising a number of PhD projects in the areas of data science and computer vision, cybersecurity, human computer interaction, internet of things, distributed systems, and many more.
computer science PhD Projects, Programmes & Scholarships
The School of Computing at Newcastle University is advertising a number of PhD projects in the areas of data science and computer vision, cybersecurity, human computer interaction, internet of things, distributed systems, and many more. Read more

Potential PhD projects

Artificial intelligence

Service oriented computing

Human-Centred computing

Networked systems and security

Theoretical computer science

Projects with top up scholarship for domestic students

We offer a range of services to help you

PhD Topics in Computer Science for Real-World Applications

CALL US : +0091.11.4951 3011

+91 9629863243

image processing projects:

Computer networking projects:

Mining projects:

Networking projects:

Related Pages

ScholarWorks

Computer Science Graduate Projects and Theses

Theses/Dissertations from 2022 2022

Theses/Dissertations from 2021 2021

Theses/Dissertations from 2020 2020

Theses/Dissertations from 2019 2019

Theses/Dissertations from 2018 2018

Theses/Dissertations from 2017 2017

Theses/Dissertations from 2016 2016

Author Corner

Recent PhD Topics in Computer Science

What factors should be considered when designing a proposed system in computer science research?

Ph.D. Topics in Computer Science

The hottest topics in computer science

Cybersecurity

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

Internet of things

Cloud computing

Machine learning

Share this:

PhD Projects in Computer Science (Sponsored/Self-Funded)

IMAGES

COMMENTS