Postgraduate Research Scholarship

Awarding Excellence: Smart & Sustainable IT for IEEE 2025 World Forum on Internet of Things: Dr Di Wu

Chair of the IEEE IoT Educational Activities Committee and Award Presenter, Dr Wanqing Tu, alongside Di Wu

From St Andrews to Chengdu, Dr. Di Wu has been awarded third place in the 2025 IEEE World Forum on Internet of Things PhD Thesis Competition.  IEEE is an internationally recognised organisation within electrical and electronics engineering. By participating in such an event, participants can receive valuable external feedback and connect with a larger community focused on the future vision of IoT Systems.

With a thesis titled “Distributed Machine Learning on Edge Computing Systems,” Dr. Wu proposes three techniques to better train machine learning models that directly affect small devices such as sensors, smartphones, and every day IoT gadgets. He states that the focus on smaller devices is becoming even more important due to the grand size of modern datasets, as well as how time-consuming, expensive, and at-risk to user privacy sending information to the cloud can be:

In my research, I proposed three techniques to make this kind of training more practical. The first helps devices decide how to split and share the workload. The second reduces the amount of data that needs to be exchanged during training. And the third lowers the amount of computation each device has to perform. Finally, I brought all these ideas together into one complete system. When we tested it on real IoT devices, it trained models faster, communicated less data, and achieved better accuracy compared with existing methods.

This improvement in efficiency suitably aligns with IEEE’S 2025 theme of “Smart and Sustainable IoT.” ‘To me’ Dr Wu states, ‘“smart” IoT means giving devices the ability to learn and make decisions locally. While “sustainable” IoT means doing this in a way that saves energy, protects user privacy, and can scale as the number of devices continues to grow. Therefore, by cutting down the computation and communication needed for training, intelligent IoT systems can become more sustainable and easier to deploy in practice.’ With this in mind, Dr. Wu propelled forward with his research that was also greatly influenced by the challenges he experienced as a machine learning engineer and the specific research questions that arose from reading subject-specific literature, discussing ideas with his supervisor Blesson Varghese, as well as building real-world prototypes throughout his PhD journey.

I truly see preparing for the nomination as a natural step that came out of the work I did during my PhD. I had published papers in related venues, including the IEEE Internet of Things Journal and IEEE Transactions on Parallel and Distributed Systems, which gave me some confidence that my work was heading in the right direction. Furthermore, writing my thesis, presenting ideas at conferences, as well as preparing for my viva helped me clarify my ideas which eventually helped me piece together and highlight the parts of my research that were most relevant to the theme. I would really encourage PhD graduates to apply for these kinds of thesis competitions.[1]

Now working as a Research Fellow funded by the UK National Edge AI Hub, Dr. Wu reflects on how this year’s IEEE displayed active research engagement with the intersection of AI and IoT — ‘both AI for IoT, where AI is used to solve IoT-specific problems, and AI on IoT, where we try to bring AI capabilities directly onto IoT devices.’ Another emerging direction he noted was the integration of sensing, communication, and computation. ‘These used to be relatively separate research areas, each led by different communities. But now we’re seeing growing interest in combining them into a single, unified system, which I think has a lot of potential.’ As Dr. Wu continues to explore efficient and scalable machine learning systems at the edge, he believes his new research direction will move beyond traditional federated learning, turning specifically to how agent-based systems and efficient foundation models (such as large language models) can be brought to the edge. ‘These areas are quite different from conventional ML systems, but they open up exciting possibilities for the next generation of edge intelligence,’ he concludes.

[1] Dr. Di Wu personally recommends competitions such as, ACM PhD Competition, the IEEE IoT PhD Competition, the IEEE TCSC PhD Thesis Award, as well as local competitions like the SICSA PhD Competition in Scotland.

PhD studentships available for 2026 entry

The School of Computer Science at the University of St Andrews is offering a number of PhD scholarships for 3.5 years of study in our doctoral research programme for entry in 2026/7. UK, EU and International students are all eligible for fully-funded scholarships consisting of tuition and a stipend. These awards are part-funded through the University of St Andrews’ ‘handsel’ scheme for tuition waivers.

The School of Computer Science is a centre of excellence for computer science teaching and research, with staff and students from Scotland and all parts of the world. It is a member of the Scottish Informatics and Computer Science Alliance (SICSA).

More details on how to apply can be found on the University Scholarships page. The closing date for equal consideration is 1 Feb 2026.

We strongly advise potential applicants to read our School PGR web pages and to find a supervisor prior to applying. Any questions can be directed to pg-admin-cs@st-andrews.ac.uk.

Fully-funded PhD scholarship in Artificial Intelligence and Copyright

The University of St Andrews School of Computer Science and the Macquarie University Law School are pleased to offer a Global Doctoral Scholarship in the area of Artificial Intelligence and Copyright Law: The Problem of Authorship. This PhD scholarship is fully-funded and includes fees (home or international) and a stipend for 3.5 years. The PhD will be supervised by Dr Tristan Henderson at St Andrews and Dr Daniela Simone at Macquarie.

Artificial Intelligence (AI) is increasingly important as businesses begin to use it to undertake work traditionally done by humans. As AI systems become more sophisticated and capable, they become more significant in content creation. But the use of AI complicates the determination of authorship of the work created using it; and, as a consequence, the subsistence and ownership of any copyright in that work. This project will investigate the challenges that arise when copyright’s concept of authorship meets AI from an interdisciplinary perspective.

Authorship is at the heart of copyright law in most legal traditions. Copyright protection usually requires some ‘authorial’ input, but jurisdictions differ on the amount and type of human contribution that counts. As AI transforms creative processes and supercharges content generation at scale more clarity is urgently needed. Many are concerned that AI output will displace human creativity – and as such they question whether it should be incentivised via copyright at all. Human creators who turn to AI to augment and complement their own practices will be equally concerned about whether the use of AI tools affects their ability to exploit their work. There is a public interest in ensuring that creativity is encouraged and rewarded.

Whilst it is clear that human input shapes AI output, AI developers and users have much less control over output than a writer does over the words produced by a pen. Is AI output authored in a sense that copyright can, or should, recognise? This question has important implications for the economic exploitation of work made using AI. Uncertainty and variation between the law of different jurisdictions creates economic and cross-border friction. An important and related question is how market participants – both rights-holders and users of creative work – know whether the level of human input rises to the level of copyrightability. Can technology, the law, or both, help provide these participants with sufficient transparency and control over their input?

Where current research focuses on the technological abilities of AI to respect copyright, or the legal application of copyright to AI outputs, we would like to go beyond this and seek student-led proposals on the application and limits of copyright’s concept of authorship from an interdisciplinary computer science (CS) and law perspective. Projects might address questions such as:

  • What amount and type of human contribution suffices to establish authorship of AI output?
  • What sort of causal relationship must exist between human input and AI output to establish authorship?
  • How can human contribution be verified, measured, traced or evidenced?
  • Is control over the output an appropriate touchstone for authorship?
  • How can existing AI systems be adapted to provide sufficient attribution?
  • How do provisions relating to computer-generated works apply?
  • What is the proper scope of copyright in AI output?

This is a joint Computer Science (CS) and Law project, and the successful candidate should have a foundational knowledge in one or both of these disciplines, and a willingness to engage with other related disciplines. Our experience indicates that successful interdisciplinary projects still reside in a ‘home’ discipline, and so we would welcome proposals that use legal approaches such as doctrinal or comparative methods and are informed by CS, as well as CS-driven proposals such as the development of new AI systems that are informed by legal requirements.

The student would join the St Andrews School of Computer Science’s Responsible and Sustainable Research Theme, and be a part of the Scottish Informatics and Computer Science Alliance, a research pool of all 14 computer science departments across Scotland. Depending on the focus of the proposal, the student could join Macquarie’s Data Horizons Research Centre, Centre for Applied Artificial Intelligence, and/or the Ethics and Agency Research Centre. Resources provided by both departments will include computing equipment, conference travel and access to GPU-equipped servers.

Details on how to apply can be found at https://www.mq.edu.au/research/phd-and-research-degrees/how-to-apply/scholarship-opportunities/scholarship-search/ai-and-copyright-law-the-problem-of-authorship and applications should be received by 3 December 2025. Interested applicants are encouraged to make informal contact with the supervisors first by e-mail: Tristan Henderson tnhh@st-andrews.ac.uk and Daniela Simone daniela.simone@mq.edu.au.

PGR Seminar with Zihan Zhang + Berné Nortier

The next PGR seminar is taking place this Friday 4th April at 2PM in JC 1.33a

Below are the Titles and Abstracts for Zihan and Berné’s talks – Please do come along if you are able.

Zihan Zhang

Title: FedOptima: Optimizing Resource Utilization in Federated Learning

Abstract: Federated learning (FL) systems facilitate distributed machine learning across a server and multiple devices. However, FL systems have low resource utilization limiting their practical use in the real world. This inefficiency primarily arises from two types of idle time: (i) task dependency between the server and devices, and (ii) stragglers among heterogeneous devices. We propose FedOptima, a resource-optimized FL system designed to simultaneously minimize both types of idle time; existing systems do not eliminate or reduce both at the same time. FedOptima offloads the training of certain layers of a neural network from a device to server using three innovations. First, devices operate independently of each other using asynchronous aggregation to eliminate straggler effects, and independently of the server by utilizing auxiliary networks to minimize idle time caused by task dependency. Second, the server performs centralized training using a task scheduler that ensures balanced contributions from all devices, improving model accuracy. Third, an efficient memory management mechanism on the server increases scalability of the number of participating devices. Four state-of-the-art offloading-based and asynchronous FL methods are chosen as baselines. Experimental results show that compared to the best results of the baselines on convolutional neural networks and transformers on multiple lab-based testbeds, FedOptima (i) achieves higher or comparable accuracy, (ii) accelerates training by 1.9x to 21.8x, (iii) reduces server and device idle time by up to 93.9% and 81.8%, respectively, and (iv) increases throughput by 1.1x to 2.0x.

Berné Nortier

Title: Shortest paths and optimal transport in higher-order systems

Abstract: One of the defining features of complex networks is the connectivity properties that we observe emerging from local interactions. Nevertheless, not all networks describe interactions which are merely pairwise. Recently, different frameworks for modelling non-dyadic, higher-order, interactions have been proposed, garnering much attention. Of these, hypergraphs have emerged as a versatile and powerful tool to model such higher-order networks. However, the connectivity properties of real-world hypergraphs remain largely understudied. A first, data-driven, work introduces a measure to characterise higher-order connectivity and quantify the relevance of non-dyadic ties for efficient shortest paths in a diverse set of empirical networks with and without temporal information. The analysis presents a nuanced picture.

A second work (in progress) considers higher-order simplicial networks within the context of optimal transport, where shortest paths do not always lead to optimal resource allocation. We extend the existing framework to the higher-order setting to explore to what degree this additional degree of freedom influences the flux of resources in a system of interest.

PGR Seminar with Sachin Yadav and Junyu Zhang

The next PGR seminar is taking place this Friday 14th February at 2PM in JC 1.33a

Below is a title and Abstract for Sachin and Junyu’s talks – Please do come along if you are able.

Sachin Yadav

Title: Reimagining the Digital Gig Economy: Evaluating the economic feasibility and technological capabilities of physical cooperative gig platform

Abstract: The gig economy, fuelled by digital platforms, has transformed the labour markets around the world, offering flexibility but often at the cost of security for the worker and fair compensation. This presentation explores platform cooperatives – a democratically owned and governed alternative – as a potential solution to these challenges. I will delve into the economic feasibility and technological capabilities of physical delivery cooperatives, comparing them to traditional investor-owned platforms. By examining key performance metrics, regulatory environments, and worker empowerment, my ongoing work will assess whether platform cooperatives can achieve a comparable level of service while fostering more equitable working conditions. This presentation aims to spark discussion on the future of the gig economy and the role cooperative models can play in creating a more sustainable digital labour landscape.

Junyu Zhang

Title: Engaging Culture Heritage with Authentic Characters to Support Inclusive Learning

Abstract: Digitalization opens up new opportunities for cultural heritage, and lately the exploration of virtual reality has created new forms of representation of cultural content for educational institutions, museum exhibitions, and heritage preservation organizations. High-fidelity technology allows virtual agents to simulate realistic human appearances and behaviour to interact and engage with their surroundings. This speech presents work-in-progress research regarding designing, creating and utilising authentic characters to strengthen the exhibition of cultural heritage. Through the discussion on research design and practice, this research examines the capability of characters to enrich immersion and communication with heritage. This presentation introduces the realism and authenticity of character design, clarifies the goals for digitalization for inclusive learning opportunities in SDG, and ends with future work.

Fully funded PhD scholarship in Multi-agent Path Planning

Lead supervisor: Professor Ian Miguel

Application deadline: 1 March 2025

Project description:

Planning is a fundamental discipline of Artificial Intelligence, which asks us to find a sequence of actions transforming an initial state into a goal state. This project focuses on multi-agent path planning (also known as multi-agent path finding), where a set of mobile agents is navigated from starting positions to target positions. MAPP is the focus of intense research effort because it has many challenging real-world applications in robotics, navigation, the video game industry, and automatic warehousing. Automatic warehousing is one of the most challenging domains and the focus of the greatest investment. For example, Amazon have invested heavily in robot-equipped warehouses. It is performed on a huge scale (thousands of robots in warehouses containing many thousands of shelves and products) with the need to find an efficient solution quickly so that the robots are always safely moving towards their goals. The typical layout of a warehouse increases difficulty further: shelves are packed tightly into the space, reducing the capacity for movement of the robots.

MAPP is inherently very difficult — there is no known “cheap” method to produce high quality solutions quickly at the scale required. Current approaches fall into two categories, both relying on AI techniques that search through the vast space of possible solutions. Those that guarantee optimality struggle to scale, while approaches that scale do so at the cost of reduced solution quality. This proposal is to advance the state of the art in optimal MAPP significantly through a novel combination of path planning and constraint programming. Constraint programming is a powerful automated reasoning technique that allows us to model a complex decision-making problem such as MAPP by describing the set of choices that must be made (e.g. which path a robot should take) and the set of constraints that specify allowed combinations of choices (e.g. robots cannot collide). This model is presented to a constraint solver, which searches for solutions automatically, using powerful deduction mechanisms to reduce search considerably.

The project includes the following objectives:

A New Modelling Perspective: The model input to a constraint solver is crucial to the efficiency with which solutions can be found. Our proposed innovation is in how MAPP is modelled. We will exploit the many equivalencies in these problems, for example equivalent routes between locations, and equivalent resources in terms of the robots. While these remain in the model they must potentially all be explored, wasting enormous effort. Instead of modelling the warehouse layout at a fine level of detail, the current default leading to the consideration of a vast number of equivalent paths, we will abstract the fine-grained grid representation into larger regions, for example representing an entire corridor between two shelves.

Ensuring Validity: The research challenge in adopting this more abstract modelling perspective is to ensure that plans found with this reduced representation are valid in the real warehouse by, for example, constraining these regions so that their capacities are respected and the flow of traffic within them is such that collisions and deadlocks cannot occur.

Evaluation and refinement: We will evaluate our new model on benchmark problems drawn from the competitions where state of the art MAPP solvers compete. This will allow us to gauge progress and refine and improve our new approach.

The result of this research will be to improve the scalability of optimal solvers, producing better quality solutions, increasing the throughput of a warehouse, and reducing operational costs.

Eligibility Criteria

We are looking for highly motivated research students willing to be part of a diverse and supportive research community. Applicants must hold a good Bachelor’s or Master’s degree in Computer Science, or a related area appropriate for their proposed topic of study.

International applications are welcome. We especially encourage female applicants and underrepresented minorities to apply. The School of Computer Science was awarded the Athena SWAN Silver award for its sustained progression in advancing equality and representation, and we welcome applications from those suitably qualified from all genders, all races, ethnicities and nationalities, LGBT+, all or no religion, all social class backgrounds, and all family structures to apply for our postgraduate research programmes.

Value of Award
  • Tuition scholarships cover PhD fees irrespective of country of origin.
  • Stipends are valued at £19,795 per annum (or the standard UKRI stipend, if it is higher).
To apply:

Interested applicants can contact Professor Ian Miguel with an outline proposal.

Full instructions for the formal application process can be found at How to apply – School of Computer Science – University of St Andrews

 

PGR Seminar with Zhongliang Guo

The next PGR seminar is taking place this Friday at 2PM in JC 1.33a

Below is a title and Abstract for Zhongliang’s talk– Please do come along if you are able.

Title: Adversarial Attack as a Defense: Preventing Unauthorized AI Generation in Computer Vision

Abstract: Adversarial attack is a technique that generate adversarial examples by adding imperceptible perturbations to clean images. These adversarial perturbations, though invisible to human eyes, can cause neural networks to produce incorrect outputs, making adversarial examples a significant security concern in deep learning. While previous research has primarily focused on designing powerful attacks to expose neural network vulnerabilities or using them as baselines for robustness evaluation, our work takes a novel perspective by leveraging adversarial examples to counter malicious uses of machine learning. In this seminar, I will present two of our recent works in this direction. First, I will introduce the Locally Adaptive Adversarial Color Attack (LAACA), which enables artists to protect their artwork from unauthorized neural style transfer by embedding imperceptible perturbations that significantly degrade the quality of style transfer results. Second, I will discuss our Posterior Collapse Attack (PCA), a grey-box attack method that disrupts unauthorized image editing based on Stable Diffusion by exploiting the common VAE structure in latent diffusion models. Our research demonstrates how adversarial examples, traditionally viewed as a security threat, can be repurposed as a proactive defense mechanism against the misuse of generative AI, contributing to the responsible development and deployment of these powerful technologies.

Fully funded PhD scholarship in Algorithms for Data Science

Lead supervisor: Dr Peter Macgregor

Application deadline: 1 March 2025

Project description:

Modern data science and machine learning applications involve datasets with millions of data points and hundreds of dimensions. For example, deep learning pipelines produce massive vector datasets representing text, image, audio and other data types. The analysis of such datasets with classical algorithms often requires significant time and/or computational resources which may not be available in many applications.

This motivates the development of a new generation of fast algorithms for data analysis, running in linear or sub-linear time and often producing an approximate result rather than an exact one. Moreover, the dataset may change over time, requiring dynamic algorithms which handle updates efficiently.

This project will tackle aspects of the design, analysis, and implementation of algorithms for processing large dynamic datasets, with the aim to develop new algorithms with state-of-the-art practical performance and/or theoretical guarantees. This could involve performing new analysis of existing algorithms, designing new algorithms with provable guarantees, or implementing heuristic algorithms with state-of-the-art empirical performance.

Possible Directions

Potential areas of research, depending on the interests of the candidate include:

  • Developing improved nearest-neighbour search algorithms (e.g., based on kd-trees, HNSW, locality-sensitive hashing).
  • Exploring any connection between hierarchical clustering algorithms and nearest-neighbour search algorithms.
  • Creating new dynamic or hierarchical clustering algorithms (e.g. based on spectral clustering or DBSCAN).
  • Creating dynamic algorithms for numerical linear algebra. For example, maintaining the PCA of a dynamically changing dataset.
  • Any other project in the area of algorithmic data science and machine learning.

Applicants should have a strong interest in the mathematical analysis of algorithms, knowledge of topics in discrete mathematics and linear algebra, and some familiarity with existing algorithms for data analysis and machine learning. Strong programming skills would also be desirable.

The scholarship:

We have one fully-funded scholarship available, starting in September 2025. The scholarship covers all tuition fees irrespective of country of origin and includes a stipend valued at £19,705 per annum. More details of the scholarship can be found here: https://blogs.cs.st-andrews.ac.uk/csblog/2024/10/24/phd-studentships-available-for-2025-entry/, but please note the different application deadline.

Eligibility criteria:

We are looking for highly motivated research students keen to be part of a diverse and supportive research community. Applicants must hold a good Bachelor’s or Master’s degree in Computer Science, or a related area appropriate for the topic of this PhD.

International applications are welcome. We especially encourage female applicants and underrepresented minorities to apply. The School of Computer Science was awarded the Athena SWAN Silver award for its sustained progression in advancing equality and representation, and we welcome applications from those suitably qualified from all genders, all races, ethnicities and nationalities, LGBT+, all or no religion, all social class backgrounds, and all family structures to apply for our postgraduate research programmes.

To apply:

Interested applicants can contact Peter Macgregor with an outline proposal.

Full instructions for the formal application process

The deadline for applications is 1 March 2025.

Fully funded PhD scholarship in software ethics

Supporting ethical deliberation in the software lifecycle

 

Lead supervisor: Dr Dharini Balasubramaniam

Application deadline: 1 March 2025

Project description:

Software ethics covers a broad spectrum of concerns including accountability, fairness, privacy and data protection, transparency, safety, security, accessibility, digital inclusion and sustainability. Much of the current dialogue on software ethics relates to the development, deployment and use of AI-based solutions, although there are ethical concerns related to most, if not all, software application domains. The pervasive nature of software, its critical importance to the functioning of many sectors, and the opaque nature of software-supported decision making in some domains all make it vital that ethical issues are explicitly considered throughout the software lifecycle.

There is generic ethics guidance, such as the ACM / IEEE Software Engineering Code of Ethics and sets of ethical principles specifically aimed at domains such as AI, available to software engineers. Generic and specific concepts such as value-based software development and responsible AI have been proposed to encourage ethical software development. However, there is still a lack of processes, notations, tools and training available to software professionals to support systematic ethical deliberation and ethics-driven development in practice.
This project will explore and attempt to address this gap. The student will design and develop ways to explicitly capture ethical requirements, risks and mitigations as first-class concepts in software artefacts. They will implement tools that work with these specifications to analyse the compliance of software artefacts with ethical requirements, and highlight potential violations and consequences. Interviews with software professionals and service providers may be used to inform and evaluate the efficacy and viability of outcomes. Open-source projects in chosen application domains may also be used for case study-based evaluation.

Topics of interest:

Specific topics of interest include, but are not limited to:

  • A framework of ethical concerns that apply to software,
  • Notations to represent ethical requirements, risks and mitigations as first-class concepts in software design and implementation,
  • Tool support for the representation and analysis of ethical concerns in software artefacts,
  • Process and tool support for considering specific aspects of software ethics, such as bias avoidance, transparency, sustainability or accessibility, and
  • Integration of ethical training and deliberation within project and product management environments.

The scholarship:

We have one fully-funded scholarship available, starting in September 2025. The scholarship covers all tuition fees irrespective of country of origin and includes a stipend valued at £19,705 per annum. More details of the scholarship can be found here: https://blogs.cs.st-andrews.ac.uk/csblog/2024/10/24/phd-studentships-available-for-2025-entry/, but please note the different application deadline.

Eligibility criteria:

We are looking for highly motivated research students keen to be part of a diverse and supportive research community. Applicants must hold a good Bachelor’s or Master’s degree in Computer Science, or a related area appropriate for the topic of this PhD.

International applications are welcome. We especially encourage female applicants and underrepresented minorities to apply. The School of Computer Science was awarded the Athena SWAN Silver award for its sustained progression in advancing equality and representation, and we welcome applications from those suitably qualified from all genders, all races, ethnicities and nationalities, LGBT+, all or no religion, all social class backgrounds, and all family structures to apply for our postgraduate research programmes.

To apply:

Interested applicants can contact Dharini Balasubramaniam with an outline proposal. Full instructions for the formal application process can be found at https://www.st-andrews.ac.uk/computer-science/prospective/pgr/how-to-apply/.

The deadline for applications is 1 March 2025.