Research

PGR Seminar with Gen Li + Jess McGowan

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

Below are the Titles and Abstracts for Gen and Jess’ talks – Please do come along if you are able.

Gen Li

Title: Visualization of clinical pathways based on sepsis comorbidities

Abstract: Sepsis is a severe infectious syndrome that can lead to critical illness and death. At present, most retrospective studies on sepsis focus on diagnosis and mortality risk prediction, with relatively limited attention to patients’ medical backgrounds. Comorbidities, as an important factor affecting the severity of the disease and treatment outcomes, present complex and variable characteristics in the treatment process. However, current research in this field generally lacks in-depth analysis of clinical pathways such as patient transfers and treatment interventions during hospitalization, which limits the development of personalized treatment strategies. Based on this, our research plans to use machine learning methods to extract similar comorbidity sub-groups of sepsis patients from electronic health records (EHRs), and further combine them with advanced visualization technology to explore the clinical pathways of these sub-groups. The research aims to help clinicians gain insights into the potential relationship between sepsis and related comorbidities, improve the interpretability of patients’ clinical records, and thus develop more effective treatment and management strategies for patients.

Jess McGowan

Title: Roll For Initiative: From Play to Personas

Abstract: In user centred design, designing for a wide target audience can lead to systems attempting to please everyone and thus pleasing no-one. Using a persona, i.e. a single member of that target audience, and designing a system dedicated to their needs results in a more focused design, which leads to improved usability. However, the design of personas is largely unstructured, with no clearly agreed methodology behind their creation. The solution to this could be found in Tabletop Role Playing Games (TTRPGs), which tend to feature clearly structured character creation instructions. This project aims to investigate to what extent can TTRPG character creation instructions aid the design of personas.

PGR Seminar with Dhananjay Saikumar

The next PGR seminar is taking place this Friday 25th April at 2PM in JC 1.33a

Below are the Title and Abstract for Dhananjay’s talk – Please do come along if you are able.

Title: Signal Collapse in One-Shot Pruning: When Sparse Models Fail to Distinguish Neural Representations

Abstract: The deep learning breakthrough in 2012, marked by AlexNet’s success on the ImageNet challenge, ushered in an era of increasingly large neural networks. Modern models now hold tens of millions to billions of parameters, enabling remarkable capabilities but creating serious challenges for deployment in real-world, resource-constrained environments. This has led to growing interest in model compression, with network pruning emerging as a widely adopted method to reduce computational and memory demands. Iterative pruning—based on repeated prune-retrain cycles—can retain accuracy but becomes infeasible at scale due to high computational cost. One-shot pruning, which removes parameters in a single step without retraining, offers a more scalable alternative but often results in severe accuracy degradation. For instance, pruning 80% of the parameters from RegNetX-32GF (a 100M+ parameter model) drops ImageNet accuracy from 80% to 1%, rendering the model unusable. This talk uncovers a new and fundamental bottleneck behind such failures: signal collapse, a previously overlooked phenomenon that disrupts the network’s ability to distinguish between inputs. To address this, a simple and efficient method called REFLOW is introduced, enabling sparse networks to recover strong performance without retraining or gradient computation. On RegNetX-32GF, REFLOW lifts accuracy from 1% to 73% at 80% sparsity—in under 15 seconds. These findings reframe the challenges of one-shot pruning and open new opportunities for practical and efficient deployment of deep learning models.

SACHI Seminar, Stavroula Pipyrou – Radical Imagination: Knowledge Through Generations

We are pleased to share our upcoming SACHI seminar this week by Dr Stavroula Pipyrou, a Senior Lecturer in Social Anthropology at the University of St Andrews and Founding Director of the Centre for Minorities Research.

📅 Wednesday 2nd April  | 🕛 13:00 – 14:00 PM | 📍 JCB, Room 1.33A

Title:

Radical Imagination: Knowledge Through Generations

Abstract:

“Today we will engage with insights from a young Greek interlocutor who did not live the Cold War period firsthand. She relates to the legacies of the Cold War through radical imagination, projecting that it is only logical that the affects of the era have left irreversible psychological marks on the people who experienced it. The talk proposes a theory of psychic time and generational battles for belonging. There is a critique of history as taught in school textbooks when compared to the lived experiences of history in the present.”

Bio:

Stavroula is Senior Lecturer in Social Anthropology at the University of St Andrews, Founding Director of the interdisciplinary Centre for Minorities Research and editor of the interdisciplinary book series Routledge Advances in Minority Studies. She works on minority politics, displacement, governance, and the Cold War. She is the author of “The Grecanici of Southern Italy: Governance, Violence, and Minority Politics” (University of Pennsylvania Press, 2016) and “Lurking Cold War: Life Through Historical Communion” (Berghahn 2025). Lurking Cold War explores the entangled registers of the Cold War that continue to stalk the social landscape in Italy and Greece. Critiquing the connections between global categories and individual experiences, Lurking foregrounds Cold War resonances through materiality, imagination, speculation and affect, in literature, bureaucracy and in the family. A theory of methexi illustrates how people and history are brought into communion, blurring the boundaries between known and unknown, reality and imagination, and form and interpretation. The result is an articulation of history that matters in a way that matters.

SACHI Seminar, Mark Zarb – Bridging Minds and Machines: Redefining Computing Education

We are pleased to share our upcoming SACHI seminar by Dr Mark Zarb, an Associate Professor based within the School of Computing, Engineering and Technology at RGU:

📅 26th March | 🕛 13:00 – 14:00 PM | 📍 JCB, Room 1.33A

Title:

Bridging Minds and Machines: Redefining Computing Education

Abstract:

Since 2009, Dr Zarb has been exploring the evolving landscape of pedagogical research, collecting ideas from across disciplines and trends. In this acronym-filled talk, he offers a guided tour through some of the latest research at RGU — from grappling with the ethical dilemmas posed by conversational AI in education, to exploring “shadow podcasts” as informal learning tools. We will look at practical challenges, unexpected questions and at how rapidly shifting technology continues to shape how (and why) we teach and learn.

Bio

Dr Mark Zarb is an Associate Professor based within the School of Computing, Engineering and Technology at RGU

His main research focus is within computing education, having led international working groups on transitions into higher education in 2018 and post-pandemic educational landscapes in 2021 and 2022.

He received his PhD (2014, University of Dundee) for work exploring the role of verbal communication styles in pair programming. His various roles and experiences allow him a wide and international perspective on computing education.

SACHI Seminar with Aluna Everitt – Democratising the Design and Development of Emerging Technologies

We are pleased to share our upcoming SACHI research seminar by Dr Aluna Everitt, a lecturer in the Department of Computer Science and Software Engineering at the University of Canterbury, New Zealand:

📅 Today | 🕛 12:00 – 1:00 PM | 📍 JCB, Room 1.33B

Title:

Democratising the Design and Development of Emerging Technologies

Abstract:

My research focuses on democratising the development of emerging technologies. More specifically, by establishing accessible approaches for designing and building emerging technologies such as robotics, wearables, and shape-changing interfaces. To advance the field, my research focuses not only on understanding these technologies (e.g., their design), but also how to build them (e.g., engineer them), and how to innovate with them (e.g., application). In this talk, I will go into detail about some of the projects I have worked on around this topic across the fields of HCI, Design, and Engineering.

Bio:

Dr. Aluna Everitt is a lecturer in the Department of Computer Science and Software Engineering at the University of Canterbury, New Zealand. Prior to moving to Christchurch (NZ), she was a Research Associate in the Cyber-Physical Systems group at the University of Oxford and a Junior Research Fellow at Kellogg College, University of Oxford. She was also a Senior Visiting Researcher and postdoc at the University of Bristol (BIG Lab). Dr. Everitt was awarded her PhD in Computer Science from Lancaster University, specializing in Human-Computer Interaction (HCI). As a multi-disciplinary researcher, her areas of interest and expertise lie across the fields of HCI, Design, and Engineering. She has a particular interest in conducting both quantitative and qualitative research which combines a mix of engineering fabrication approaches for iterative prototyping, together with collaborative design (co-design) to encourage users and experts from different domains to develop content and applications for the next generation of interactive hardware systems and interfaces (e.g., shape-changing displays, wearables, and robotics).

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 Mustafa Abdelwahed and Maria Andrei

The next PGR seminar is taking place this Friday 6th December at 2PM in JC 1.33a

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

Mustafa Abdelwahed:

Title: Behaviour Planning: A toolbox for diverse planning

Abstract:

Diverse planning approaches are utilised in real-world applications like risk management, automated streamed data analysis, and malware detection. These approaches aim to create diverse plans through a two-phase process. The first phase generates plans, while the second selects a subset of plans based on a diversity model. A diversity model is a function that quantifies the diversity of a given set of plans based on a provided distance function.

Unfortunately, existing diverse planning approaches do not account for those models when generating plans and struggle to explain why any two plans are different.

Existing diverse planning approaches do not account for those models when generating plans, hence struggle to explain why any two plans are different, and are limited to classical planning.

To address such limitations, we introduce Behaviour Planning, a novel toolbox that creates diverse plans based on customisable diversity models and can explain why two plans are different concerning such models.

Maria Andrei: 

Title: Leveraging Immersive Technology to Enhance Climate Communication, Education & Action

Abstract: Climate change represents one of the most pressing challenges of our time, not only in its environmental impacts, but also as a pivotal science communication problem. Despite widespread scientific consensus on the causes and mitigation strategies for climate change, public understanding remains deeply fragmented and polarized. This disconnect hinders the collective action required from individuals, organizations, and policymakers to combat global warming effectively. My research explores the potential of immersive technologies to bridge the gap between scientific knowledge and public understanding by leveraging experiential learning experiences to inspire the attitudinal and behavioural shifts necessary to address climate change.

PGR Seminar with Carla Davesa Sureda

The next PGR seminar is taking place this Friday 22nd November at 2PM in JC 1.33a

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

Title:

Towards High-Level Modelling in Automated Planning

Abstract:

Planning is a fundamental activity, arising frequently in many contexts, from daily tasks to industrial processes. The planning task consists of selecting a sequence of actions to achieve a specified goal from specified initial conditions. The Planning Domain Definition Language (PDDL) is the leading language used in the field of automated planning to model planning problems. Previous work has highlighted the limitations of PDDL, particularly in terms of its expressivity. Our interest lies in facilitating the handling of complex problems and enhancing the overall capability of automated planning systems. Unified-Planning is a Python library offering high-level API to specify planning problems and to invoke automated planners. In this paper, we present an extension of the UP library aimed at enhancing its expressivity for high-level problem modelling. In particular, we have added an array type, an expression to count booleans, and the allowance for integer parameters in actions. We show how these facilities enable natural high-level models of three classical planning problems.

Doughnuts will be available! 🍩

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.

PGR Seminar with Daniel Wyeth and Ferdia McKeogh

The next PGR seminar is taking place this Friday 15th November at 2PM in JC 1.33a

Below is a Title and Abstract for Daniel’s and Ferdia’s talks – Please do come along if you are able.

Daniel:

Deep Priors: Integrating Domain Knowledge into Deep Neural Networks

Deep neural networks represent the state of the art for learning complex functions purely from data.  There are however problems, such as medical imaging, where data is limited, and effective training of such networks is difficult.  Moreover, this requirement for large datasets represents a deficiency compared to human learning, which is able harness prior understanding to acquire new concepts with very few examples.  My work looks at methods for integrating domain knowledge into deep neural networks to guide training so that fewer examples are required.  In particular I explore probabilistic atlases and probabilistic graphical models as representations for this prior information, architectures which enable networks to use these, and the application of these to problems in medical image understanding.

Ferdia:

“Lessons Learned From Emulating Architectures”

Automatically generating fast emulators from formal architecture specifications avoids the error-prone and time-consuming effort of manually implementing an emulator. The key challenge is achieving high performance from correctness-focused specifications; extracting relevant functional semantics and performing aggressive optimisations. In this talk I will present my work thus far, and reflect on some of the unsuccessful paths of research.

Doughnuts will be available! 🍩