Postgraduate

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.

PGR Seminar with Leonid Nosovitsky + Xinya Gong

The next PGR seminar is taking place this Friday 11th April at 2PM in JC 1.33a

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

Leonid Nosovitsky

Title: Bridging Theory and Practice: Advancing Multiparty Session Types for Industry Use

Abstract: Multiparty Session Types (MPST) provide a typing discipline for communication protocols. They allow to statically check that a code implementation conforms to a specified protocol; they can also verify that a protocol satisfies many safety properties like liveness and deadlock freedom, which are crucial in concurrent communicating systems.  Despite huge improvements in MPST research, different extensions have limitations.  For example, one of extensions is crash-handling. This branch was motivated by lack of network reliability to make MPST framework more applicable and usable in industrial scenarios. The crash-semantics theory introduced by Barwell et al. does not involve any constraints relaxations, which makes it very intricate for adoption in practical scenarios.  Our project concentrates on addressing usability limitations to make MPST more integrable into industrial applications.

Xinya Gong

Title and Abstract TBC

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.

Distinguished Lecture Series 2025

This years Distinguished Lecture series was delivered yesterday ( Tuesday 1st April) by Professor Arthur Zimek, University of Southern Denmark in Odense, Denmark.

In his talk on, ‘Data Mining and the “Curse of Dimensionality”’ he considered the challenges of the “curse” from the perspective of data mining. In Talk 1, he discussed the “curse” in more detail, identifying relevant aspects or problems. In Talk 2, he considered clustering facing these problems and discussed some strategies and example methods for subspace clustering. In Talk 3, he discussed outlier detection, considering strategies for improved efficiency, effectiveness, and subspace outlier detection.

PGR Seminar with Joe Loughney

The next PGR seminar is taking place this Friday 28th March at 2PM in JC 1.33a

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

Title: Flexible-Order Symmetry Breaking in the Subgraph Isomorphism Problem

Abstract: The Subgraph Isomorphism Problem has many applications, including bioinformatics, computer vision and graph databases. Current state-of-the-art solvers using constraints programming techniques can handle cases with up to 1000 pattern vertices and 10,000 target vertices. We explore various approaches to variable and value symmetry breaking in the problem (and viable strategies to combine the two), implemented in the Glasgow Subgraph Solver, and introduce the notion of ‘flexible ordering’ on symmetry breaking constraints.

PGR Seminar by Constantine Theocharis + Yigit Yazicilar

The next PGR seminar is taking place this Friday 21st March at 2PM in JC 1.33a

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

Constantine Theocharis

Title: Efficient Programs with Dependent Types

Abstract:

Dependent types allow us to program using the full power of set theory at our disposal. We can encode conditions of arbitrary complexity, and then show that these conditions are met by our programs, statically. While this paradigm is very effective for verifying systems, often their real-world implementations are done in languages without these verification capabilities, because they produce more efficient programs. In this talk, I will explore some of the main sources of inefficiency in (functional) languages with dependent types, and some work that aims to mitigate these, so that verification and implementation can happen in the same language. A common pattern in these languages is to have ‘refinements’ of data which carry along with them proofs of the properties we care about. The first piece of work is about how to make these refinements true zero-cost abstractions. Another source of inefficiency is that these languages must heap allocate almost everything since the sizes of types cannot always be known at compile time. The second piece of work is about how to keep track of type sizes as part of the type system, so that all heap allocations are explicit and unnecessary for the most part.

Yigit Yazicilar

Title: Automated Nogood-Filtered Fine-Grained Streamlining

Abstract:
We present an automated method to enhance constraint models through fine-grained streamlining, leveraging nogood information from learning solvers. This approach reformulates the streamlining process by filtering streamliners based on nogood data from the SAT solver CaDiCaL. Our method generates candidate streamliners from high-level Essence specifications, constructs a streamliner portfolio using Monte Carlo Tree Search, and applies these to unseen problem instances. The key innovation lies in utilising learnt clauses to guide streamliner filtering, effectively reformulating the original model to focus on areas of high search activity. We demonstrate our approach on the Covering Array Problem, achieving significant speedup compared to the state-of-the-art coarse-grained method. This work not only enhances solver efficiency but also provides new insights into automated model reformulation, with potential applications across a wide range of constraint satisfaction problems.

PGR Seminar with Mirza Hossain

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

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

Title: BioFuse: Optimizing Biomedical Embeddings with Foundation Models

Abstract: Pre-trained foundation models have revolutionized biomedical AI, excelling in specialized domains like radiology and histopathology. However, integrating multiple models remains a challenge due to compatibility and feature fusion issues. BioFuse is an open-source framework designed to optimize biomedical embeddings by automatically selecting and fusing the best model combinations. Leveraging 9 state-of-the-art foundation models and a grid search strategy, BioFuse generates task-specific embeddings that improve downstream classification. On the MedMNIST+ benchmark, it achieves SOTA AUC in 5/12 datasets while maintaining near-SOTA performance in others. Surprisingly, our experiments reveal strong cross-modal capabilities, where models trained on one modality perform well on others. With a high-level API and an extensible architecture, BioFuse streamlines model integration and paves the way for new insights in biomedical data fusion.

PhD student project showcase in CyberASAPY8 Demo Day

A group of PhD students: Yaxiong Lei and Zihang Zhang, in our school have been awarded a CyberASAP project. This is funded by the Department of Science, Innovation, and Technology (DSIT) and organised by InnovateUK. CyberASAP aims to fund innovative cybersecurity solutions from academics. Their project, LockEyeGaze, confronts the cybersecurity challenge of sophisticated computer vision and 3D modelling technologies such as deepfake and AI-generated tampering. They are leveraging the dynamic patterns of eye movements for security, which are significantly more difficult to replicate than static biometric features like static face, iris and fingerprints. Their project is selected to present at CyberASAP Year 8 Demo Day in Canary Wharf, London today.

Links:

https://web-eur.cvent.com/event/4a986031-294f-4ad0-9a9b-a4863690bd19/summary

https://iuk-business-connect.org.uk/events/cyberasap-year-8-demo-day/