Postgraduate research
Mathematics and Computer Science
Computer Science
- Data Intensive Distributed Systems for High Performance Analytics
- Distributed, Scalable and Parallel Machine Learning Models
- System Models for Self-Adapting and federated networks of Digital Twins
- Physics Informed Neural Networks for Trustworthy Digital Twins to emulate the behaviour of Cyber-Physical Systems
- Graph Representation Learning
- Spectral Graph Theory
- Computer Vision
- Pattern Recognition
- Machine Learning in Healthcare
- Scalable data integration with generative AI
- Model-driven software engineering and low-code development for scalable applications
- AI and large language models (LLMs) in software engineering
- Testing and verification with model-based techniques and rewriting systems
- Health and assistive technologies focused on physical activity and vision-related innovations
- AI for Software Engineering e.g., automatic code generation, code summarization, test case generation, empirical studies, etc.
- Community detection in social media networks
- Machine Learning / Deep Learning for Semantic Parsing – natural text analysis and generation, including paraphrase generation and Question Answering (QA)
- Machine Learning / Deep Learning for Natural Language Processing (NLP) application
- Machine Learning / Deep Learning for Visual QA – image analysis, including medical images
- Image generation and translation via Machine Learning / Deep Learning
- Machine Learning / Deep Learning for code summarization, program debugging and test case generation
- Social media analysis and text mining
- Theory of Distributed System and Blockchains
- Trusted and Distributed AI Systems
- AI Robustness and Security
- Creative AI Computation and Models (ML and RL)
- Formal Methods, Stochastic Modelling and Optimization
- Categorical Type Theory
- Programming Language Semantics
- Logics and their Application to Computer Science
- Multilanguages and their Semantics
Dr John H. Drake
- Automatic design of optimization algorithms using machine learning approaches
- Intelligent decision support systems for real-world optimization problems
- Meta- and Hyper-heuristics
- Evolutionary computation
- Scheduling and timetabling
- Advanced AI-based approaches for disease prevention and prediction, including digital twins
- Software for implementation of AI-based clinical decision support tools into healthcare settings
- Exploring the role of data science and AI in healthcare systems
- Online algorithms
- Scheduling algorithms
- Design and analysis of algorithms
- Computational Creativity (Generation and Evaluation)
- Generative AI Multi-Agents Networks
- Large Language Models and Prompt Engineering applied to Creative Domain (Jokes, Poetry and Stories)
- AI applied to Digital Culture, Humanities and Arts
- Spacecraft Guidance, Navigation, and Control System
- Reinforcement Learning and Embodied Intelligence
- Robotics
- Digital and Intelligent Manufacturing
- Graph transformation
- Model-based software engineering
- Network modelling and analysis
- Graph neural networks
- Smart contracts
- Human-Computer Interaction (HCI)
- User Experience (UX)
- Usability
- Participatory Design (PD)
- technology-enhanced learning (TEL)
- Reversible computation of programming languages
- Concurrent execution
- Computer Vision: Video Analysis, Action Recognition, Pose Estimation, Graph Matching, Centerline Detection, 3D reconstruction
- Machine Learning: Deep Learning, Self-Supervised Learning, Weakly-Supervised Learning, Graph Learning
- Human-Computer Interaction (User-Centred Design (UCD) and evaluation methods and metrics, interaction theories development, User Experience (UX) Design, Problem Solving, Decision-Making, Sensemaking)
- Human-Data Interaction (User-centric data visualizations and decision-support systems design and evaluation, Problem Solving, Decision-Making, Sensemaking)
- User-centred AI (trust, verifiability measures and methods, autonomy vs. Transitional autonomy, interpretability and explainability, communications with Public)
- Mixed-Reality/Digital Twins and embedding Synaesthesia, Multimodality and Serendipity to improve human performance and UX
- Cognitive Computing, Mental Models, Optimization and Heuristics design
- Sociotechnical systems Design, Design Thinking, Data-driven Intelligent Service Design (e.g. Intelligent Mobility and Healthcare), Human Factors and Ergonomics
Wentao Li
- Big data processing (graph processing, distributed/parallel algorithms)
- Graph mining (graph neural networks, recommender systems)
- Vector databases and foundation models
- Computer Vision: Object Detection, Semantic Segmentation, Scene Graph Generation, Image Captioning, Visual Question Answering
- Machine Learning: Deep Learning, Graph Neural Network, Transformer, Self-supervised Learning, Variational Bayesian, Diffusion Models
- Leveraging AI for Higher Education (Teaching, Learning, and Assessment Design)
- Management Information Systems (Technology and Innovation Frameworks, Decision-making, and Knowledge Management)
- Artificial Intelligence for Business Information Systems’ Applications
- Cloud Computing Complex Decision-making and Management
- Adaptive Learning Systems for Higher Education
- Computer Vision: AI-Generated Content, Visual Geometry, Scene Understanding, Representation Learning, Video Understanding
- Machine Learning:Deep Learning, Self-Supervised Learning, Knowledge Distillation, Network Compression, Graph Learning
- Analysis and design of algorithms
- Data structures for space-efficient in-memory data processing
- Mining uncertain data
- Fundamentals of deep learning
Dr Yann Savoye
- Computer Graphics (geometry, rendering, animation, computational design)
- Computer Vision and Image Processing
- Creative Visual AI and Generative Deep Learning
- Pure Mathematics and Numerical Optimization
- Emerging Immersive Technology / Virtual Reality / Augmented Reality / Interaction
- Intelligent high-performance embedded computing systems
- AI and machine learning for image and data processing
- Fault-tolerant distributed computing
- Hardware acceleration using reconfigurable systems such as Field Programmable Gate Arrays (FPGAs)
- Wireless sensor networks
- Computational Creativity
- Computational Storytelling
- Applications of Large Language Models
- Automated Reasoning
- Creative Computing
- Software Engineering
- Internetworking
- Visual neural networks, bio-inspired vision systems, robotic vision
- Spiking neural networks, bio-plausible neural circuits, neural system modelling
- Machine learning, collision detection, facial recognition, road signposts recognition
- Robotics, swarm robots, manipulation, navigation, path integration, grasping
- Low carbon AI, neuromorphic circuits, neural circuits realization, bio-robotics
- Autonomous vehicle, safety, collision avoidance, multimodality sensing
- Foundational artificial intelligence modelling
- Probability and statistical modelling
- Machine learning applications
- Lightweight AI modelling and implementation
Mathematics
- analysis of non-linear dynamical systems motivated by biology and ecology
- dynamical behaviour on networks and the effects of network structure
- generalisations of reaction-diffusion equations in hierarchical geometries
- modelling and analysis of animal movement
- Topological solitons
- Mathematical methods for nuclear systems in the Skyrme model
- Nahm transforms and constructions of self-dual Yang-Mills fields
- Differential geometry of field theories and moduli spaces
- Adiabatic approximations in field theory
- Computer simulation of materials, including solid-liquid interfaces and confined liquids
- Development of computational methods for studying high-dimensional dynamical systems
- Studying collisions of nanoparticles using topological data analysis
- Spectral methods for partial differential equations
- Orthogonal polynomials
- Computational complex analysis
- Numerical methods for differential equations with complex variables
- Mathematical methods for AI and machine learning
- Applications of AI in health, natural language processing, and theorem proving
- Financial mathematics
- Diophantine equations
- Exposition of mathematics
- Self-validated numerical algorithms, rigorous numerics
- Floating point arithmetic and error analysis
- Low-rank approximation of matrices, tensors and multivariate functions
- Computational electromagnetism
- Coupled physics problems
- Inverse problems
- Reduced order models
- Discrete Geometry
- Integrable Systems
- Surface Theory
- Shape Optimisation
- Quaternionic Holomorphic Geometry
• Linking Climate Variability and Change: Use of response theory for performing climate change projections in climate models of different level of complexity
• Tipping Points and Metastability of the Climate System: Critical transitions in the Earth's climate and noise-induced transitions between competing states defined by a dynamical landscape.
• Nonequilibrium Systems: Theoretical advances and numerical testing of linear and nonlinear response formulas for complex systems and Parametrizations for Multiscale processes:
• Extreme Events: Theory and Applications: Development of extreme value theory for chaotic systems and use of large deviation theory for studying persistent extremes in geophysical systems.
- Shape optimization
- PDE-constrained optimization
- Finite elements
- Mathematics of mass extinctions
- Mathematical models of tipping points and regime shifts in population dynamics and climate
- Biologically inspired diffusion-reaction equations
- Models of biological invasion
- Nonlinear dynamics, bifurcations, chaos
- Natural Language Processing (NLP) and Text Mining
- Application of network science to uncover relationships and structures within text data
- Machine Learning Applications in Actuarial Sciences
- Integral operators
- Asymptotic methods
- Wave propagation in complex media
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Stochastic differential equations and stochastic dynamics
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Non-smooth dynamical systems
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Metastable phenomena
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Dynamical systems driven by non-Gaussian Lévy motion
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Vibro-impact systems in Energy Harvesting
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Climate Modelling
- Natural Language Processing
- Semantic Analysis
- Machine Learning
- Text Mining
- Language Models
- Statistical modelling of complex data and high dimensional data
- Multivariate, functional and longitudinal data analysis
- Machine learning and neural networks
- Mortality modelling and forecasting