Mathematics and Computer Science
Computer Science
Ashiq Anjum is a professor of Distributed Systems and the director of enterprise and impact. He investigates theoretical and practical solutions for data intensive distributed systems, distributed machine learning models, self-adapting digital twins and physics informed machine models for digital twins. Anjum’s research work has been funded through a number of research grants including the EPSRC projects in AI driven digital twins for net zero and clinical care and the EU funded projects on distributed intelligence, modelling and iterative analytics. He has been investigating distributed analytics platforms for the LHC data in collaboration with CERN Geneva Switzerland for the last twenty years. He closely works with telecom, aerospace, rail and automobile companies to investigate self-learning digital twins and distributed machine learning platforms for intelligently analyzing IoT data streams for accuracy, reliability, safety and capacity. He is also actively collaborating with leading VR providers to commoditise AI driven digital twins and enable real time physics informed and distributed machine learning algorithms for augmented and virtual reality environments with applications in health, aerospace and telecom.
Dr Daqi Liu’s research interests include computer vision, machine learning and bio-inspired computational models. He has published various scientific papers in top-ranked journals, including IEEE transactions on Pattern Analysis and Machine Intelligence, IEEE transactions on Neural Networks and Learning Systems, IEEE transactions on Cybernetics etc. His current research focuses on extracting the most informative knowledge from the prevalent complex multi-modal information, which aims to facilitate the better decision-making for the practitioners. Such research is extremely important and much needed in current big data era, which can be widely applied to solve various fundamental computer vision applications, e.g., object detection, semantic segmentation, scene graph generation, image captioning or visual question answering.
Dr Fabrício Góes research focuses on the application of Large Language Models (LLMs) in the field of Computational Creativity. His current work centres around evaluating creativity using LLMs in various domains such as lyrics, poetry, jokes, stories, games, and culinary recipes. Dr Fabrício Góes is also interested in studying how machines can perform human-level evaluation tasks.
Dr Furqan Aziz research focus is directed towards (deep) graph-based machine learning techniques such as graph representation learning, graph kernels, and graph convolutional neural networks and their applications in diverse scientific disciplines such as computer vision, bioinformatics, and biomedical sciences. Currently, he is working on different interdisciplinary projects that focus on developing novel graph-based algorithms and network-based machine learning approaches and exploring its applications in healthcare.
Dr Fuxiang Chen’s research focuses on utilising unstructured data with an emphasis on core Natural Language Understanding, Software Engineering, Medical Images and their practical usages. His research publications are mainly found in top-tier venues such as AAAI, EMNLP, FSE, etc. He collaborates very frequently with renown researchers from top universities such as Korea Advanced Institute of Science & Technology (KAIST), South Korea, and University of British Columbia (UBC), Canada. He also has a wealth of industrial experiences. Recently, he is working on multiple projects, including developing efficient parameter fine-tuning techniques for large language models, among others. In the past, he has worked on a myriad of interesting problems such as automated debugging and explanation, code summarization, code generation, analysis of pre-trained language models, paraphrase generation, automated test case generation, rumour detection, summarization of low-resource news, etc.
Professor Hongji Yang received the B.S. and M.S. degrees in computer science from Jilin University, China and the Ph.D. degree in computer science from Durham University, U.K. He is working as a professor at the School of Computing and Mathematica Sciences, University of Leicester. His research interests have evolved from Computer Organisation, Computer Networking, Software Engineering to Creative Computing. He has published over 500 papers and five books. He became a Golden Core Member of the IEEE Computer Society in 2010. He is the director of Creative Computing and his current main research direction is: Establishing A Framework for Artificial Creativity (AC).
Professor Zhou currently is a full Professor of Machine Learning at School of Computing and Mathematical Sciences, University of Leicester, United Kingdom. He is Deputy Director of AIDAM at University of Leicester. He has published over 500 peer-reviewed papers in the field. He was the recipient of "CVIU 2012 Most Cited Paper Award", “MIUA 2020 Best Paper Award”, “ICPRAM 2016 Best Paper Award” and was nominated for “ICPRAM 2017 Best Student Paper Award” and "MBEC 2006 Nightingale Prize". His research work has been or is being supported by UK EPSRC, ESRC, AHRC, MRC, EU, Royal Society, Leverhulme Trust, Invest NI, Puffin Trust, Alzheimer’s Research UK, Invest NI and industry.
Dr John H. Drake
His research interests lie at the interface between Computer Science, Artificial Intelligence and Operational Research, focusing on metaheuristic and evolutionary computation methods. He is currently undertaking cutting edge research at the intersection of Data Science and AI, developing intelligent decision support systems for real-world optimisation problems, automatically designing new optimisation algorithms using machine learning approaches. He is interested in most metaheuristic or evolutionary methods, with particular experience using Selection Hyper-heuristics, Memetic Algorithms, Genetic Algorithms, Genetic Programming, Grammatical Evolution, Estimation of Distribution Algorithms, to solve a variety of problems in domains such as Vehicle Routing, Additive Manufacturing and Staff Scheduling among others.
Professor Lu Liu’s research interests are AI, Data Science, Sustainable Systems and the Internet of Things, focusing on developing trustworthy and sustainable systems based on machine learning for health, Net Zero and digital manufacturing. Professor Liu has over 250 scientific publications in reputable journals and international conferences. He is named in the Stanford University list of World Top 2% Scientists 2023. Professor Liu has secured over 30 grants which are supported by UKRI/EPSRC, EU, Innovate UK, Royal Society, British Council and leading industries (e.g. BT, Royce-Royce, CGI). He received the Staff Excellence Award in Doctoral Supervision in 2018. He has been the recipient of 7 Best Paper Awards from international conferences with his students. Professor Liu is the University Turing Liaison (Academic) for Turing University Network (The Alan Turing Institute), Co-Director of Leicester Centre for Digital Manufacturing (LCDM), Deputy REF Lead for UoA 11 and Executive Committee Member of Leicester Centre for Artificial Intelligence, Data Analysis, and Modelling (AIDAM).
Marco's research focuses on applying artificial intelligence in computational creativity and computational storytelling. A pertinent application involves generating linear or interactive stories by blending AI (e.g., large language models) with narratology theories. Marco seeks to supervise research students exploring the utilization of AI technologies and methods in computational creativity and storytelling.
Rajeev Raman is a professor in the School and currently Director of Research. His research interests are in efficient and scalable algorithm and data structures and their applications to storing, querying and updating large data sets. He is particularly interested in highly space-efficient in-memory databases (see tutorial at SIGMOD 2018), and considers applications in text indexing and bioinformatics. In addition, he has a number of interests in AI and Data Science, for example mining uncertain data and studying classification problems in deep learning from a more rigorous standpoint.
Dr Free has worked at the clinical interface between healthcare and technology for many years. His research interests focus on the use of advanced data science (including AI/ML) to develop and translate models and technologies into smart data-driven tools for improving healthcare outcomes. He is particularly interested in the use and integration of complex data from different disciplines for this purpose including ‘omics data, clinical data of different types (test results, coded observations, imaging) and mobile digital health data collection. Current projects he is leading include development of AI/ML-based models to predict community acquired pneumonia outcome from complex multi-faceted hospital admission data and generic approaches for embedding logical and AI/ML models into data-driven workflows to provide trusted/interpretable clinical decision support to healthcare workers.
Siyang’s research interests lie in Affective Computing, Machine Learning and Computer Vision, especially for developing machine learning algorithms/systems for automatic human behaviour understanding (e.g., mental health and personality assessment, facial expression/emotion analysis, and graph representation learning). He recently focuses on designing state-of-the-art generative machine learning algorithms for automatic human behaviour reaction generation, and starting a new research area called ‘multiple appropriate facial reaction generation’ with multiple leading affective computing colleagues from UK, Italy, France and Spain (e.g., University of Cambridge), aiming to make digital human/virtual agents having human-style verbal and non-verbal behaviours under various human-computer interaction scenarios.
Tanya’s research activities are aimed at improving the efficiency, reliability and data processing performance of intelligent autonomous systems for mission critical applications such as space, transport and health. She has worked closely with the pioneer of the small satellite platform, Surrey Satellite Technology Ltd., and has had joint projects with Airbus Defence and Space, as well as the European Space Agency. Her work has been recognised internationally for pioneering key contributions to space computing and engineering. She has supervised 24 PhD students to successful completion as the main supervisor and has authored and co-authored around 200 scientific publications. Tanya currently has research projects available on Machine Learning enabled systems for real-time high-performance processing of remote sensing satellite imagery and instrument data.
Applied Mathematics
Alberto studies and develops theory, numerical methods, and open-source software to solve optimization problems constrained to partial differential equations. These problems arise in numerous scientific and industrial applications, and indeed Alberto collaborates with many colleagues in engineering and physics as well as with several industrial partners. Alberto is happy to supervise any student who is passionate about applied mathematics, numerical methods, and scientific computing.
Dr Bo Wang’s research focuses on statistical modelling and computation for complex data and high dimensional data, including multivariate data analysis, functional and longitudinal data analysis, Gaussian process modelling, machine learning, Bayesian statistics, and their applications in the fields of medicine, finance and mortality modelling and forecasting, etc.
Neslihan's research focuses on Natural Language Processing (NLP), Language Models, Semantic Analysis, Text Mining, Machine Learning (ML) and their practical applications. She specializes in constructing mathematical and computational frameworks to advance ML and NLP systems and designing automated decision support tools addressing practical challenges in the real world. Neslihan is interested in supervising research students on topics related to NLP, Machine Learning and Semantic Analysis.
Professor Sergei Petrovskii is interested in supervising PhD projects on applications of mathematical modelling to problems arising in ecology. Here ecology is understood in a very broad sense and includes (but not limited to) topics like species extinctions, effects of climate change on population dynamics and biodiversity, complexity of ecosystems dynamics, socio-ecology and agroecology, social dynamics and human behaviour, etc.
Pure Mathematics
Josh works on gauge theory and topological solitons; specifically, special PDEs arising from variational problems in geometry and theoretical physics. He is particularly interested in applications to nuclear physics through a model known as the Skyrme model. A focus of his work is in using tools from topology, and differential and algebraic geometry to study qualitative properties of solutions, and to classify examples. This includes analysing the geometry of the spaces of solutions, which often exhibit rich structure.
Josh is happy to supervise students with a passion for applying pure mathematical ideas (such as geometry and topology) to problems arising in mathematical physics, and as such would look for students with a good background in geometry, analysis, and with good physical intuition. Some experience with numerical computations may also be useful.