The research of our group focuses on the mathematical aspects of Data Science, and aims to tackle challenges arising in this interdisciplinary field by drawing on methods and theories from Statistics, Applied Mathematics, Computational Science and their related areas.
The members of our group work on a vast variety of inter-connected topics, including Applied Probability, Econometrics, Machine Learning, Medical Statistics, Non-parametric Statistics, Numerical Analysis, Optimisation, Statistical Decision Theory, and Uncertainty Quantification. The specific research interests of our group members are listed below.
The Data Science group hosts a weekly Data Science and Computational Statistics seminar series.
The group benefits from the University of Birmingham's membership of the The Alan Turing Institute, the UK's national institute for data science and artificial intelligence, and interacts closely with the Institute for Interdisciplinary Data Science and AI.
Head of Data Science Group
Professor
Dr Li has research interests in scientific computing, computational statistics and uncertainty quantification.
Academic Staff
Lecturer
Biman Chakraborty is a Lecturer in Statistics. He has published several research papers in premier International Statistics journals as well as reviews and book chapters in nonparametric multivariate statistics, statistical computing and statistical reliability. He acts as reviewer to many International journals regularly. He also received many research grants from various agencies in his career.
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Associate Professor
Dr Duong's research interests lie in the intersections of analysis and applied probability. Most of his research is inspired from applications in molecular dynamics, material sciences and biological systems.
Assistant Professor
Dr Jiang's research interests are: inverse problems and imaging; model order reduction; uncertainty quantification.
Reader in Statistics and Econometrics
Dr Li works in environmental and natural resource economics, and applied econometrics.
Lecturer
Dr Lionnet's research interests span theoretical and numerical analysis of Backward Stochastic Differential Equations (BSDEs), probabilistic numerical methods for PDEs, and systemic risk in financial networks.
Assistant Professor in Applied Mathematics and Statistics
Dr Sachs's research interests are in numerical analysis of stochastic differential equations; computational statistics, the design and analysis of Markov Chain Monte Carlo methods; machine learning methods for molecular systems/dynamics.
Assistant Professor in Mathematics
Lecturer in Mathematics and Statistics
Dr Shang's primary research interests lie in the optimal design of numerical methods for stochastic differential equations with a strong emphasis on applications ranging from computational mathematics, statistics, physics, to data science.
Assistant Professor
Dr Shao's principal research interests are in the intersection of financial and actuarial mathematics: quantitative analysis of Insurance-Linked Securities (ILS), Extreme Value Theory (EVT) and heavy-tailed distributions, with an emphasis on pricing catastrophe risk (CAT) bonds, nuclear power-linked securities and applications.
Assistant Professor
Dr Tang's research interests include large-scale optimisation, statistical learning theory, and applications in computer vision and medical imaging.
Lecturer in Mathematics and Statistics
Dr Touloupou's research is concerned with mathematical modelling of infectious diseases and the development of novel statistical methods needed for model fitting and model selection.
Associate Professor
Dr Zhang's research lies in Markov Decision Processes and Stochastic Games, Markov Chains and Pure Jump Processes, Statistical Decision Theory and Sequential Analysis, and applications to e.g., Impulse Control Problems and Telecommunication.
Research and Teaching Fellows
Research Fellow in Mathematical Analysis
Dr Buze's research interests range from analysis of PDEs with links to optimal transport theory, to mathematical aspects of materials science, with particular focus on atomistic-scale based modelling of materials.
Research Fellow
Mr Marsh's research interests cover inference for stochastic epidemic models, Bayesian computation, Markov chain Monte Carlo methodologies and data augmentation techniques, and inference for complex high dimensional genetic data.
PhD Students
Note that not all PhD researchers have requested profiles.
Affiliated Academic Staff
Professor of Biostatistics
Institute of Applied Health Research
Professor of Machine Learning
Institute of Metabolism and Systems Research