Supervisory pool
Primary StatML supervisors
Below is a list of the primary StatML supervisors, who are typically academics from Imperial’s Department of Mathematics and Oxford’s Department of Statistics.
Imperial
Niall Adams
Niall is a Professor of Statistics and has a broad research interest in pattern discovery and streaming data.
Deniz Akyildiz
Deniz is an engineer-turned-computational statistician with broad interests in the statistical and computational aspects of sampling, optimization, and stochastic filtering, particularly in machine learning. His recent focus is on developing algorithms and theory for generative models with applications to inverse problems.
Randolf Altmeyer
Randolf’s research interests include Statistics for Stochastic Processes, Bayesian and Nonparametric Statistics, Stochastic Analysis and Statistics on Networks.
Mauricio Barahona
Mauricio is a Professor of Biomathematics and has a broad interest in applied mathematics in biology, physics, and engineering.
Heather Battey
Heather Battey is a reader in statistics with interests in statistical theory, particularly the structure of models and their parametrisations, and how these interact with properties of inferential procedures.

Dean Bodenham
Dean is a Lecturer in Statistics with an interest in streaming data analysis and changepoint detection.Â
Barbara Bravi
Barbara is a Lecturer in Biomathematics with a broad interest in applications in biomedicine and bioinformatics.Â
Ed Cohen
Ed is a Director of StatML and has a broad interest in statistical methodology for the analysis of signals and images. This includes temporal, spatial and spatio-temporal point processes on complex domains such as networks and manifolds, time series analysis, change-point detection, and online-estimation. He works on a diverse set of applications, with specialities in biological imaging. He is a project-lead on the NeST Programme.
Andrew Duncan
Andrew in a Senior Lecturer in Statistics and Data Centric Engineering. His research interests like at the interface of applied probability, computational statistics and machine learning, with a particular focus on industrial applications. He has worked on application areas ranging from cellular biology, chemical engineering, predictive health management for complex engineering systems, aerospace and energy.
Phillip Ernst
Phillip is a Professor of Statistics and a Royal Society Wolfson Fellow with a broad interest in stochastic processes.Â
Marina Evangelou
Marina’s main research interests are in statistical methods for high dimensional and complex data in biomedicine.Â
Sarah Filippi
Sarah is a Director of StatML and has a broad interest in statistical modelling and machine learning methods, their theoretical properties and their application to biomedical problems (from computational biology and cellular biology to epidemiology and clinical studies). She is particularly interested in Bayesian statistics and nonparametric methods, decision making process under uncertainty and scalable Gaussian Processes.
Axel Gandy
Axel is a Professor of Statistics with an interest in Bayesian models, networks and survival analysis. Â
Nicola Gnecco
Niccola is a Lecturer in Statistics and a faculty member at I-X, an Imperial’s cross-department initiative in AI. His vision is to develop statistical models that are flexible enough to account for recent advances in machine learning while benefiting from causality and extreme value theory for better robustness and extrapolation. His research spans two directions: distribution generalization from a causal perspective and causal inference for extremes.Â
Nick Heard
Nick’s main current research interests are in statistical modelling of dynamic network graphs, changepoint analysis and anomaly detection, with application areas including connectomes in neuroscience and enterprise cyber-security.
Nick Jones
Nick’s interests lie in Statistical genetics and mtDNA somatic ageing in neurodegeneration. Applied Bayesian Machine Learning and feature engineering.
Nikolas Kantas
 Nikolas is a Reader in Statistics with a broad interest in optimisation and control problems.Â
Alessandra Luati
Alessandra is a Professor of Statistic. Her main research interests are concerned with time series analysis and mathematical statistics.
Anthea Monod
Anthea works in the areas of topological data analysis and algebraic statistics, which adapt algebraic topology and algebraic geometry to computation, statistics, data analysis, and machine learning. She is the Co-Director of the EPSRC “Erlangen Programme” Mathematical and Computational Foundations of Artificial Intelligence Hub, which leverages algebra, geometry, and topology to understanding the theoretical underpinnings of deep learning and AI.Â
Daniel Mortlock
Daniel is interested in all aspects of Bayesian inference, with a particular focus on foundational issues, prior specification, model robustness and censored data. He is also interested in the realistic model specification for scientific data-sets, in particular in astronomy and physics.Â
Johanes Muhle-Karbe
Johannes is the Head of the Mathematical Finance Section and the Co-Director of the Imperial Centre of Excellence in Quantitative Finance. His research focuses on stochastic control and its applications in economics and finance, in particular, market microstructure.Â
Guy Nason
Guy is Chair in Statistics at Imperial with broad research interests, but the majority of my time is spent on time series, mostly network time series. Most of my research is connected to the vast amount of exciting research activity happening in the NeST Programme Grant and most of my students are affiliated with the research thrust.
Milkko Pakkanen
Mikko is a Reader in Data Science and Quantitative Finance.
Ricardo Passeggeri
Ricardo is a Lecturer in Statistics and has a broad interest in Probability and Statistics.Â
Ciara Pike Burke
Ciara’s research focuses on developing statistically efficient algorithms for sequential decision-making problems, including variants of the multi-armed bandit, reinforcement learning and online learning problems.Â
c.pike-burke@imperial.ac.uk
Oliver Ratmann
Oliver is passionate about developing and applying statistical and machine learning methods to tackle the grand challenges in society, global health research, and support underserved populations. His experience and research lie in statistical machine learning, Bayesian statistics, infectious disease modelling, child health, cardiovascular diseases, phylodynamics, non-parametric statistics, probabilistic computing languages, survey and sampling methods, human mobility and behaviour, and extreme weather modelling.Â
Kolyan Ray
olyan’s research focuses on the mathematical theory of Bayesian methods in high- and infinite-dimensional models (`Bayesian nonparametrics’) with applications to methodology. Areas of interest include uncertainty quantification, causal inference, variational inference, inverse problems and asymptotic statistics.Â
Robin Ryder
Robin specializes in computational Bayesian statistics (Markov Chain Monte Carlo and Approximate Bayesian Computation) and on stochastic modelling for the social sciences (Historical Linguistics, Animal communication, Cultural evolution, Sociology…)
Cris Salvi
Since 2021, Cris has been a member of the Department of Mathematics at Imperial College London, first as a Chapman Postdoctoral Fellow and now as an Assistant Professor. He is also affiliated with Imperial-X, the college’s flagship institute for AI and data science. Prior to this, he completed my PhD at the University of Oxford under the supervision of Prof. Terry Lyons. His research interests lie in stochastic analysis and deep learning.
Francesco Sanna Passino
Francesco’s research interests are broadly based on statistical analysis of dynamic networks, mostly from a Bayesian perspective. His work focuses on spectral embedding methods and low-rank structures in networks, model-based clustering, and probabilistic approaches for inferring latent structures in evolving graphs.
Adam Sykulski
Adam’s research is in time series, spatial, and spatio-temporal statistics, with an application focus in climate and ocean sciences.
Yanbo Tang
Yanbo is a Lecturer in Statistics with a research interest in higher order asymptotic.Â
Felipe Tobar
Felipe is a Senior Lecturer in Machine Learning, and his research spans Gaussian processes, optimal transport, diffusion models and Bayesian signal processing. He also works on applications to astronomy, health, finance, climate and audio. Felipe has taught courses on Probability, Statistics, (Advanced) Machine Learning and Scientific computing in undergraduate, graduate and executive education programmes.Â
David van Dyk
David is a Professor of Statistics with a broad interest in astrostatistics.
Almut Veraart
Almut is a Professor of Statistics and has a broad interest in statistics for stochastic processes.Â
Andrew Walden
Andrew’s main research interests are time series and graphical modelling.
Alastair Young
Alastair has been Professor of Statistics at Imperial since 2005, when he joined from the Statistical Laboratory of the University of Cambridge. He has research interests in contemporary statistical theory and methodology, in particular in information splitting approaches to selective inference.Â
Kelly Zhang
Kelly is a Lecturer in Statistics and I-X, specializing in reinforcement learning and adaptive experimentation for healthcare and recommendation systems. She is particularly interested in exploring how deep learning and foundation models can enhance decision-making algorithms in these critical domains.Â
Oxford
Julien Berestycki
Probability: tree-like structures and branching phenomena
François Caron
François is a Professor of Statistics at the University of Oxford. His research interests include Bayesian parametric and nonparametric methods, statistical machine learning, and network analysis.
Charlotte Deane
Proteomics and drug discovery
George Deligiannidis
Probability applied to theory of machine learning
Christl Donnelly
Professor Christl Donnelly studies the statistical epidemiology of infectious diseases to understand disease transmission and control. Her group’s work varies widely in terms of methods and applications, with many projects aiming to provide robust evidence to policymakers.
Alison Etheridge
infinite dimensional stochastic analysis, mathematical ecology and population genetics
Robin Evans
Causal inference and discovery, realistic causal data simulation (including using deep generative methods), graphical models, latent variables, and algebraic statistics. Applications to medicine, epidemiology, and social sciences
Jotun Hein
Stochastic and Algorithmic Aspects of Molecular Evolution, Origins of Life and Population Genetics
Chris Holmes
Causal inference, Bayesian statistics
Ana Ignatieva
Ana’s research lies at the intersection of probability, statistics and computation, motivated by applications in statistical and population genetics. Her work focuses on developing methods and tools for understanding evolution by analysing genetic sequencing data, for humans and other species.
anastasia.ignatieva@stats.ox.ac.uk
https://www.stats.ox.ac.uk/people/professor-anastasia-ignatieva
Ben Lambert
Computational methods for biological systems
James Martin
James’s research is in probability theory, with strong links to statistical physics and theoretical computer science. Particular interests include: random graphs; interacting particle systems; models of random growth and percolation; queueing networks; combinatorial games.
Garrett Morris
Garrett’s research is focused on the development of novel methods in computer-aided drug discovery, molecular docking, virtual screening, cheminformatics and bioinformatics. He is particularly interested in the unification of physics, chemistry, and machine learning to improve generalizability, active learning, deep learning, Bayesian optimization, explainable AI, and generative AI.
Simon Myers
Geoff Nicholls
Geoff did a PhD in Theoretical Physics at Cambridge and a postdoc in the Vision, Speech and Signal Processing lab in Engineering at the University of Surrey.
He got into statistics as a postdoc in Oxford in the early 90’s working with Peter Clifford.
Nowadays Geoff is an Associate Professor in the Statistics Department at Oxford doing research
on statistical methods, computational statistics and machine learning.
Thomas Nichols
Dr. Nichols is the Professor of Neuroimaging Statistics at the Oxford Big Data Institute. He works on multiple testing, spatial modelling, meta-analysis and genetics, all with the context of brain image data. Â
Pier Palamara
Tom Rainforth
Prof Tom Rainforth is an Associate Professor of Statistical Machine Learning based in the Department of Statistics at the University of Oxford and leader of the RainML research lab (see http://www.rainml.uk). His research covers a wide range of topics in machine learning and experimental design, with areas of particular interest including Bayesian experimental design, deep learning, active learning, probabilistic machine learning, and uncertainty quantification.
Patrick Rebeschini
Professor of Statistics and Machine Learning – He is interested in the investigation of fundamental principles in high-dimensional probability, statistics and optimization to design computationally efficient and statistically optimal algorithms for machine learning.
Gesine Reinert
Gesine Reinert has been at the Department of Statistics, and Keble College, since 2000, after positions at Cambridge, UCLA, and USC, Los Angeles. Her core expertise is statistical network analysis and Stein’s method, extending to topological data analysis, probabilistic methods for machine learning, and graph neural networks.
Tom Snijders
Tom Snijders studied mathematical statistics at the University of Groningen and obtained his PhD (cum laude) in 1979. He is Emeritus Professor of Statistics in the Social Sciences in Oxford and Emeritus Professor of Statistics and Methodology in Groningen. His main research areas are methods for social network analysis and multilevel analysis. He is the maintainer of the RSiena package in R. In 2022 he obtained the Paul L. Lazarsfeld award of the Methodology Section of the American Sociological Association.
David Steinsaltz
David Steinsaltz has a background in stochastic process theory, and has been working since 2007 in the Oxford University Department of Statistics on applications to biodemography, survival analysis, and genomics. Most recently his work has concentrated on Bayesian computational methods and stochastic decoupling for massive multiple testing problems, as well as statistical methods for climate science
Yee Whye Teh
Yee Whye is a Professor of Statistical Machine Learning and a Research Scientist at Google DeepMind. He is interested in statistical machine learning, deep generative models, deep learning, and their intersections. He is also interested in supporting DE&I and outreach activities at the university and beyond.
Mark van der Wilk
Machine learning, generalisation
Frank Windmeijer
Frank graduated from the University of Amsterdam, and has held positions at the ANU in Canberra, UCL and IFS in London, and the University of Bristol, before joining the Oxford Statistics department in 2020. His main research interest is causal inference, with a particular focus on instrumental variables estimation and selection of valid/invalid instruments.
Affiliated StatML supervisors
Below is a list of affiliated StatML supervisors, consisting of academics from various departments at Imperial and Oxford who co-supervise StatML students.
Imperial
Yingzhen Li
Yingzhen is a Senior Lecturer in Machine Learning in the Department of Computing. Her main research interests are in approximate inference and Bayesian deep generative models.Â
Ruth Misener
Ruth is a Professor of Computational Optimisation . Her research team focuses on computational optimisation challenges arising in industry, for example scheduling in manufacturing or experimental design in chemicals research.
Ioanna Papatsouma
Ioanna is a Senior Teaching Fellow in Statistics. Her research interests lie broadly in the area of clustering; developing methodology for clustering mixed-type data, constrained clustering and robust clustering.Â
Calvin Tsay
Calvin is a Lecturer in the Computational Optimisation Group within the Department of Computing. His research interests are in the areas of optimisation algorithms, process dynamics/control, optimisation for machine learning, and process systems engineering.Â
Antonio Del Rio Chanona
Antonio is the head of the Optimisation and Machine Learning for Process Systems Engineering group at the Department of Chemical Engineering, and the Centre for Process Systems Engineering.Â
Kevin Webster
Kevin is a Senior Teaching Fellow whose research interests are in the areas of machine learning, deep learning, dynamical systems, statistical learning theory and music information retrieval.Â
Oxford
Seth Flaxman
Dr Flaxman (www.sethrf.com) is an associate professor in the Department of Computer Science. His research is on scalable methods and flexible models for spatiotemporal statistics and Bayesian machine learning, applied to public policy and global health in collaboration with the Machine Learning & Global Health network (www.MLGH.net) which he helps lead.
Habib Ganjgahi
Fergus Imrie
Fergus is a Florence Nightingale Bicentenary Fellow in the Department of Statistics at the University of Oxford. His research focuses on developing machine learning methods and techniques for medicine and drug discovery. He is particularly interested in experimental design and decision-making, self-/semi-supervised learning, domain adaptation, and causality.
Nick Irons
Nick Irons is a Florence Nightingale Fellow in Computational Statistics and Machine Learning within the Department of Statistics and the Leverhulme Centre for Demographic Science. His research develops methods and applications of (primarily Bayesian) statistics to inform decision-making in the health and social sciences, and his interests in statistics include causal inference, model selection and hypothesis testing, nonparametric and high-dimensional models, design and analysis of experiments, and modeling of complex data (e.g., hierarchical, spatiotemporal, mechanistic, and infectious disease models).
Desi R. Ivanova
Bayesian experimental design
David Janz
Sequential decisions, Bayesian optimisation
Varun Kanade
Varun Kanade is an Associate Professor in the Department of Computer Science, Oxford, with research interests in machine learning theory, optimization, foundation models and deep learning, and randomized algorithms.
Rebecca Lewis
Rebecca’s research lies in statistical theory and methods, where she works on problems in high-dimensional and non-parametric settings.
George Nicholson
George is interested in statistical machine learning methods that enhance scalable healthcare solutions, leveraging multimodal biomedical data sets (e.g., genetic, molecular, primary care, imaging, wearables) to help understand and predict disease, and optimize personalized treatments.
Agni Orfanoudaki
Tingting Zhu
Associate Professor in AI for Digital Health – Tingting specialises in developing methodologies for analysing multimodal heterogeneous time-series patient data from wearables, primary, and secondary care. Her work focuses on generative modelling, risk profiling and prediction, treatment effect estimation, and recommendation systems.