People

Michael is DeepMind Professor of AI at the University of Oxford, Founding Scientific Director, AI at the Aithyra Institute in Vienna, and a UKRI sponsored Turing AI World-Leading Research fellow. He was previously Head of Graph Learning Research at Twitter and professor at Imperial College London. He held posts at Stanford, MIT, and Harvard. Michael has more than a decade of experience in big tech and start-up companies.

Anthea is Associate Professor in Mathematics and Machine Learning at the Department of Mathematics at Imperial College London. She works in adapting algebraic topology and algebraic geometry to data analysis, statistics and machine learning, with a focus on feasible, interpretable and computationally efficient methods. Her fields of research are topology, algebraic geometry and geometric deep learning.

Jeffrey has made broad contributions across topology, tropical geometry, and data-driven techniques in computational statistical physics. He is professor in the Department of Mathematical Sciences at Durham University and leads the Durham node of the Centre for Topographical Data Analysis. He was also Mathematics lead for the EPSRC Centres for Doctoral Training in Human-Centred AI at the University of Swansea.   

Professor of Mathematics, University of Oxford and Director of the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Heather is an applied mathematician whose research sits at the intersection of topology, algebra, statistics, and machine learning. She is also Co-Director of the Centre for Topological Data Analysis and has pioneered mathematical methods for extracting structure from complex, high-dimensional data.

Primoz Skraba is a Professor in Applied and Computational Topology. He received a PhD in Electrical Engineering from Stanford University in 2009 and has held positions at INRIA in France and the University of Nova Gorica in Slovenia. He was also a member of the AI Department at the Jozef Stefan Institute, Slovenia, overseeing multiple EU and industrial projects on applications of AI and ML in areas such as energy, finance, and government (policy).

Jacek is Professor of Pure Mathematics at the University of Southampton. His research covers topological data analysis, noncommutative geometry, K- theory of operator algebras, coarse geometry, and mathematical physics. He has founded two EPSRC funded research groups involving a wide network of collaborations, spanning mathematics, physics, medicine, genetics, computer science, and electrical engineering. 

Ran is a Professor and Chair of Mathematical Studies at the University of Aberdeen. He works in pure and applied algebraic topology and applications in neuroscience, medical imagine and ecology. He held an EPSRC grant Topological Analysis of Neural Systems and a collaboration grant with the Blue Brain Project. His approach to neural networks combines graph theory, combinatorics, algebra and topology.   

Omer Bobrowski is a Professor in Mathematical Data Science. His research primarily centres on stochastic topology and its applications in data analysis. His theoretical work studies phase transitions in stochastic topology and the distribution of noise in applied topological tools. On the applied side, he develops statistical methods for topological data analysis and explores its use in various fields.

Yue is Associate Professor in the Department of Mathematical Sciences at Durham University. He is also a UKRI Future Leaders Fellow and leading expert in tropical geometry, mathematical software, and the application of both to neural networks and problems in industry and sciences. He is a core developer of various computer algebra systems including Polymake, Singular and OSCAR. 

Jared is Professor of the Mathematics of Information at the University of Oxford. He is also Editor-in-Chief of Information and Inference: A Journal of the IMA. His work has been recognised by a Sloan Fellowship and Martin and Leverhulme Prizes. His research focuses on the design, analysis, and application of numerical algorithms for information inspired applications in signal & image processing.

Alessandro is Professor of Verification and Control in the Department of Computer Science at the University of Oxford. His research interests lie in the formal verification and optimal control of heterogeneous and complex dynamical models, built from first principles or learnt from data. Alessandro blends in techniques from machine learning and AI, such as Bayesian inference, RL and game theory. 

Gesine is Professor in Statistics at the University of Oxford. She is a Fellow of the Alan Turing Institute and a Fellow of the Institute of Mathematical Statistics. Her research concerns the probability theory and statistics of biological sequences and biological networks.
Gesine is an expert in network analysis and in probabilistic approximations and underpinnings of machine learning procedures.

Peter Grindrod CBE is CEO of Astut Ltd and Professor of Mathematics at the University of Oxford. He is a leading applied mathematician whose work has shaped the approach to data science and artificial intelligence in the UK. A founding director of the Alan Turing Institute, he champions the view that advances in AI depend on deep mathematical understanding rather than scale alone. His research connects theory with industry and society.

Ulrike is a Professor of Mathematics at Oxford University and the Director of the Isaac Newton Institute for Mathematical Sciences a Topologist and co-founder of the UK Centre for TDA. She received an EPSRC Advanced Fellowship, the Whitehead and Bessel Prizes, and was elected to the Royal Society, the German National Academy and the European Academy of Sciences as well as a Fellow of the AMS and IMA.

Coralia is Professor of Numerical Optimization at the Mathematical Institute, University of Oxford and a Fellow of Balliol College. She has been a long-standing Turing Fellow at The Alan Turing Institute for Data Science, London. Coralia’s research interests include the development, analysis, and application of nonlinear optimization algorithms such as compressed sensing, climate modelling and machine learning

Marika is a Professor of Mathematics, Physics and AI. She trained in theoretical physics under Stephen Hawking, and is currently interested in geometric machine learning for fundamental physics applications and physics-inspired methods for ML. Marika was a Turing Institute Fellow and recipient of the “Dutch ERC” Vidi. She has a long track record with start-ups, including in encryption and fintech.  

Tom is Professor of Pure Mathematics at Imperial College: a mathematician combining fundamental research in geometry with cluster-scale computation, data mining, and ML. He is currently on secondment to the Office of the Chief Scientific Adviser and a member of the Executive Committee developing the Academy for the Mathematical Sciences, with responsibility for Early Career and EDI.

Sam is Professor of Mathematics in the Mathematical Institute in Oxford, and fellow of New College. He completed his undergraduate and doctoral studies in mathematics and finance at the University of Adelaide, before moving to Oxford in 2010. His research revolves around the mathematics of probability, statistical estimation methods, decision making, and modelling in finance other application areas.

Rama is Professor of Mathematics at Oxford’s Mathematical Institute, Head of the Mathematical & Computational Finance Group. He is the Director of the Centre for Doctoral Training in Mathematics of Random Systems. His research topics include stochastic computational methods, mathematical foundations of AI, generative models and data-driven modelling in finance. Rama has advised several AI-focused start-ups.

Giuseppe is a Professor of Computer Science at the University of Oxford and a Governing Body Fellow at Green Templeton College. He was previously a Professor at the Department of Computer, Control, and Management Engineering of the University of Roma “La Sapienza”. Giuseppe is an AAAI Fellow, ACM Fellow, and EurAI Fellow. He received an ERC Advanced Grant and is on the Board of EurAI. 

Haim is Lecturer in Computer Science in the School of Electronic Engineering and Computer Science
at Queen Mary University of London and Affiliated Lecturer in the Language Technology Lab at the University of Cambridge He is a natural language processing expert whose work focuses on the information that can be extracted from the parameters of Large Language Models.

Raphael is an Associate Professor in Mathematical Programming at the Oxford Mathematical Institute and the Tanaka Fellow in Applied Mathematics at Pembroke College Oxford. His research interests lie at the interface between complexity theory, optimisation algorithms, probability theory and data science. Notable achievements include the award of the SIAM Activity Group on Optimization Prize.

Varun is Associate Professor of Computer Science at the University of Oxford and is interested in all aspects of machine learning theory. He has worked on computational learning theory, including agnostic learning, boosting, reliable learning, and fair learning. Varun has also worked on optimisation algorithms for learning and identifying computational and statistical trade-offs.

Marta is Professor of Computing Systems at the University of Oxford, where she was the first female professor in the Department of Computer Science. Renowned for pioneering probabilistic and quantitative model checking, she led development of the PRISM model checker and advances in verification for AI safety and robustness. She is a Fellow of the Royal Society, ACM, and Academia Europaea.

Marc is a Professor at the University of Oxford. He has been a recipient of an LMS Whitehead Prize (2003), a Leverhulme Prize (2006) and a Frontiers in Science Awards (2024), and was an Invited Speaker at the 2010 International Congress of Mathematicians. Marc specialises in low-dimensional topology, hyperbolic geometry and group theory. He is interested in using machine learning as a tool for pure mathematicians.

Jeroen is Professor of Applied Mathematics in the Department of Mathematics at Imperial College London: an expert in deterministic and random dynamical systems, equivariant and symplectic learning, and the dynamics of machine learning algorithms. He led the European research consortia BREUDS and CRITICS. Jeroen is on the board of the CDT Mathematics of Random Systems.

Renaud is a Professor of Networks and Nonlinear Systems at the Mathematics Institute at the University of Oxford. His research focuses on complex systems, network theory and data-driven modelling of social and brain dynamics. He is recognised for his work on community detection, temporal networks, and the mathematical analysis of collective behaviour.

Terry is the Wallis Professor of Mathematics at the University of Oxford. He was a founding member of, and then Director of, the Oxford Man Institute of Quantitative Finance. He is an expert in stochastic analysis, known for his work on rough paths and universal approximation in neural networks. He received Whitehead and Polya prizes and served as President of the London Mathematical Society.  

Mahesan is Professor of Electronics and Computer Science at the University of Southampton and has more than 30 years of experience in theoretical and applied machine learning. He has particular interest in the fields of computational biology and bioinformatics. He has worked on gene-expression analysis, DNA microarray data and protein structure prediction. He was previously Head of Computer Science at Sheffield.  

Harald is a Professor in the Mathematical Institute at the University of Oxford and a Tutorial Fellow at St Hugh’s College. He obtained his PhD from the Statslab at the University of Cambridge. He works on topics that connect recent progress in pure mathematics with real world applications such as where stochastic analysis intersects with statistics, machine learning, algebra, geometry, or topology.

Dave is a Professor of Computer Science at the University of
Oxford. His research is in formal verification, with a particular focus
on the analysis of probabilistic systems, and he leads the development of the widely used verification tools PRISM and PRISM-games. Dave’s current research interests include verification techniques for applications in AI and machine learning.

Norbert is Professor in the Department of Mathematical Sciences. A specialist in geometry, his research spans Riemannian and discrete geometry, spectral theory, graph theory, dynamical systems, and Schrödinger operators, with recent work also exploring spectra and dynamics in discrete and differential geometry and applications to X-ray crystallography, robotics and AI.  He was made Professor in 2013.

Patrick is Professor of Statistics and Machine Learning in the Department of Statistics at the University of Oxford. His research focuses on uncovering and leveraging fundamental principles in high-dimensional probability, statistics, and optimisation to develop computationally efficient and statistically optimal algorithms for machine learning and artificial intelligence.

Christoph is Professor of Applied Mathematics and an expert on stochastic simulation and control, mean-field models, and mathematical foundations of deep learning. He currently collaborates with industry and government in the areas of AI security, air traffic control, and financial market microstructure. Christoph’s fundamental research lies at the interface of control and reinforcement learning.

Justin is a Professor of Mathematics at the University of Oxford, where he is a researcher in the areas of Applied Mathematics, Machine Learning, and Financial Mathematics. He is faculty member in the Mathematical & Computational Finance, Machine Learning & Data Science, and Oxford Centre for Industrial & Applied Mathematics (OCIAM) research groups at the Mathematical Institute. 

Ruben is an Associate Professor within Mathematical Sciences at the University of Southampton where he earned his PhD in 2005. Although he has a pure mathematics background in Algebraic Topology, he is currently working in the interface between pure and applied mathematics within complexity theory, particularly mathematical aspects of complex networks, and topological data analysis.