Mathematical foundations of AI workshop

Esmeralda S. Whitammer (opens in new tab)

Chancellor’s Fellow, Institute for Machine Learning School of Informatics, University of Edinburgh

Esmeralda S. Whitammer is a Chancellor’s Fellow in the Institute for Machine Learning at the University of Edinburgh, where she studies algorithms for probabilistic inference and generative modelling, particularly dynamics-based methods such as diffusion models, approximate sampling of Bayesian posteriors over high-dimensional and structured variables, and the use of pretrained generative models as probabilistic priors. Their work has found applications in the physical and applied sciences, including inverse imaging, remote sensing, discovery of novel biological and chemical structures, and, most recently, robot control. Dr Whitammer holds a PhD in mathematics from Yale University (2021) and was previously a postdoctoral researcher at Mila – Québec AI Institute in Montréal (2021 to 2024).

Paul Fearnhead (opens in new tab)

Professor, Lancaster University

Paul Fearnhead is Professor of Statistics at Lancaster University and currently leads the Prob_AI Hub (opens in new tab). His background is in computational and Bayesian statistics, with research encompassing novel Monte Carlo methods, fast algorithms for change-point detection; likelihood-free inference methods and inference for diffusion. He has been awarded the Guy Medals in Bronze and Silver by the Royal Statistical Society, and Cambridge University’s Adams Prize.

Varun Kanade (opens in new tab)

Associate Professor, University of Oxford

Varun Kanade’s research interests lie in theoretical machine learning and more broadly in the theory of computation. He is particularly interested in the interplay between computational and information-theoretic limitations of learning problems.

Sushrut Karmalkar

Senior Researcher, Microsoft Research Cambridge

Sushrut Karmalkar is a Senior Researcher at Microsoft Research Cambridge, specializing in machine learning theory, statistics, and robust algorithms. My current research focuses on the theoretical foundations of transformer architectures, mechanistic interpretability, and masked diffusion models. I received my Ph.D. in Computer Science from UT Austin and completed a postdoctoral fellowship at UW-Madison.

Rachel Lawrence

Senior Researcher, Microsoft Research Cambridge

Rachel Lawrence works in the Machine Intelligence area at MSR Cambridge, studying generative models and exploring the reasoning capabilities of large language models. She is interested in building trustworthy automated systems which are equipped with logical and mathematical problem-solving tools.

Chris Nemeth (opens in new tab)

Professor, Lancaster University

Chris Nemeth is Professor of Mathematical AI at Lancaster University and Co-Director of the Data Science and AI Institute (DSAIL). His research focuses on probabilistic machine learning and computational statistics, particularly scalable Bayesian inference, Monte Carlo methods, network analysis, and generative modelling.