Thomas Cass

Thomas Cass is Professor of Mathematics at Imperial College London. At Imperial, he directs the EPSRC Centre for Doctoral Training (CDT) in the Mathematics of Random Systems, a jointly-run project with the University of Oxford, which trains the next generation of inter-disciplinary researchers in probabilistic modelling and algorithms. He is also a member of the Leadership Team for DataSig I, a six-year EPSRC Programme Grant (2019-2025) in the UK that develops mathematical and computational tools for multi-modal data. His team have pioneered the use of high order signature methods from rough path theory as a new approach in mathematical data science. In March 2025, he and his team were awarded another large EPSRC Programme Grant, beginning in October 2025, to advance these foundations. DataSig II will develop the mathematical and computational infrastructure for streamed data using rough analysis in the era of Large Language Models (LLMs).

 

Professor Cass completed his PhD at the Statistical Laboratory at the University of Cambridge in 2007. He then moved to the Mathematical Institute at University of Oxford (2008-2011), during which period he was also a Fellow and Member of the Governing Body of Christ Church, Oxford. He joined Imperial in 2011. He has been a Visiting Researcher at the Alan Turing Institute in London since 2019, and was the Erik Ellentuck Fellow at the Institute for Advanced Study (IAS) in Princeton in the academic year 2023-24.

 

Professor Cass’s research interests span pure and applied mathematics with a focus on probability, stochastic analysis, mathematical data science and mathematical finance. His research uses rough analysis and rough path theory, a major tool in the study of random systems over the past two decades. Together with his collaborators at DataSig, he has adapted rough analysis to provide a set of practical tools in contemporary machine learning and data science with applications ranging from radioastronomy to molecular biology. He has written landmark publications in rough analysis in journals such as the Annals of Mathematics and the Annals of Probability, as well as leading journals and conferences in machine learning.