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Computational Statistics and Machine Learning

This theme is concerned with advancing the theory, methodology, algorithms and applications to modern, computationally intensive, approaches for statistical inference.

We encompasses topics from machine learning methods for adaptive systems and flexible modelling, to optimisation and simulation based approaches to solve intractable computational problems of relevance to statistics, such as听variational or Monte Carlo methods and simulation-based inference.

The need for advanced algorithms is a reality in any modern day application of statistics, be it in finance, social science,听natural sciences or听beyond. The development of simulation and optimisation algorithms, as well as the analysis of their听properties in the context of statistical inference, is a core mission of our group. Our research has advanced the applicability of models that imply complex posterior distributions in Bayesian inference, or more generally those that involve sophisticated generative models required to faithfully capture the structure of real data.听

Machine learning听originated from research in artificial intelligence, where historically the focus has been on data-driven approaches for听the development of autonomous systems. However, it has grown to affect science and society in many high-stakes applications听that require听careful statistical diligence and a deep understanding of the specific needs raised by real-world data. Our group advances machine learning methodology听by embracing the adaptability and generality of AI-inspired research with the expectations of听stakeholders who听require robustness and uncertainty quantification in their applications.

Theme Members

Research Groups

  • . The focus of this research group is on the intersection of statistical inference and machine learning methodology and theory.听

Recent and Upcoming听Events

Current and Recent Externally Funded Projects

  • Transfer Learning for Monte Carlo Methods, EPSRC New Investigator Award听. June 2024 - June 2027. PI: Briol. 听
  • Robust Foundations for Bayesian Inference, EPSRC听. June 2024 - June 2025. PI: Briol, co-I: Knoblauch.
  • CHAI -听Causality in Healthcare AI with Real Data, EPSRC AI Hub听, February 2024 - January 2029, Co-Is: Silva and Karla Diaz-Ordaz
  • The Causal Continuum - Transforming Modelling and Computation in Causal Inference, EPSRC Open Fellowship听, October 2022 - September 2027, PI: Silva
  • Robust and Scalable Markov Chain Monte Carlo for Heterogeneous Models, EPSRC听. July听2022 - October 2024. PI: Livingstone
  • Geometry and Bayesian statistics to reconstruct protein radical structures from ENDOR spectroscopy,听, Fellow: Pokern