Thomas Rainforth

Oxford University

I am a Senior Researcher in Machine Learning and leader of the RainML Research Lab at the Department of Statistics in the University of Oxford. I am the prinicpal investigator for the ERC Starting Grant Data-Driven Algorithms for Data Acquisition (funded by the UKRI Horizon Guarantee Scheme). My research covers a wide range of topics in and around machine learning and experimental design, with areas of particular interest including Bayesian experimental design, deep learning, representation learning, generative models, Monte Carlo methods, active learning, probabilistic programming, and approximate inference.

Modern Bayesian Experimental Design

Bayesian experimental design (BED) provides a powerful and general framework for optimizing the design of experiments. However, its deployment often poses substantial computational challenges that can undermine its practical use. In this talk, I will outline how recent advances have transformed our ability to overcome these challenges and thus utilize BED effectively, thereby providing huge opportunities for gathering data intelligently and adaptively. Related review paper: