Chris Holmes is Visiting Faculty at EIT, leading cutting-edge initiatives at the intersection of artificial intelligence, statistics, machine learning and advanced analytics in partnership with the University of Oxford. With a strong background in both academic research and industry applications, Chris’ work is driven by leveraging AI to solve complex, real-world problems and using AI to shape scientific discovery. Alongside EIT, Chris is a Professor of Biostatistics at the University of Oxford, currently focused on robust statistical machine learning methods and causal inference in health and medical sciences.

Previously, Chris was the Programme Director for Health and Medical Sciences at the Alan Turing Institute. Gaining his doctorate in Bayesian Statistics at Imperial College London, by investigating novel nonlinear pattern recognition methods. This was followed by a post-doctoral position and then a lectureship at Imperial College. Chris worked in industry for a number of years before his PhD, researching scientific computing and developing techniques for real-time pattern recognition models in Supervisory Control and Data Acquisition (SCADA) systems.

Chris has a broad interest in the theory, methods and applications of statistics and statistical machine learning. His background is in Bayesian Statistics which provides a unified framework to modelling and information processing and he is particularly interested in causal machine learning and robustness of AI systems.