Christopher Yau

Professor of Artificial Intelligence

University of Oxford

I am Professor of Artificial Intelligence at the University of Oxford in a joint position between Nuffield Department for Women’s and Reproductive Health and the Nuffield Department of Population Health. My group is based at the Big Data Institute. I am also Director of the Health Data Research-Alan Turing Institute Wellcome PhD programme in Health Data Science and a recipient of the UKRI Turing Artificial Intelligence Fellowship.

About Me

I studied undergraduate Engineering at Cambridge where I did my masters dissertation with Professor Andrew Blake at Microsoft Research on digital image analysis. Afterwards, I joined the EPSRC-funded Life Sciences Interface Doctoral Training Centre, led by Professor David Gavaghan in Oxford where I completed my doctoral thesis in Statistics under the supervision of Professor Chris Holmes.

I subsequently took up MRC Fellowship in Biomedical Informatics before joining Imperial College London as a Lecturer in Statistics in the Department of Mathematics. I rejoined Oxford where I became Associate Professor in Genomic Medicine as a Principal Investigator at the Wellcome Trust Centre for Human Genetics. I then became Reader and then Professor of Artificial Intelligence at the University of Birmingham and the University of Manchester before returning to Oxford.

I am a member of the MRC Better Methods, Better Research Programme Panel (formerly the Methodology Research Panel) and the Millenium Medal committee. I lead the Genomics England Clinical Interpretation Partnership in Quantitative Methods, Functional Genomics and Machine Learning. I am also a consultant for Kheiron Medical Technologies, the Medicines & Healthcare products Regulatory Agency and Singula Bio. I was previously on the Taskforce group for the Academy of Medical Science FLIER programme.

Research Interests

For many years, I have been interested in the development of novel statistical methods for solving a broad spectrum of biomedical problems from microscopy to genomics to mental health. Real problems inspired and underpin the methodological research I do and getting to grips with real scientific investigations and experiments grounds the research in reality.

My approach is normally Bayesian using formal probabilistic methods. This gives methods a coherent development framework allowing for greater rigour in model evaluation.

I believe in a multi-disciplinary approach which involves working with and training not only statistical and machine learning scientists but also those from a biomedical, social science or clinical background.


  • Statistical Machine Learning
  • Computational Genomics
  • Digital Health


  • DPhil Statistics, 2009

    University of Oxford

  • MEng in Information Engineering, 2004

    University of Cambridge