machine learning

The Great UK PhD Data Science Survey

What am I doing? My name is Christopher Yau and I am Professor of Artificial Intelligence at the University of Oxford and Health Data Research UK. I am carrying out a survey of UK PhD students who are working in any area of data science and I need your help! We hope to get survey responses from over 300 PhD students so please help us by sparing 10-15 minutes of your time to answer some questions.

New report: My Cancer & AI

A report on a patient engagement workshop series on what cancer patients think about artificial intelligence is now available. The workshop series was developed in partnership with Ovarian Cancer Action (OCA) as part of my Turing AI Fellowship. I am grateful to patients and OCA for their support in developing this work and its impact on my future research plans. The report is available to download from here.


Congratulations to PhD student Dominic Danks whose paper Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis has been accepted for presentation at AISTATS 2022.

NeurIPS 2021 Success - Multi-Facet Clustering Variational Autoencoders

Christopher Yau has supported Health Data Research UK (HDRUK) PhD students Fabian Falck and Haoting Zhang in the development of work that has now been published as a paper at the NeurIPS 2021 conference. The work entitled Multi-Facet Clustering Variational Autoencoders is a novel class of variational autoencoders with a hierarchy of latent variables, each with a Mixture-of-Gaussians prior, that learns multiple clusterings simultaneously, and is trained fully unsupervised and end-to-end.

Artificial Intelligence Webinar - Ovarian Cancer Action UK

Recently Christopher Yau worked with Ovarian Cancer Action UK to put together a webinar on his research for patients and the public. You can find the video on Youtube: “What is artificial intelligence and what does it mean for cancer research?”.

Multiple postdoctoral research positions available

We have 4-5 two/three-year postdoctoral positions available linked to the following projects: Turing AI Fellowship OPTIMAL MuM-PreDICT Roche RPF Suitable applicants should either have experience in developing statistical and/or machine learning methods or a background in mathematics and computational science that would enable them to learn relevant research approaches. Interested applicants are advised to examine recent group publications and projects to understand the research methods that have been adopted within the group.


Research to improve care for pregnant women with two or more health conditions


AI for Multiple Long-Term Conditions

ICML 2021

Congratulations to PhD student Dominic Danks whose paper BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders. has been accepted for presentation at the International Conference for Machine Learning 2021.

Roche ML for Antibody Design

Machine Learning Models for the Development of Antibodies