Congratulations to group members Charles Gadd, Woojung Kim and Dominic Danks on the following papers accepted at ML4H and NeurIPS:
mmVAE: multimorbidity clustering using Relaxed Bernoulli β-Variational Autoencoders
Feature Allocation Approach for Multimorbidity Trajectory Modelling
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs
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.
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.
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.
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?”.
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.
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.
Increasing rates of opioid-related overdose have been identified globally. Treatment for opioid use disorders (OUD) includes medications for opioid use disorder (MOUD) alongside behavioral support. In this study we use statistical analysis and machine learning to show that the Breaking Free Online program may reduce opioid use and improve biopsychosocial functioning in individuals with OUD.
Link to paper
Christopher Yau has been awarded a 5-year Turing AI Acceleration Fellowship.
The Turing AI Acceleration Fellowships are part of the government’s AI sector deal investment in Turing AI fellowships, recommended by the independent 2017 UK AI Review, whose report ‘Growing the artificial intelligence industry in the UK,’ was co-authored by Reguis Professor of Computer Science at Southampton, Dame Wendy Hall, and Jérôme Pesenti, now Vice President of AI at Facebook.