Machine Learning

ML4H 2024
Congratulations to Woojung Kim on having his paper accepted at ML4H 2024. This paper introduces the Mixed Type Multimorbidity Variational Autoencoder (M3VAE), a deep probabilistic generative model developed for supervised dimensionality reduction in the context of multimorbidity analysis.
Aug 16, 2024 1 min read Machine Learning, Digital Health
MLCB 2024
Congratulations to Charles Gadd on having his paper accepted at MLCB 2024. Changes in the number of copies of certain parts of the genome, known as copy number alterations (CNAs), due to somatic mutation processes are a hallmark of many cancers.
Aug 16, 2024 1 min read Machine Learning, Cancer
New paper in Bioinformatics
Congratulations to Kaspar Martens on having his paper accepted in Bioinformatics. Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches.
Dec 13, 2023 2 min read Machine Learning, Cancer
New publication in BMC Bioinformatics
Congratulations to Joel Nulsen on having his paper accepted in BMC Bioinformatics. Genomic insights in settings where tumour sample sizes are limited to just hundreds or even tens of cells hold great clinical potential, but also present significant technical challenges.
Nov 30, 2023 1 min read Machine Learning, Cancer
MLCB 2023
Congratulations to Kaspar Martens on having his paper accepted at MLCB 2023. Generative models for multimodal data permit the identification of latent factors that may be associated with important determinants of observed data heterogeneity.
Aug 16, 2023 1 min read Machine Learning, Cancer
ML4H 2022
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
Dec 1, 2022 1 min read 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!
Jul 5, 2022 4 min read Machine Learning, Phd
The Great UK PhD Data Science Survey
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.
Jun 22, 2022 1 min read Machine Learning, Cancer
New report: My Cancer & AI
AISTATS 2022
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.
Mar 13, 2022 1 min read Machine Learning, Phd, Student
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.
Dec 7, 2021 1 min read Machine Learning