Sign up to save your podcasts
Or
Share Chris Holmes | AI, Digital Health, & The Alan Turing Institute
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Chris Holmes | AI, Digital Health, & The Alan Turing Institute
Chris Holmes is Professor of Biostatistics at the University of Oxford and Programme Director for Health and Medical Sciences at The Alan Turing Institute. Chris’ research interests include Bayesian nonparametrics (which is the right kind of nonparametrics), statistical machine learning, genomics, and genetic epidemiology.
0:00 - Intro
1:38 - Chris Holmes, Professor of Biostatistics at Oxford University
3:28 - UK Biobank & designing a valuable dataset
8:42 - Healthcare charities in the UK
11:16 - Digital Health: prioritizing research questions
19:55 - Bayes, nonparametrics, and Bayesian nonparametrics
23:30 - Model prediction is at the heart of Bayesian inference
28:00 - Prioritization in model building for biology
33:09 - Model constraints to generate valid inference
37:34 - Hypothesis driven science in statistical learning versus deep learning
43:30 - Developing models in genomics & clinical informatics
48:37 - Building stable, generalizable and robust models
52:41 - Important questions to think about
54:05 - Causal reasoning and clinical risk prediction
57:50 - What topic should the statistical community debate?
View all episodes
By Glen Wright Colopy
5
77 ratings
Chris Holmes is Professor of Biostatistics at the University of Oxford and Programme Director for Health and Medical Sciences at The Alan Turing Institute. Chris’ research interests include Bayesian nonparametrics (which is the right kind of nonparametrics), statistical machine learning, genomics, and genetic epidemiology.
0:00 - Intro
1:38 - Chris Holmes, Professor of Biostatistics at Oxford University
3:28 - UK Biobank & designing a valuable dataset
8:42 - Healthcare charities in the UK
11:16 - Digital Health: prioritizing research questions
19:55 - Bayes, nonparametrics, and Bayesian nonparametrics
23:30 - Model prediction is at the heart of Bayesian inference
28:00 - Prioritization in model building for biology
33:09 - Model constraints to generate valid inference
37:34 - Hypothesis driven science in statistical learning versus deep learning
43:30 - Developing models in genomics & clinical informatics
48:37 - Building stable, generalizable and robust models
52:41 - Important questions to think about
54:05 - Causal reasoning and clinical risk prediction
57:50 - What topic should the statistical community debate?