O’Bayes 2019 has now started!

The O’Bayes 2019 conference in Warwick University has now started, with about 100 participants meeting over four days (plus one of tutorials) in the Zeeman maths building of the University. Quite a change of location and weather when compared with the previous one in Austin. As an organiser I hope all goes well at the practical level and want to thank the other persons who helped me towards this goal, first and foremost Paula Matthews who solved web and lodging and planning issues all over these past months, as well as Mark Steel and Cristiano Villa. As a member of the scientific committee, I am looking forward the talks and discussants along the coming four days, again hoping all speakers and discussants show up and are not hindered by travel or visa issues…

hittin’ a Brexit wall

PhD studentships in AI and Data Science, at the University of Warwick

A (rather urgent) call for candidates for PhD studentships in AI and Data Science at Warwick. These are part of Warwick’s MRC-Funded Doctoral Training Program in Interdisciplinary Biomedical Research. Selected students would start in Autumn 2018. The deadline is on June 3.

may June 8 be the ides of May!

London calling

London calling to the imitation zone
Forget it, brother, you can go it alone
London calling to the zombies of death
Quit holding out and draw another breath
London calling and I don’t want to shout

The Clash, 1979

Gregynog [jatp]

learning and inference for medical discovery in Oxford [postdoc]

[Here is a call for a two-year postdoc in Oxford sent to me by Arnaud Doucet. For those worried about moving to Britain, I think that, given the current pace—or lack thereof—of the negotiations with the EU, it is very likely that Britain will not have Brexited two years from now.]

Numerous medical problems ranging from screening to diagnosis to treatment of chronic diseases to  management of care in hospitals requires the development of novel statistical models and methods. These models and methods need to address the unique characteristics of medical data such as sampling bias, heterogeneity, non-stationarity, informative censoring etc. Existing state-of-the-art machine learning and statistics techniques often fail to exploit those characteristics. Additionally, the focus needs to be on probabilistic models which are
interpretable by the clinicians so that the inference results can be integrated within the medical-decision making.

We have access to unique datasets for clinical deterioration of patients in the hospital, for cancer screening, and for treatment of chronic diseases. Preliminary work has been tested and implemented at UCLA Medical Center, resulting in significantly management care in this hospital.

The successful applicant will be expected to develop new probabilistic models and learning methods inspired by these applications. The focus will be primarily on methodological and theoretical developments, and involve collaborating with Oxford researchers in machine learning, computational statistics and medicine to bring these developments to practice.

The post-doctoral researcher will be jointly supervised by Prof. Mihaela van der Schaar and Prof. Arnaud Doucet. Both of them have a strong track-record in advising PhD students and post-doctoral researchers who subsequently became successful academics in statistics, engineering sciences, computer science and economics. The position is for 2 years.