Monte Carlo methods are numerical integration methods that are frequently needed in Bayesian inference. I am interested in making Monte Carlo scale to expensive-to-evaluate integrands. Like many natural scientists, my collaborators in cardiac cell biology often face this issue, as evaluating the integrand once means approximately solving large sets of differential equations that describe the system under study. This line of research has brought me to study determinantal point processes, a distribution over configurations of points that show repulsiveness while being statistically tractable.
I am also trying to make Monte Carlo scale to inference with tall datasets: today's data deluge involves working with datasets containing a large number of items, which frequentist statisticians classically tackle with stochastic gradient. I am looking for the Monte Carlo equivalent of stochastic gradient, which would unlock big data applications of Bayesian inference. It is a hot topic, as Bayesian inference provides the uncertainty quantification astrophysicists or medical scientists need. This line of research has also triggered my interest in concentration inequalities.
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In a nutshell, I obtained my Ph.D. in November 2012 from University Paris-Sud, France, working with Balázs Kégl on Monte Carlo methods and Bayesian optimization, applied to particle physics and machine learning. In particular, I was a member of the Pierre Auger collaboration.
Then I joined Chris Holmes's group at the University of Oxford, UK, to work as a postdoc on Markov chain Monte Carlo for tall data. Since then, I am also working on applications to computational biology within the 2020 science network, of which I am now an emeritus fellow.
I am the PI of ANR starting grant BoB 'Bayes on a budget' (170k€, 2016-2020), which implements my research programme. The consortium is made of computer scientist Michal Valko, signal processer François Septier, mathematician Adrien Hardy, PhD candidates Guillaume Gautier and Ayoub Belhadji, and myself.
I am passionate about teaching. In 2016-2017, I am teaching two master-level courses: Bayesian nonparametrics 101 at ENSAE Paris and Machine learning applied to bankruptcy prediction at Ecole Centrale de Lille.
The objective of the former is that students can read and understand recent advances in BNP research. The latter course is built around a machine learning competition on bankruptcy prediction, and aims at preparing future data scientists to face the wild data world out there.