This page contains a summary of my research and teaching activities. For a detailed CV and full publication list, please click here;
I graduated in 2009 from ENS Cachan (France) with an MSc in mathematics for vision and learning. I obtained my PhD in 2012 at University Paris-Sud XI (France), working under the supervision of Balazs Kégl. Since January 2013, I'm a postdoctoral researcher in the Statistics department of the University of Oxford (UK), in Chris Holmes' group.
I am interested in numerical Bayesian methods. Particular topics include large-scale approximate inference, adaptive Markov Chain Monte Carlo (MCMC), and Bayesian optimization. During my PhD, I was first interested in solving methodological problems motivated by MCMC inference in the Pierre Auger experiment, a large-scale cosmic ray observatory located in the Argentinian pampa. Second, I worked on automatic hyperparameter tuning methods, with the idea in mind to deliver turn-key machine learning software. Besides continuing research on these topics, I am currently interested in approximate Bayesian inference and decision-making for large datasets, and a variety of inference and model comparison applications in complex biological systems.
This year, I am lecturing part of the course on Monte Carlo methods for 4th year students at Oxford's department of Statistics. Last year, I tutored problem classes for the same course. Prior to that, I was a teaching assistant in computer science and applied maths at Polytech' Paris-Sud engineering school, where I gave undergraduate (U) and graduate (G) exercise and practical sessions on linear and nonlinear optimization (U), stochastic processes (U), basic algorithmics and C programming (U), integer programming (G). Besides, I also took part in an orientation module to help first-year students to plan their studies.
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