PostDoc @ Optimization and Statistics, Paul-Sabathier University of Toulouse
I am a postdoctoral researcher in the statistics and optimization group at the Paul Sabatier university in Toulouse, France, advised by Juan Cortes, François Bachoc and Pierre Neuvial. Contact: firstname dot lastname at univ-tlse3.fr
protein design, Bayesian optimization, Gaussian processes, stopping strategies, concentration inequalities, quadrature of convex functions
On the practical side, I want to improve the protein-design workflow. With Bayesian optimization, I hope to reduce the number of expensive and time consuming wetlab experiments. Using results from preceding experiments, unlabelled data from related tasks and simulations, should allow to make informed choices about which protein modifications are worthwhile to explore. To this end, I have started developing POLi, a python library that allows for easier comparison of new protein optimization algorithms on different objectives.
On the theoretical side, I am pursuing the question: how much data is necessary to obtain a decent (Gaussian process) model. Importantly, I want an answer for a dataset at hand and NOT for all datasets. Optimal stopping and probably-approximately-correct bounds are tools, I am interested to answer that question.
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition (publication, code)
with Wouter Boosma, Jes Frellsen and Damien Garreau
Adaptive Cholesky Gaussian Processes (publication, code)
with Kristoffer Stensbo-Smidt, Pablo Moreno Muñoz, Wouter Boosma, Jes Frellsen and Søren Hauberg
Probabilistic linear solvers: a unifying view (publication, arXiv)
with Jon Cockayne, Ilse Ipsen and Philipp Hennig
Before I came to Toulouse, I was a postdoctoral researcher at the machine learning section at Copenhagen university and the cognitive systems section at the Danish tehnical university, advised by Wouter Boosma, Jes Frellsen and Søren Hauberg.
I did my Ph.D. at the Max Planck Institute for Intelligent Systems in Tübingen under Philipp Hennig on probabilistic linear algebra.