Sébastien Le Digabel is a Professor of Mathematics at Polytechnique Montreal and a regular member of the GERAD research center. Before that, he obtained a PhD in applied mathematics from Polytechnique in 2008, and worked as a postdoctoral fellow at the IBM Watson Research Center and the University of Chicago in 2010 and 2011.

His research interests include the analysis and development of algorithms for blackbox optimization, and the design of related software. Blackbox optimization occurs when the functions to optimize are given by numerical simulations for which derivative information is not available. In this context, derivative-free optimization may be considered, and in particular the Mesh Adaptive Direct Search (MADS) method of Audet and Dennis, for which Le Digabel's thesis brought some extensions and upgrades. All of his work on MADS is included in the NOMAD software, a free package for blackbox optimization available at www.gerad.ca/nomad.

S. Le Digabel's research is funded by the Canadian NSERC foundation, the Quebec FRQNT fund, IVADO, InnovÉÉ, Hydro-Québec, Rio Tinto, and Huawei-Canada.