After dual PhDs from Ecole Polytechnique and Stanford University in optimisation and finance, followed by a postdoc at U.C. Berkeley, Alexandre d’Aspremont joined the faculty at Princeton University as an assistant then associate professor. He returned to Europe in 2011 and is now a research director at CNRS, attached to Ecole Normale Supérieure in Paris. He received the SIAM Optimization prize, a NSF CAREER award, and an ERC starting grant. He co-founded and is scientific director of the MASH Msc degree at PSL. He also co-founded Kayrros SAS, which focuses on energy markets and earth observation.
Topics of interest
Optimisation, machine learning
Project in Prairie
Alexandre d’Aspremont’s work is focused on optimization and applications in machine learning, statistics, bioinformatics, signal processing and finance. He collaborates with several companies on projects linked to earth observation, insurance pricing, statistical arbitrage, etc. He is also co scientific director of MASH, a Msc program focused on machine learning and its applications in digital marketing, journalism, public policy, etc.
Optimization plays a central role in modern statistics and machine learning
in particular. Beyond direct applications of optimization algorithms, convex duality results and complexity theory underpin many recent developments in statistical learning (e.g. compressed sensing or matrix completion problems). Yet the link and clear empirical tradeoff between statistical performance and computational complexity has yet to be fully explained, which are crucial in most domains, in particular safety/health critical ones.