Researcher at Inria, leading since 2011 the machine learning team which is part of the Computer Science department at Ecole Normale Supérieure. Ph.D. Berkeley (2005). ERC Starting grant (2009) and Consolidator Grant (2016), Inria young researcher prize (2012), ICML test-of-time award (2014), Lagrange prize in continuous optimization (2018). Co-editor-in-chief of the Journal of Machine Learning Research.
Topics of interest
Machine learning, Optimization, Statistics
Project in Prairie
Francis Bach will address fundamental problems in machine learning, using optimization methods that come with theoretical guarantees and can help solve challenging problems in computer vision and natural language processing. He will participate in the teaching effort through his existing classes and in collaborations with PRAIRIE industry partners.
Machine learning algorithms are ubiquitous in most scientific, industrial and personal domains, with many successful applications. As a scientific field, machine learning has always been characterized by the constant exchanges between theory and practice, with a stream of algorithms that exhibit both good empirical performance on real-world problems and some form of theoretical guarantees. Many of the recent and well publicized applications come from deep neural networks, where these exchanges are harder to make. Bridging this new gap between theory and practice is key to obtaining performance guarantees and uncertainty estimates, which are crucial in most domains, in particular safety/
health critical ones.