I am working in the Causal Inference group as a PhD student, where I am founded via a Google European Doctoral Fellowship.
My research focuses mainly on kernel methods, such as kernel mean embeddings. They lead to metrics over distributions, which are now widely used to design distribution comparison tests, such as the Maximum Mean Discrepancy and the HSIC test. Studying these metrics, I recently turned towards generative adversarial nets (GANs), which can be seen as an effective way to generate a fake distribution that minimises its distance to another reference distribution.
Sept. 13 - Present: PhD Student at the Max Planck Institute for Intelligent Systems (supported in part by the Google Doctoral European Fellowship in Causal Inference)
July 2013 : General Engineering Master Diploma at Mines ParisTech (Ecole Nationale Supérieure des Mines de Paris), Major: Geostatistics
Advances in Neural Information Processing Systems 29, pages: 1732-1740, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 (conference)
In Proceedings of The 32nd International Conference on Machine Learning, 37, pages: 2218–2226, JMLR Workshop and Conference Proceedings, (Editors: Bach, F. and Blei, D.), JMLR, ICML, 2015 (inproceedings)
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems