I am working on machine learning for spatio-temporal data. In particular, my work is focused on how to leverage differential equations and their links with neural networks in order to create performant prediction models for complex data, such as videos.
Inspired by such connections, I designed with colleagues a novel temporal model based on residual connections instead of recurrent neural networks, and that lead to state-of-the-art results for stochastic video prediction.
Recent Research Areas
During the second half of my studies at Ecole Normale Supérieure de Lyon, I became interested in machine leaning and artificial intelligence, and have done three related long research internships since then.
I worked more particularly on:
- fairness and accountability of automated decision-making processes;
- robustness of classifiers to adversarial and random examples;
- convex optimization;
- unsupervised representation learning for time series.
Previous Research Experience
Here is a list of my research internships (in reverse chronological order), that were done within the scope of my studies at Ecole Normale Supérieure de Lyon:
- EPFL, MLO laboratory, Lausanne, Switzerland, supervised by Martin Jaggi (5 months): unsupervised general purpose scalable representation learning for time series;
- ENS de Lyon, LIP laboratory, team MC2, Lyon, France, supervised by Omar Fawzi (6 months): robustness of classifiers to random and adversarial perturbations, convex optimization;
- Inria, team Privatics, Lyon, France, supervised by Daniel Le Métayer: fairness and accountability of automated decision-making processes;
- University of Konstanz, Computer Graphics and Media Informatics working group, Konstanz, Germany, supervised by Abdalla G. M. Ahmed and Oliver Deussen: blue-noise sampling;
- Inria, team Marelle, Valbonne, France, supervised by Yves Bertot: integration of a logical system in the higher order proof system Coq.
You can find more information about my research experience in my CV.