My research is centered around optimization, federated (reinforcement) learning, and ethical concerns that come with training machine learning models. Quite generally, I am interested in finding algorithms that can adapt to problem's structural properties to improve either convergence rate, communication complexity, or other properties like privacy and fairness.
I am also strongly interested in methods that make training and using models more practical and trustworthy. In particular in quantification of model's uncertainty, and in procedures that allow learning with missing data (e.g. imputation).
Feel free (and even, encouraged) to reach to me if you want to discuss any of these questions, I am always happy to chat! You can conctact me by e-mail at paul.mangold@polytechnique.edu.