I am a research scientist at Criteo AI Lab in the Causal Learning team. 

My research mostly focuses on explaining and improving machine learning methods with the help of Bayesian and causal statistics. Recent works include insights into connection between Bayesian and standard neural networks, fairness assessment in recommentation systems, policy allocation in multi-treatment causal problems. 

Before joining Criteo, I did my postdoc at Inria Grenoble Rhone-Alpes in the Statify team. The goal was to continue my previous research on the exploration distributional properties of Bayesian neural networks. More specifically, I was interested in explaining the difference between deep learning models of wide and shallow regimes in order to improve the interpretability and efficiency of the models. 

I obtained my PhD degree in applied mathematics in 2022 at the University Grenoble Aples and Inria reseach center. I was a part of Statify and Thoth teams, under supervision of Julyan Arbel and Jakob Verbeek. During November 2019-January 2020, I was visiting Duke University  and working on prior predictive distributions in Bayesian neural networks under supervision of David Dunson. Prior to that, I obtained my Bachelor degree at Moscow Institute of Physics and Technology (MIPT) and did the second year of Master program at Grenoble Institute of Technology (Grenoble - INP, Ensimag).

Keywords: Bayesian neural networks, theoretical deep learning, fairness, uplift modeling, causal statistics

My CV can be found here. 

Hobbies:  travelling, hiking and playing the ukulele.