I am a senior research scientist at Criteo AI Lab in the Fundamental Deep Learning team.  I contribute to both academic and industrial research, focusing on explaining and improving machine learning methods through probabilistic and causal statistical approaches. At Criteo, I lead research initiatives aimed at ensuring algorithmic fairness in online systems and provide consulting to policymakers on AI regulations.

In addition to my professional endeavors, I am an advocate for gender diversity and inclusivity in the tech industry, actively promoting and organizing events to foster equitable opportunities and representation.

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 research 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: trustworthy AI, theoretical deep learning, fairness, uplift modeling, causal statistics

My CV can be found here. 

Hobbies:  travelling, hiking and playing the ukulele.