I started my PhD in 2023 co-supervised by Gunnar Rätsch and Vincent Fortuin. I work on informed, Bayesian, and representation machine learning with applications to medical data.

I did my BSc in Applied Mathematics and Physics at MIPT, specializing in computational methods for physics simulations. In parallel, I developed deep learning models for high-energy physics at GSI and LAMBDA.

In my MSc I studied Computational Sciences and Engineering at EPFL, combining my interest in numerical and data-driven modeling. I interned at startup companies Spiden and Neural Concept, where I worked on synthetic data generation for medical devices and 3D computer vision for engineering. My master’s thesis focused on physics-informed neural networks for fluid flow modeling.