Petr Mokrov

Personal Website

mokrov.pv@phystech.edu

Google Scholar

He graduated with a master’s degree from the joint program of MIPT (department of Intellectual systems) and Skoltech (Data Science) in 2022. He is a PhD student at Skoltech under the supervision of Evgeny Burnaev. He is an author of a few scientific papers on machine learning.

Professional interests: generative modeling, optimal transport

Publications

1. Large-scale Wasserstein Gradient Flows, NeurIPS’2021
2. Building the Bridge of Schrödinger: A Continuous Entropic Optimal Transport Benchmark, NeurIPS’2023
3. Energy-guided Entropic Neural Optimal Transport, ICLR’2024
4. Neural Optimal Transport with General Cost Functionals, ICLR’2024