Education

• 2020 - Present
      PhD, Continual Learning for Generative Models
      Computational Intelligence group, Vrije Universiteit Amsterdam (VU)
      Supervised by: Jakub Tomczak (VU), Max Welling (UvA), Guszti Eiben (VU)

• 2017 - 2019
      MSc, Statistical Learning Theory (double degree)
      Higher School of Economics and Skoltech
      Thesis: Bayesian Generative Models for Knowledge Transfer in Deep Neural Networks on MRI Data
      Supervised by: Evgeny Burnaev
      GPA: 8.65/10 (HSE); 4.74/5 (Skoltech)

• 2013 - 2017
      BSc, Mathematical Methods in Economic Analysis
      Higher School of Economics
      Thesis: Comparing Forecasting Power of Bayesian VAR with 1-d Time Series Models.
      Supervised by: Boris Demeshev
      GPA: 8.88/10

• 2016
      Exchange program, Erasmus School of Economics
      Erasmus University Rotterdam, the Netherlands

Work experience

• May 2023 - August 2023
      Research Intern
      Microsoft, AI4Science, Cambridge
  Training strictly local equivariant machine learning force field models with extended cutoff radius.

• July 2021 - October 2021
      Research Intern
      Qualcomm AI Research, Amsterdam
  Worked on compressed sensing with unknown orientation. See our ICML paper.

• July 2019 - August 2020
      Junior Research Engineer
      Skoltech, Moscow
      Supervised by Evgeny Burnaev

• Summer 2018
      Deep Learning Intern
      NVIDIA, Moscow
      Developed pipeline for online detection of creatures in the video games, based on deep neural network

• 2017 - 2018
      Web Analyst
      Tinkoff bank, Moscow
      Impact evaluation for online ads, anomaly detection in user behavior, A\B-testing for landing pages optimization

Teaching