CV

Education

• PhD (Computer Science): Constructor University, Bremen, Germany, 2024-2026 (expected)
• MSc (Math of Machine Learning): HSE University, Moscow, Russia, 2021-2023
  Thesis title: Ensembling Neural Networks in the Transfer Learning Setup
• BSc (Applied Mathematics & Informatics): HSE University, Moscow, Russia, 2017-2021
  Thesis title: Empirical Analysis of Self-Supervised Training Properties

Languages: Russian (native), English (C1)

Work experience

• Centre of Deep Learning and Bayesian Methods — HSE University, Moscow, Russia, 2021-2023
  Research assistant: Fundamental research in deep learning
• SmartDec — Moscow, Russia, 2019-2020
  Intern, development & analytics: Industrial research in cryptography and zero-knowledge proofs

Skills

• Programming languages:
  • Proficient: Python, LaTeX
  • Intermediate: C/C++, Rust
• Technologies:
  • Data science: PyTorch, NumPy, Pandas, scikit-learn, matplotlib
  • Linux: bash, git, ssh, docker

Publications

Where Do Large Learning Rates Lead Us?

Where Do Large Learning Rates Lead Us? A Feature Learning Perspective

To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning

[Re] “Towards Understanding Grokking”

On the Memorization Properties of Contrastive Learning

Talks

To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning

October 28, 2023

Paper presentation at Fall into ML 2023: Conference on Machine Learning at HSE University, Moscow, Russia

Self-supervised Pre-training with Masked Image Modeling

March 31, 2023

Research seminar at Bayesgroup, online

On the Memorization Properties of Contrastive Learning

July 24, 2021

Poster session at Workshop on Overparameterization: Pitfalls & Opportunities at ICML, online

Teaching

Large Scale Deep Learning Models

Intro to Deep Learning

Self-supervised Learning

Machine Learning