Egor Shulgin
Egor Shulgin research investigates heterogeneous, efficient, and personalized federated learning, and practical optimizers for modern deep learning.
Biography
Egor Shulgin is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), advised by Professor Peter Richtárik. His work has appeared at venues including ICML, ICLR, AISTATS, and UAI. During his PhD, he conducted research internships at Apple and Samsung AI Center in Cambridge, UK. He received his BSc in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology in 2019.
Expertise and Interests
Egor's research focuses on optimization for machine learning, including distributed and federated training, privacy-preserving learning, and practical optimizers for modern deep learning.
Awards and Distinctions
- Outstanding Reviewer (Top 10%), International Conference on Machine Learning (ICML) 2022, 2022
- ETH Summer Research Fellowship (top 0.7% of applicants), ETH Zurich CS Department, 2020
Education
- Bachelor of Science (B.S.)
- Applied Mathematics, Computer Science and Physics, Moscow Institute of Physics and Technology (MIPT), Russian Federation, 2019
