I neither know nor think I know

Egor Shulgin is a CS student in the MS/Ph.D. program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia.

Education and Early Career

Egor obtained his bachelor's degree in Applied Mathematics, Physics, and Computer Science from the Moscow Institute of Physics and Technology (MIPT) in 2019. He first came to KAUST for a research visit in 2019. Later, he joined Prof. Peter Richtarik's group as a MS/Ph.D. student.

In 2021 Egor did a research internship with Distributed AI team at the Samsung AI center in Cambridge, United Kingdom. In 2022 he interned at Apple Private Machine Learning team with a focus on Federated Learning.

Research Interest

Egor's research interests include theoretical aspects of optimization for machine learning and private federated learning.

Education Profile

  • B.S., Applied Mathematics, Computer Science and Physics, Moscow Institute of Physics and Technology (MIPT), Russia, 2019

Awards and Distinctions

  • ICML 2022 Outstanding Reviewer (Top 10%)
  • ETH Zurich Summer Research Fellowship CS Department


Bldg 1, Level 2, 2225-WS21

Selected Publications

Shulgin, E., & Richtárik, P. (2022). Shifted compression framework: Generalizations and improvements. In Uncertainty in Artificial Intelligence.
Safaryan, M., Shulgin, E., & Richtárik, P. (2021). Uncertainty principle for communication compression in distributed and federated learning and the search for an optimal compressor. Information and Inference: A Journal of the IMA, 11(2), 557-580.
Kovalev, D., Shulgin, E., Richtárik, P., Rogozin, A. V., & Gasnikov, A. (2021). ADOM: Accelerated decentralized optimization method for time-varying networks. In International Conference on Machine Learning.
Gower, R. M., Loizou, N., Qian, X., Sailanbayev, A., Shulgin, E., & Richtárik, P. (2019). SGD: General analysis and improved rates. In International conference on machine learning.