Experience

Graduate Student Researcher
Optimization and Machine Learning Lab at KAUST August 2020 – Present
- Conducted theoretical and practical research on distributed optimization methods for federated learning. Contributed to multiple NeurIPS, ICML publications, and workshop papers.
- This lab is a part of Visual Computing Center led by Peter Wonka and GenAI CoE (formerly AI Initiative) led by Juergen Schmidhuber.
Research Intern
Optimization and Machine Learning Lab at KAUST January 2019 – February 2019
- Conducted independent theoretical research on Stochastic Coordinate Descent algorithms, which formed the foundation of my bachelor’s thesis.
Junior Researcher in the Research Group of Randomized Algorithms for Distributed Optimization Problems
Laboratory of Advanced Combinatorics and Network Applications at MIPT August 2018 – October 2019
- Conducted research under a dedicated grant focusing on randomized algorithms for Machine Learning.

PhD in Applied Mathematics and Computational Science
King Abdullah University of Science and Technology June 2022 – June 2026
Supervisor: Peter Richtárik;
GPA: 3.89/4.00 (Transcript);
Selected Courses: Private Data Analysis, Reinforcement Learning.
MS in Applied Mathematics and Computational Science
King Abdullah University of Science and Technology August 2020 – May 2022
Supervisor: Peter Richtárik;
GPA: 3.63/4.00 (Transcript);
Thesis: Non-convex Stochastic Optimization With Biased Gradient Estimators
Selected Courses: Deep Learning, Numerical Linear Algebra, SGD Methods, Stochastic Processes, Federated Learning, Algorithms, Stochastic Numerical Simulation.
MS in Applied Mathematics and Physics
Moscow Institute of Physics and Technology September 2019 – August 2021
Supervisor: Alexander Gasnikov;
GPA: 3.89/4.00;
Thesis: Distributed Nonconvex Stochastic Optimization With Gradient Compression;
Selected Courses: Bayesian Methods of Statistical Estimation, Combinatorics.
BS in Applied Mathematics and Physics
Moscow Institute of Physics and Technology September 2014 – August 2019
Supervisor: Peter Richtárik;
GPA: 3.49/4.00;
Thesis: Stochastic Coordinate Descent Method with Arbitrary Sampling.
Selected Courses: Discrete Mathematics, Calculus, Linear Algebra, Functional Analysis, Probability Theory, Stochastic Processes, Statistics, Optimization, Machine Learning.