Ilyas Fatkhullin
PhD Student at ETH AI Center and Computer Science Department of ETH Zurich, Switzerland
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About
I am a final year PhD student at ETH Zurich advised by Prof. Niao He. My research focuses on developing theoretically-grounded algorithms for machine learning and optimization, with particular emphasis on large-scale optimization, reinforcement learning, and theoretical foundations. Previously, I had an honor to work with Prof. Boris Polyak on control theory problems and with Prof. Peter Richtárik on federated learning, focusing on communication-efficient distributed training.
- • Conferences: NeurIPS, ICML, AISTATS.
- • Journals: SIAM Journal on Control and Optimization, Journal of Machine Learning Research.
I am currently supported by the ETH AI Center Doctoral Fellowship. Previously, I received DAAD Scholarship from German Academic Exchange Service for Master studies in Germany.
Research Interests
Large-Scale Optimization
Designing scalable optimization algorithms for efficient training of modern machine learning models.
- • Stochastic optimization
- • Adaptive and parameter-free algorithms
- • Communication-efficient distributed training
Reinforcement Learning and Control
Developing principled approaches to decision-making under uncertainty with focus on efficiency, safety, and interpretability.
- • Sample and computational efficiency
- • Safe and robust RL algorithms
- • Multi-agent learning and games
Theoretical Foundations
Uncovering fundamental properties of optimization problems and establishing rigorous theoretical guarantees for algorithmic performance.
- • Non-convex optimization theory
- • Hidden convexity and reformulations
- • Heavy-tailed distributions
- • Non-Euclidean geometries