Rustem Islamov
Looking for autumn/winter internship positions
About me
I am a final-year PhD student at University of Basel working under the supervision of Aurelien Lucchi. Prior to that, I obtained my Master diploma from Institut Polytechnique de Paris in Data Science and Bachelor diploma from Moscow Institute of Physics and Technology in Applied Mathematics. I am interested in Optimization and its applications to Machine Learning.
On top of that, I am a big fan of tennis and judo.
Interests
- Optimization
- Efficient and Scalable Algorithms
- Scaling Laws from First Principles
- Convex/Non-convex Stochastic Optimization
- Machine Learning
- Loss Landscape of Neural Networks
- Federated learning
- Communication-efficient and Local Algorithms
Education
PhD in Computer Science
Supervisor: Aurelien Lucchi
Master of Science in Data Science
Thesis: Unified Analysis of Asynchronous Algorithms
Thesis Supervisor: Mher Safaryan, Dan Alistarh
Bachelor of Science in Applied Mathematics and Physics
Thesis: Distributed Second Order Methods with Fast Rates and Compressed Communication
Thesis Supervisor: Peter Richtárik
Recent news
- September 24, 2026I am happy to share that I have two papers accepted to NeurIPS 2026: one on Local SGD and one on Adam-SGD gap.
- August 23, 2026I am attending Swiss Optimization Symposium in Monte Verità . My talk focuses on recent works that use convergence bounds to improve deep learning models training. The slides can be found via the link.
- August 21, 2026I attended UAI 2026 in Amsterdam. I presented my work on Byzantine-robust and differentially private optimization.
- June 29, 2026I am attending ICML 2026 in Seoul. I present four papers during poster sessions on 9th and 10th of July. Moreover, I give an oral presentation on 9th of July on Non-Euclidean Edge of Stability. Finally, I was also invited to give a lighting talk at Protocal Learning workshop.
- June 6, 2026I attended SIOPT 2026. I organized a session on recent advances in federated optimization and gave a presentation on Safe-EF method.
Selected Publications
My contributions have appeared in leading machine learning venues including NeurIPS, ICML, ICLR, and AISTATS. Here is the list of papers I am most proud of:
[21]
On the Role of Batch Size in Stochastic Conditional Gradient Methods, (ICML 2026).
[20]
Non-Euclidean Gradient Descent Operates at the Edge of Stability, (Oral, ICML 2026)
[14]
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs, (Oral, AISTATS 2025).