Maxim Raginsky
Professor
Studies statistical machine learning and information theory, including dynamical systems views of learning and optimization.
Maxim Raginsky's lab at UIUC works on learning theory & optimization and signal processing & information theory. 15 papers since 2023, 2 at top venues, mostly ALT and CDC. 17 alumni. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 13%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 17
Papers by yearfrom the lab homepage, 2023–2026
- 3 papers2026 · T1 0 · collecting
- 3 papers2025 · T1 0
- 4 papers2024 · T1 1
- 5 papers2023 · T1 1
T1 venuesOtherVenues: ALT, CDC, NeurIPS, TMLR
Research focuspaper titles that use each topic's words
- Signal processing & information theory4 papers
- Learning theory & optimization3 papers
- dynamical0 papers
- dynamical systems0 papers
- studies statistical0 papers
Paper list
From the lab homepage.
20263 papers
- Communications in Optimization TheoryA variational approach to sampling in diffusion processes (opens in a new tab)
- IEEE Transactions on Information TheoryMajorizing measures, codes, and information (opens in a new tab)
- ALTTalagrand meets Talagrand: Upper and lower bounds on expected soft maxima of Gaussian processes with finite index sets (opens in a new tab)
20253 papers
- Mathematics of Control, Signals, and SystemsFitting an immersed submanifold to data via Sussmann's orbit theorem (opens in a new tab)
- Foundations and Trends in Machine LearningGeneralization bounds: Perspectives from information theory and PAC-Bayes (opens in a new tab)
- Geometry, Topology and Control System Design: Proceedings of a Banff International Research Station WorkshopSome remarks on controllability of Liouville's equation (opens in a new tab)
20244 papers
- Conference on Learning for Dynamics and ControlRademacher complexity of neural ODEs via Chen-Fliess series (opens in a new tab)
- International Symposium on Mathematical Theory of Networks and SystemsRevisiting stochastic realization theory using functional Itô calculus (opens in a new tab)
- TMLR (T1)Transformer-based models are not yet perfect at learning to emulate structural recursion (opens in a new tab)
- The Mathematical IntelligencerVariations on a theme by Aristotle (with a little help from Euler, Lagrange, Hamilton, and Pontryagin) (opens in a new tab)
20235 papers
- CDCA constructive approach to function realization by neural stochastic differential equations (opens in a new tab)
- NeurIPS (T1)A unified framework for information-theoretic generalization bounds (opens in a new tab)
- Biological TheoryBiological Autonomy (opens in a new tab)
- Conference on Learning for Dynamics and ControlNonlinear controllability and function representation by neural stochastic differential equations (opens in a new tab)
- IEEE Control Systems LettersVariational principles for mirror descent and mirror Langevin dynamics (opens in a new tab)
Where alumni went17 listed, 0% to industry
Industry 0Academia 6
Also to
- Columbia University
- Computer Science and Automation Indian Institute of Science
- EPFL
Apply to UIUCFall 2027
UIUC Siebel Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL iBT / IELTS
- Fee
- USD 90
Not yet confirmed for this intake.
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