Aquileo | Maxim Raginsky

Maxim Raginsky
Professor
William L. Everitt Fellow

Department of Electrical and Computer Engineering
Coordinated Science Laboratory
University of Illinois at Urbana-Champaign

Affiliated with:
Illinois Institute for Data Science and Dynamical Systems
Center for Advanced Electronics through Machine Learning
Center for the Science of Information

162 CSL
1308 W Main St
Urbana, IL 61801

E-mail: maxim [at] illinois [dot] edu

Research

My interests cover probability and stochastic processes, deterministic and stochastic control, machine learning, optimization, and information theory. Much of my recent research is motivated by fundamental questions in modeling, learning, and simulation of nonlinear dynamical systems, with applications to advanced electronics, autonomy, artificial intelligence, and quantum information science.

Check out my research-related materials:

Complete list of publications (by year)
Curriculum Vitæ [pdf; last updated in June 2026]
I used to maintain a research blog, The Information Structuralist. I now write a substack newsletter about history and philosophy of engineering at The Art of the Realizable.

Graduate students

Tatiana Grigorovich
June Hou
Anson Li

Postdocs

Boya Hou (co-advised with Olgica Milenkovic)

Alumni

Yifeng Chu (Ph.D. 2026; thesis title "Information Theory and Statistical Physics Perspectives on Stochastic Processes")
Tanya Veeravalli (Ph.D. 2025; thesis title "Geometric and Functional Representations of Stochastic Neural Dynamical Systems: From Realization Theory to Controlled Approximation"), now at A^*STAR, Singapore
Joshua Hanson (Ph.D. 2024; thesis title "Geometric and Nonlinear Control Methods in Deep Learning Theory"), now at Sandia National Laboratories
Anant Raj (Marie Curie Postdoctoral Fellow, co-advised with Francis Bach), now Assistant Professor of Computer Science and Automation Indian Institute of Science
Belinda Tzen (Ph.D. 2022 in Computer Science; thesis title ''Applications of Diffusion Processes: Machine Learning, Optimization, and Sampling"), now Distinguished Postdoctoral Research Scientist at Columbia University
Jie Xiong (Ph.D. 2022; thesis title "Neural Ordinary Differential Equation Models for Circuits"), co-advised with Elyse Rosenbaum
Alan Yang (M.S. 2021; thesis title "Input-to-state stable continuous time recurrent neural networks for transient circuit simulation"), co-advised with Elyse Rosenbaum
Ehsan Shafieepoorfard (Ph.D. 2019; thesis title "Rationally inattentive decision-making: Bayesian decision-making with information choice")
Jaeho Lee (Ph.D. 2018; thesis title "Robustness and generalization guarantees for statistical learning of generative models"), now Associate Professor at POSTECH, South Korea
Naci Saldi (postdoc, co-advised with Tamer Başar), now Associate Professor of Mathematics at Bilkent University, Turkey
Yanina Shkel (postdoc, co-advised with Sergio Verdú), now Assistant Professor at EPFL
Aolin Xu (Ph.D. 2016; thesis title "Information-theoretic limitations of distributed information processing")
Daphney-Stavroula Zois (postdoc) now Associate Professor of Computer Engineering at SUNY Albany
Peng Guan (Ph.D. 2015; thesis title "Topics in online Markov decision processes")
Aray Kaliyeva (M.S. 2014; thesis title "Adaptive control of a linear system with quantized state observations")
Soomin Lee (postdoc co-advised with Angelia Nedi&cacute;)
Xiaoyu Guang (M.S. 2014; thesis title "A channel emulation viewpoint of coding theorems")

Teaching

At Illinois

Spring 2026: ECE 553, Optimum Control Systems
Fall 2025: ECE 555, Control of Stochastic Systems
Spring 2025: ECE 490, Introduction to Optimization
Fall 2024: ECE 580, Optimization by Vector Space Methods
Spring 2024: ECE 486, Control Systems
Fall 2023: ECE 515, Control System Theory and Design
Spring 2023: ECE 586, Topics in Decision and Control: Stochastic Differential Equations
Fall 2022: ECE 517, Adaptive and Nonlinear Control
Spring 2022: ECE 486, Control Systems
Fall 2021: ECE 580, Optimization by Vector Space Methods
Spring 2021: ECE 543, Statistical Learning Theory
Fall 2020: ECE 517, Adaptive and Nonlinear Control
Spring 2019: ECE 555, Control of Stochastic Systems
Fall 2018: ECE 486, Control Systems
Spring 2018: ECE 543, Statistical Learning Theory
Fall 2017:
- ECE 563, Information Theory
- ECE 486, Control Systems (co-teaching with Ali Belabbas)
Spring 2017: ECE 498MR, Introduction to Stochastic Systems
Fall 2016: ECE 486, Control Systems
Spring 2016: ECE 498MR, Introduction to Stochastic Systems
Fall 2015:
- ECE 534, Random Processes
- ECE 598MR, Statistical Learning Theory
Fall 2014: ECE 598MR, Statistical Learning Theory
Spring 2014: ECE 486, Control Systems
Fall 2013: ECE 598MR, Statistical Learning Theory
Spring 2013: ECE 486, Control Systems
Fall 2012: ECE 534, Random Processes

At Duke

Spring 2011: ECE 299, Statistical Learning Theory
Fall 2008: BME 171, Signals and Systems

Biography: I received the B.S. and M.S. degrees in 2000 and the Ph.D. degree in 2002 from Northwestern University, all in Electrical Engineering. I have held research positions with Northwestern, the UIUC (where I was a Beckman Foundation Fellow from 2004 to 2007), and Duke University. In 2012, I have returned to the lovely cornfields of Central Illinois. I lead a low-energy lifestyle.