Electrical Engineering Alum Maxim Raginsky Elevated to IEEE Fellow
The grade of Fellow recognizes the extraordinary accomplishments of IEEE Senior Members who have contributed to the advancement or application of engineering, science, and technology
Maxim Raginsky (BS/MS ’00, PhD ’02) has been named an Institute of Electrical and Electronics Engineers (IEEE) Fellow in the class of 2025.
Raginsky was cited for “contributions to information-theoretic analysis of stochastic systems in optimization and machine learning.”
Raginsky earned a PhD in 2002 and a bachelor’s and master’s degree in 2000 at Northwestern Engineering, all in electrical engineering. Admitted as an undergraduate student at the McCormick School of Engineering and the University of Chicago’s Department of Physics, Raginsky chose to pursue engineering to create new systems and artifacts. He stayed at Northwestern longer than he initially expected.
“Over the years, I came to appreciate the intellectual environment, the dedicated and engaging instructors, and the opportunities for undergraduate research at Northwestern,” Raginsky said. “My original intent was to graduate with a BS in electrical engineering and work in the fiber optics and optoelectronics industry, but I got hooked on the intellectual challenges and decided to pursue a PhD.”
Raginsky was advised by Horace Yuen, a leader in physics- and mathematics-based quantum and classical cryptography who passed away on January 16.
Raginsky's research interests include probability and stochastic processes, deterministic and stochastic control, machine learning, optimization, and information theory. He is currently a professor and William L. Everitt Fellow in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign.
“Control systems are all around us. They are largely invisible precisely because they work so well; however, as the world around us becomes more and more complex, we need to collect enormous amounts of data and measurements and turn to machine learning and AI to keep pace with this complexity,” Raginsky said. “Because control systems, machine learning, and artificial intelligence are so tightly interconnected, I strongly believe that today’s engineers need to understand all three of these fields equally well, and this philosophy drives my research.”
Raginsky also explores the commonalities and differences between artificially engineered systems that are designed to be adaptive and resilient and biological systems that self-regulate in their environment.
“I have had a longstanding interest in understanding the interplay between communication, control, and learning in complex engineering systems,” Raginsky said. “As machine learning and AI are becoming integrated into every aspect of our technological infrastructure, I feel honored to be recognized by my peers and the IEEE for my contributions to this highly important circle of questions and problems.”