Winner
Mingyi Hong (Department of Electrical and Computer Engineering, University of Minnesota)
Citation
Professor Hong's work has transformed our understanding of distributed, nonconvex, and bilevel optimization, advancing algorithms that resolve longstanding open questions on ADMM (Alternating Direction Method of Multipliers), establish rigorous complexity results for stochastic bilevel problems, and illuminate the structure of minimax formulations. His research has profoundly shaped modern applications in machine learning, signal processing, and AI alignment, while also inspiring new directions across theory, algorithms, and computation. His creativity, depth, and impact embody the spirit of the Balas Prize.
Prize committee
Sam Burer (chair), Alberto Del Pia, Wolfram Wiesemann, Stefan Wild