BIOE 500 Spring 2026 - Dr. Matthew Francis Singh

Precision Modeling and Control for Human Neurostimulation

Abstract: The human brain is notoriously complex and nonlinear. At the same time, it is difficult to access in-vivo, necessitating spatially-coarse methods for both measurement and actuation. Thus, new methods are needed to perform identification and control in the brain medicine setting. I will present two classes of algorithms developed by our group: 1) dual-state parameter estimation algorithms, and 2) ergodic control over long-term dynamics. While motivated by problems in neural engineering, we emphasize the applicability of general principles across a wide range of control problems in which conventional approaches prove computationally intractable or difficult to implement in a medical setting.

Biography: Matthew Singh, an assistant professor in the Department of Statistics at the University of Illinois Urbana-Champaign, explores how nonlinear dynamics and machine learning can untangle the brain's complexity. His work develops personalized brain-stimulation strategies and tackles theoretical challenges in large-scale neural modeling, aiming to bridge math, AI, and cognitive health. He received his PhD in Neuroscience from Washington University in St. Louis. His COmputation and NEurodynamics lab at the Beckman Institute for Advanced Science and Technology bridges mathematical theory and bioinformatics towards modeling, predicting, and enhancing neural computation.