Faculty

Matthew Golub is an assistant professor in the Allen School, where his research group focuses on the intersection of neuroscience, neuroengineering, machine learning and data science to develop computational models and algorithms for understanding how single-trial neural population activity drives our abilities to generate movements, make decisions, and learn from experience. He is also a member of the Theory Faculty of the UW Computational Neuroscience Center and of the Training Faculty of the UW Graduate Program in Neuroscience. In addition, Golub is an Affiliate Investigator at the Allen Institute for Neural Dynamics.

Previously, Golub was a Postdoctoral Fellow in the Department of Electrical Engineering at Stanford University, where I was jointly advised by Professors Krishna Shenoy (EE, BioE & Neurobiology; Stanford & HHMI), Bill Newsome (Stanford Neurobiology) and David Sussillo (Stanford EE & Facebook Reality Labs). His postdoctoral work was focused on developing deep learning techniques for understanding population-level neural computations underlying perceptual decision making in the brain. This work was recognized by a K99/R00 Pathway to Independence Award from the National Institutes of Health.

He completed his Ph.D. at Carnegie Mellon University, where he was jointly advised by Professors Byron Yu and Steve Chase. There, he developed brain-computer interfaces as a scientific paradigm for investigating the neural bases of learning and feedback motor control and earned the A.G. Milnes Best Thesis Award by the Department of Electrical & Computer Engineering for his dissertation, “Interpreting neural population activity during feedback motor control.”