Assistant Professor, Paul G. Allen School of Computer Science & Engineering
Machine learning and data science for neuroscience and neuroengineering, brain-machine interfaces
Ph.D. Electrical and Computer Engineering, Carnegie Mellon University, 2015
M.S., B.S. Electrical Engineering, Stanford University, 2009
Matthew Golub joins the Allen School this fall from Stanford University where he is a postdoctoral fellow in electrical engineering. At Stanford, Golub is developing deep learning techniques to understand the neural computations that underlie decision making in the brain, for which he received a Pathway to Independence Award from the NIH.
Golub’s research blends machine learning with neuroscience and neuroengineering. His work focuses on probabilistic latent variable models, deep neural networks and other optimization-based frameworks to examine data from large populations of neurons in the brain. He explores how neural populations collectively compute and communicate within the brain in order to support our abilities to generate movements, make decisions and learn from experience. Golub also develops brain-machine interfaces, devices that restore movement and communication for people with spinal cord injuries, neurodegenerative diseases or limb amputations. Golub's work has been recognized by a Pathway to Independence Award from the NIH and the A.G. Milnes Best Thesis Award from the ECE Department at Carnegie Mellon University.