Matthew Golub

Timeline

1. 2009–2015

   PhD at Carnegie Mellon

   He earns a B.S. and an M.S. in electrical engineering at Stanford University in 2009, then a Ph.D. in electrical and computer engineering at Carnegie Mellon University in 2015, advised by Byron Yu and Steve Chase. His dissertation on interpreting neural population activity during feedback motor control develops brain-computer interfaces as a scientific paradigm for studying learning and motor control, and it earns the A.G. Milnes Best Thesis Award from the Department of Electrical and Computer Engineering.

2. 2014–2015

   Brain-computer interface studies of learning

   In 2014, he is a coauthor of "Neural constraints on learning" in Nature, in which monkeys controlling a cursor through motor cortex activity learn patterns inside an intrinsic manifold more readily than patterns outside it. In 2015, he is first author of an eLife paper that uses a brain-machine interface in rhesus monkeys to study internal models, and finds that a mismatch between the subjects' internal models and the actual interface explains roughly 65% of movement errors.

3. 2019–2020

   Recurrent networks and neural dynamics

   In 2019, he is a coauthor, with Niru Maheswaranathan, Alex Williams, Surya Ganguli and David Sussillo, of a study that reverse engineers recurrent networks trained for sentiment classification and finds an approximate line attractor in their dynamics. In 2020, he coauthors the Annual Review of Neuroscience article "Computation Through Neural Population Dynamics" with Saurabh Vyas, David Sussillo and Krishna Shenoy, listing the Department of Electrical Engineering at Stanford University as his affiliation.

4. By 2022

   Postdoctoral fellow at Stanford

   By 2022, he is a postdoctoral fellow in electrical engineering at Stanford University, advised by Krishna Shenoy, Bill Newsome and David Sussillo. His work on deep learning and dynamical systems techniques for understanding how neural population activity supports decision-making earns an NIH K99/R00 Pathway to Independence Award.

5. 2022

   Professor at the Allen School

   In 2022, he joins the University of Washington as an assistant professor in the Paul G. Allen School of Computer Science & Engineering and directs the Systems Neuroscience & AI Lab, known as SNAIL or the Golub Lab. 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.

6. 2025

   MR-LFADS for brain communication

   In 2025, his team, with Ph.D. student Belle Liu as lead author, publishes MR-LFADS at the International Conference on Machine Learning. The deep learning method identifies communication between brain regions even when some regions are not recorded, and on real neural recordings it predicts how disrupting one region affects another. At the Allen School's 2025 research showcase, he also describes an active-learning algorithm that chooses which neurons to stimulate with lasers, so that a model of the neural network can be learned from little data.

7. As of October 2026

   Directing the SNAIL lab

   He is an assistant professor in the Allen School at the University of Washington and a visiting scientist at the Allen Institute for Neural Dynamics. His research group develops computational models and algorithms for understanding how single-trial neural population activity drives movement, decision making and learning, and he teaches courses on machine learning and its application to neuroscience and neuroengineering.