Scott W Linderman

Scott W Linderman

Assistant Professor, Statistics
Assistant Professor (By courtesy), Computer Science
Assistant Professor (By courtesy), Electrical Engineering
Member, Bio-X
Member, Wu Tsai Human Performance Alliance
Member, Wu Tsai Neurosciences Institute
PhD, Harvard University, Computer Science (2016)
SM, Harvard University, Computer Science (2013)
BS, Cornell University, Electrical and Computer Engineering (2008)
Affiliation:
Wu Tsai Neuro Faculty Scholars
Affiliated Faculty
Committee:
Interdisciplinary Scholar Awards
Seminar Series
Linderman Lab Website
Profile
swl1@stanford.edu
Scott is an Assistant Professor of Statistics and, by courtesy, Electrical Engineering and Computer Science at Stanford University. He is also an Institute Scholar in the Wu Tsai Neurosciences Institute and a member of Stanford Bio-X and the Stanford AI Lab. His lab works at the intersection of machine learning and computational neuroscience, developing statistical methods to analyze large scale neural data. Previously, Scott was a postdoctoral fellow with Liam Paninski and David Blei at Columbia University, and he completed his PhD in Computer Science at Harvard University with Ryan Adams and Leslie Valiant. He obtained his undergraduate degree in Electrical and Computer Engineering from Cornell University and spent three years as a software engineer at Microsoft before graduate school.

Related Projects

Funded research
Wu Tsai Neurosciences Institute
Design and development of a high-performance intra-cortical speech BCI

Many neurological injuries and diseases such as brainstem stroke and Amyotrophic Lateral Sclerosis (ALS) result in severe speech impairment, drastically reducing quality of life. Recent progress in brain-computer interfaces (BCI) has allowed these individuals to communicate, but performance is still far lower than typical spoken conversation speeds.

Funded research
Wu Tsai Neurosciences Institute
Inference via Abstraction: A framework for efficient Bayesian cognition

We propose a novel framework for efficient Bayesian cognition called Inference via Abstraction (IvA), which learns to approximate complex world models with simpler abstractions that capture main dependencies, but leverage structure in the prior distribution for efficient inference. We instantiate IvA with a combination of probabilistic graphical models and deep neural networks.

Funded research
Wu Tsai Neurosciences Institute
NeuroPlant Initiative

The NeuroPlant Initiative aims to leverage a botanical armamentarium to manipulate the brain — by building a pipeline to explore chemicals synthesized in plants as potential new treatments for neurological disease and as a window into the chemistry of the brain.