Scott Linderman

Scott Linderman is an Assistant Professor of Statistics and an Institute Scholar in the Wu Tsai Neurosciences Institute at Stanford University. He works at the intersection of machine learning and neuroscience, developing new models and algorithms to better understand complex biological data. Scott was a graduate student at Harvard University with Ryan Adams and Leslie Valiant and then a postdoctoral fellow at Columbia University with Liam Paninski and David Blei. His methodological work has aimed to discover latent network structure in neural spike train data, distill high-dimensional neural and behavioral time series into underlying latent states, and develop the approximate Bayesian inference algorithms necessary to fit probabilistic models at scale. Throughout, he has worked closely with experimental collaborators to apply these methods to recordings from a variety of model organisms, including C. elegans, larval zebrafish, rodents, and primates. His lab at Stanford is developing the computational and statistical techniques necessary to extract scientific insight from next-generation neuroscience datasets, like those being pioneered at the Allen Institute for Brain Science.