Scott W. Linderman arrives at Stanford from postdoctoral work in the Department of Statistics at Columbia University. He completed his PhD in Computer Science at Harvard University, where his thesis, "Bayesian methods for discovering structure in neural spike trains" on networks, point processes, and state space models for neural data analysis, was awarded the 2016 Leonard J. Savage Award for Outstanding Dissertation in Applied Bayesian Methodology from the International Society for Bayesian Analysis. In 2017 Scott coauthored "Reparameterization gradients through acceptance-rejection sampling algorithms," which received the Best Paper Award at the 20th International Conference on Artificial Intelligence and Statistics (AISTATS).
Scott's research is focused on machine learning, computational neuroscience, and the general question of how computational and statistical methods can help decipher neural computation. His work seeks to develop rich statistical models for analyzing neural data, and has helped to reveal latent structure underlying neural activity and its relation to sensory inputs and behavioral outputs. With his appointment on June 1st, he will also join the Stanford Wu Tsai Neurosciences Institute as a Faculty Scholar.