Welcome to the Laboratory for Neural Statistics led by Alex Williams at the Center for Neural Science (CNS) at NYU and the Center for Computational Neuroscience (CCN) at the Flatiron Institute.
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We develop statistical models and open-source computational tools to extract insights from neural data. We are particularly interested in characterizing flexibility and variability in neural circuits—e.g., how do the dynamics of large neural ensembles change over the course of learning a new skill, during periods of high attention or task engagement, or during development and aging. Summarizing these processes even on an descriptive level is a difficult and unsolved challenge.
Past projects have explored the use of tensor decomposition as a model of trial-by-trial gain modulation, time warping models to account for trial-by-trial timing variability, spontaneous remapping of spatial coding in entorhinal cortex, and neural sequence detection methods through convolutional matrix factorizations and Bayesian nonparametric mixture models.
Alex is jointly appointed as an assistant professor in the Center for Neural Science at NYU and an associate research scientist + project leader in the Center for Computational Neuroscience at the Flatiron Institute. He performed his postdoctoral and graduate work at Stanford University, respectively with Scott Linderman and Surya Ganguli. Before that, he worked one year at the Salk Institute with Terry Sejnowski and two years at Brandeis University with Eve Marder and Tim O'Leary. He began studying neuroscience at Bowdoin College, where he was advised by Patsy Dickinson.
