Alex Williams is assistant professor of neural science at New York University and an associate research scientist at the Flatiron Institute’s Center for Computational Neuroscience. His group develops statistical methods and open-source software for making sense of large-scale neural recordings, with contributions spanning tensor decompositions, time-warping models, and metric-space approaches to representational geometry. A recurring theme in his work is that the tools neuroscientists reach for—similarity measures, regression models, model-comparison metrics—embed assumptions that shape the conclusions drawn from data, and that these choices deserve more scrutiny than they typically receive.
Williams holds a Ph.D. in neuroscience from Stanford University and a B.A. from Bowdoin College. His research is supported by the National Institutes of Health, the McKnight Foundation and the Simons Foundation.
