Kanaka Rajan is a computational neuroscientist and an assistant professor at the Friedman Brain Institute at the Icahn School of Medicine at Mount Sinai in New York. Her research seeks to understand how important cognitive functions — such as learning, remembering and deciding — emerge from the cooperative activity of multi-scale neural processes. Using data from neuroscience experiments, Rajan applies computational frameworks derived from machine learning and statistical physics to uncover integrative theories about the brain that bridge neurobiology and artificial intelligence. Leveraging her unique expertise in the fields of engineering, biophysics and neuroscience, Rajan has pioneered computational approaches for understanding how the brain processes information, and how these processes become disrupted by neuropsychiatric diseases.
Rajan’s work has been recognized with several awards, including the Lamport Research Award in basic science, the Young Investigator award from the Brain & Behavior Research Foundation, the Scholar Award in Understanding Human Cognition from the James S. McDonnell Foundation, the Joseph and Nancy DiSabato Research Scholar Award, the Dyal Research Scholar Award, and a Sloan research fellowship. Her work is supported by R01 funding from the National Institutes of Health through the BRAIN Initiative and a Foundations award from the National Science Foundation.
Prior to joining the faculty at Mount Sinai, Rajan completed her postdoctoral work at Princeton University, where she made significant contributions to the modeling of key neural processes, including feature selectivity and working memory functions in recurrent neural network models of the brain. She received her Ph.D. at Columbia University.