Stefano uses tools at the interface of theoretical neuroscience and machine learning to seek understanding of how the brain processes information. His work at the UW aims to elucidate mechanisms of manifold learning seeking to understand how local neuron properties contribute to global circuit function. His work will provide insight into how neural circuits learn, represent and process the immensely complex stimuli encountered in nature. Stefano has a doctoral degree in Theoretical Neuroscience from the Weizmann Institute of Science in Israel and master’s and bachelor’s degrees in Theoretical Physics from Scuola Normale Superiore, Italy.
Argha received his Ph.D. degree in Applied Mathematics at Indian Institute of Technology (Indian School of Mines), Dhanbad, India in 2018. His research interests are Mathematical and Computational Neuroscience. Currently, he is working on neural networks modeling for learning. Apart from research, he enjoys playing Tabla, a musical instrument, reading Bengali story books and cooking.
Merav received her Ph.D, in honor,. from Hebrew University’s Interdisciplinary Center for Neural Computation, in collaboration with Columbia University’s Center for Theoretical Neuroscience. In her Postoctoral work Merav is striving for a deeper understanding of how our brains process information. Her work seeks to identify brain areas that alter their activity during the course of learning a visually-guided behavioral task. Her approach incorporates mathematical tools to reveal, understand and model neural recordings and trained artificial networks