Kevin Feigelis

The David L Sze and Kathleen Donohue Interdisciplinary Fellow
PhD Student
Department of Physics
Affiliation:
Interdisciplinary Graduate Fellows Alumni
Originally from New Jersey, Kevin hold a B.S. in Physics from Rutgers University, and is currently a PhD candidate in the Stanford University Physics department. During his quest to uncover the fundamental principles which govern our Universe, he became interested in how these same mathematical tools could be applied to elucidate the nature of human-level intelligence. Advised by Daniel Yamins and co-advised by Mark Schnitzer, Kevin works at the interface of computational neuroscience and artificial intelligence -- creating models which mimic the flexible task learning and switching dynamics observed in humans and other animals when exposed to rich, complex environments.

 

   

Related Projects

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
2017
Engineering versatile deep neural networks that model cortical flexibility

In the course of everyday functioning, animals (including humans) are constantly faced with real-world environments in which they are required to shift unpredictably between multiple, sometimes unfamiliar, tasks. But how brains support this rapid adaptation of decision making schema, and how they allocate resources towards learning novel tasks is largely unknown both neuroscientifically and algorithmically.