Kevin T. Feigelis

Physics
continual learning, planning, cognitive science
Affiliation:
Center for Mind, Brain, Computation and Technology
MBCT Graduate Alumni

Kevin T. Feigelis is a PhD candidate at Stanford University in the Department of Physics. A New Jersey native, he attended Rutgers University where he was awarded his Bachelor's in Physics, Summa Cum Laude. He is currently a member of the Stanford Neuroscience and Artificial Intelligence Laboratory (SNAIL), directed by Prof. Daniel Yamins. Kevin's research is directed at addressing a fundamental question that lies at the intersection of Artificial Intelligence and Neuroscience: What neural mechanisms enable humans to be such wonderfully adaptive learners? Towards this goal, Kevin both leverages and innovates upon modern advances in Machine Learning to create artificial agents that reason similarly to humans when confronted with complex tasks in realistic simulated environments. Aside from seeking to uncover the underlying brain mechanisms by which animals and humans learn adaptively over their liftimes, he hopes that his discoveries will one day have widespread application, ranging from powering next-generation robotics, to determining effective curricula to educate children, and even diagnosing learning disabilities in themselves. Outside of academia, Kevin enjoys playing music, writing, reading, and hiking.

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.