Planning to plan: a Bayesian model for optimizing the depth of decision tree searchCogSci
July 27, 2021
I'm Ionatan, a fourth-year PhD student in neuroscience at NYU. I work with Wei Ji Ma, studying how people plan sequences of actions in complex environments. This typically involves applying methods from artificial intelligence and reinforcement learning to problems where the mental simulation of possible futures is intractable. Currently I am working on fitting process-level models to large-scale naturalistic data, developing a framework based on Bayesian inference for optimizing planning depth in decision tree search, and training deep neural networks to predict human gameplay in a challenging variant of tic-tac-toe. I'm also an NSF GRFP Fellow and president of the Scientist Action and Advocacy Network. In my free time, I'm usually traveling or hiking for landscape photography, hosting an anime podcast, or playing soccer or basketball.