About Me

I'm Ionatan, a 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.

Representative Publications

  • Kuperwajs, I., Schütt, H.H, & Ma, W.J. (2022). Improving a model of human planning via large-scale data and deep neural networks. CogSci. [pdf]
  • Kuperwajs, I. & Ma, W.J. (2022). A joint analysis of dropout and learning functions in human decision-making with massive online data. CogSci. [pdf]
  • Kuperwajs, I. & Ma, W.J. (2021). Planning to plan: a Bayesian model for optimizing the depth of decision tree search. CogSci. [pdf]
  • van Opheusden, B., Galbiati, G., Kuperwajs, I., Bnaya, Z., Li, Y., & Ma, W.J. (2021). Revealing the impact of expertise on human planning with a two-player board game. PsyArXiv. [pdf]

    • Selected Talks

    Improving a model of human planning via large-scale data and deep neural networks

    CogSci
    July 30, 2022

    A joint analysis of dropout and learning functions in human decision-making with massive online data

    CogSci
    July 29, 2022

    Planning to plan: a Bayesian model for optimizing the depth of decision tree search

    CogSci
    July 27, 2021

    Model-based and model-free decision-making in a complex planning task

    NYU Center for Neural Science Department Seminar
    February 21, 2020