Ryan Badman, PhD

Ryan Badman currently studies topics related to computational neuroscience, improved statistics and modeling at the theory/experiment boundary, curriculum learning, foraging, social neuroscience, and dual-use ethical issues in the militarization of AI research. A major problem he is tackling is how to analyze more complicated neural signals as behavior and tasks get closer to real-world complexity. He holds a physics PhD from Cornell University and has previous postdoctoral positions in human cognitive science (RIKEN, Japan) and computer science and computational chemistry (Brandeis University). Techniques and methods used in ongoing work include neuro-inspired AI, reinforcement learning, curriculum learning, nonlinear dimension reduction and neural manifolds, decision and behavioral modeling, brain imaging analysis, and recurrent neural network simulations.