Social learning helps humans and animals rapidly adapt to new circumstances, and drives the emergence of complex learned behaviors. My research is focused on Social Reinforcement Learning—developing algorithms that combine insights from social learning and multi-agent training to improve AI agents' learning, generalization, coordination, and human-AI interaction.

I currently hold a joint position as a Senior Research Scientist at Google Brain and Visiting Postdoctoral Scholar at UC Berkeley. I received my PhD from MIT, where I worked on Affective Computing and deep/reinforcement/machine learning. For a brief overview of my thesis, check out this write-up in Computer Vision News. I have interned at DeepMind, Google Brain, and worked as an OpenAI Scholars mentor. I am currently on the faculty job market.

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  • Multi-Agent Learning and Coordination
  • Human-AI Interaction
  • Affective Computing
  • Reinforcement Learning
  • Machine Learning
  • PhD in the Media Lab, 2019

    Massachusetts Institute of Technology

  • MSc in Computer Science, 2014

    University of British Columbia

  • BSc in Computer Science, 2012

    University of Regina

  • BA in Psychology, 2012

    University of Regina