Assistant Professor, Paul G. Allen School of Computer Science & Engineering

Research focus

Deep reinforcement learning, multi-agent reinforcement learning, reinforcement learning from human feedback, human-AI interaction, social learning

Education

Ph.D. Media Arts and Sciences, Massachusetts Institute of Technology, 2019
M.Sc. Computer Science, University of British Columbia, 2014
B.Sc. Computer Science, University of Regina, 2012
B.A. Psychology, University of Regina, 2012

Natasha Jaques will join the Allen School this winter from Google Brain, where she is a senior research scientist exploring if AI agents benefit from social learning. She has also interned at DeepMind and Google Brain, and was an OpenAI Scholars mentor.

Jaques’s research focuses on social reinforcement learning in multi-agent and human-AI interactions. Her interest in AI learning and collaboration extends into developing multi-training algorithms that create automatic curriculum to help AI learn from each other, and improving mechanisms that allow AI to learn from human partners. Jaques has received numerous awards including Best Demo at NeurIPS, Best of Collection in the IEEE Transactions on Affective Computing, and Best Paper at the NeurIPS workshops on ML for Healthcare and Cooperate AI. Her work has been featured in Science Magazine, MIT Technology Review, Quartz, IEEE Spectrum, Boston Magazine and on CBC Radio.