Bio

I am a PhD student in the Caltech Computer Vision Lab, advised by Pietro Perona. I also collaborate with Meister Lab. My research focuses on sequential decision making in brains and machines. This involves topics spanning computer vision, machine learning / reinforcement learning, systems neuroscience, and behavior.

Currently, I am working on modeling animal decision making in complex tasks and model-based deep reinforcement learning for control and planning. I believe we build better AI by learning about intelligence from its source. On the application side, I am interested in problems that deal with decision making and planning as well as spatiotemporal data.

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News

[10/2019] Our work on rapid learning and intrinsic motivated exploration in complex maze environments for mice has been accepted to the NeurIPS Biological and Artificial Reinforcement Learning workshop

[03/2019] I presented our work on automated training and iterative latent strategy inference at the SoCal Machine Learning Symposium

Preprint

Mouse Academy: high-throughput automated training and trial-by-trial behavioral analysis during learning
Qiao, M., Zhang, T., Segalin, C., Sam, S., Perona, P, Meister, M.