Current Research
I am broadly interested in general-purpose agents who can acquire useful representations and skills without external supervision.
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Reciprocal Reward Influence Encourages Cooperation From Self-Interested Agents
John L. Zhou, Weizhe Hong, and Jonathan C. Kao
NeurIPS, 2024
arXiv /
code /
We introduce an intrinsic reciprocal reward that encourages an agent to reciprocate the influence of other agents’ actions on its own return, and show that this encourages cooperation from other self-interested agents in sequential social dilemmas.
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Neuroscience Cloud Analysis As a Service
Taiga Abe et al. (Co-author)
Neuron, 2022
code /
paper /
An open-source, drag-and-drop platform that uses cloud resources to run modern neuroscience data analyses.
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Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders
Matthew R Whiteway et al. (Co-author)
PLOS Computational Biology, 2021
code /
paper /
A semi-supervised framework that combines the output of supervised pose estimation algorithms with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos.
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Previous Research
My previous work in other fields.
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A Requirement of Protein Geranylgeranylation for Chemokine Receptor Signaling and Th17 Cell Function in an Animal Model of Multiple Sclerosis
Gregory Swan et al. (Co-author)
Frontiers in Immunology, 2021
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We elucidate the critical role of protein geranylgeranylation in regulating T lymphocyte migration and function.
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