Jake Grigsby

I am a first year CS PhD student at UT Austin, working with Prof. Yuke Zhu and the Robot Perception and Learning Lab. My research focuses on deep learning and reinforcement learning. Before coming to Austin, I studied Math and CS at the University of Virginia, where my research was advised by Prof. Yanjun Qi.

Code

Research

Long-Range Transformers for Dynamic Spatiotemporal Forecasting

Grigsby, Jake, Wang, Zhe, and Qi, Yanjun

Preprint 2022

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ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning

Wang, Zhe, Grigsby, Jake, Sekhon, Arshdeep and 1 more author

Conference on Uncertainty in Artificial Intelligence 2022

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RARE: Renewable Energy Aware Resource Management in Datacenters

Venkataswamy, Vanamala, Grigsby, Jake, Grimshaw, Andrew and 1 more author

Workshop on Job Scheduling for Parallel Processing 2022

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Towards Automatic Actor-Critic Solutions to Continuous Control

Grigsby, Jake, Yoo, Jin Yong, and Qi, Yanjun

NeurIPS Workshop on Deep Reinforcement Learning 2021

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A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets

Grigsby, Jake, and Qi, Yanjun

UVA Distinguished Major Thesis 2021

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Deep learning analysis of deeply virtual exclusive photoproduction

Grigsby, Jake, Kriesten, Brandon, Hoskins, Joshua and 3 more authors

Phys. Rev. D 2021

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Measuring Visual Generalization in Continuous Control From Pixels

Grigsby, Jake, and Qi, Yanjun

NeurIPS Workshop on Deep Reinforcement Learning 2020

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