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.



  1. Long-Range Transformers for Dynamic Spatiotemporal Forecasting
    Grigsby, Jake, Wang, Zhe,  and Qi, Yanjun
    Preprint 2022
  2. 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
  3. 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
  4. Towards Automatic Actor-Critic Solutions to Continuous Control
    Grigsby, Jake, Yoo, Jin Yong,  and Qi, Yanjun
    NeurIPS Workshop on Deep Reinforcement Learning 2021
  5. A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets
    Grigsby, Jake,  and Qi, Yanjun
    UVA Distinguished Major Thesis 2021
  6. Deep learning analysis of deeply virtual exclusive photoproduction
    Grigsby, Jake, Kriesten, Brandon,  Hoskins, Joshua and 3 more authors
    Phys. Rev. D 2021
  7. Measuring Visual Generalization in Continuous Control From Pixels
    Grigsby, Jake,  and Qi, Yanjun
    NeurIPS Workshop on Deep Reinforcement Learning 2020