Kevin Huang

About

Hi! I am a second-year PhD student at the University of Washington, advised by Byron Boots, studying robotics, machine learning, and optimal control. Broadly, I am interested in developing end-to-end intelligent, embodied systems that continuously learn from their environment, as well as robust control and planning in uncertain and dynamic environments.

Previously, I completed my undergrad at Caltech and am fortunate to have worked with Yisong Yue and Anima Anandkumar at Caltech, and Pablo Moscato at the University of Newcastle.

Publications and Manuscripts

Jake Sacks, Rwik Rana, Kevin Huang, Alex Spitzer, Guanya Shi, Byron Boots. Deep Model Predictive Optimization. Submitted to IEEE International Conference on Robotics and Automation (ICRA) 2023.
[paper]

Kevin Huang, Rwik Rana, Alex Spitzer, Guanya Shi, Byron Boots. DATT: Deep adaptive trajectory tracking for quadrotor control. Conference on Robot Learning (CoRL) 2023. (oral presentation, 6.6% acceptance rate)
[paper] [website] [code]

Jermey Bernstein*, Chris Mingard*, Kevin Huang, Navid Azizan, Yisong Yue. Automatic Gradient Descent: Deep Learning without Hyperparameters. 2023.
[paper]

Kevin Huang, Sahin Lale, Ugo Rosalia, Yuanyuan Shi, and Anima Anandkumar. CEM-GD: Cross Entropy Method with Gradient Descent for Model Based Reinforcement Learning, 2021.
[paper] [code]

Pablo Moscato, Mohammad Nazmul Haque, Kevin Huang, Julia Sloan, and Jonathon Corrales de Olivera. Learning to extrapolate using continued fractions: Predicting the critical temperature of superconductor materials, 2021. Submitted to IEEE Transactions on Artificial Intelligence.
[paper]