I’m interested in machine learning for robotic perception and control, with a focus on potential applications in autonomous vehicles. My recent work focuses on high-speed obstacle avoidance using information theoretic model-predictive control. Before that, I did some work in learning a real-time, dynamic bipedal locomotion controller for rough terrain. My advisor is Dr. Byron Boots, who directs the University of Washington Robot Learning Lab.
A. Shaban, C. Cheng, N. Hatch, and B. Boots. “Truncated Back-Propagation for Bilevel Optimization.” Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019). (PMLR) (arXiv)
N. Hatch. “Group Theory: An Introduction and an Application.” University of Chicago VIGRE REU; August 2011. (paper)
I joined the University of Washington CS PhD program in Winter 2020, after two and a half years in the Georgia Tech ML PhD program. Prior to that, I worked for three years as a software engineer at eSpark Learning, an education technology company that adaptively curates educational resources for grade school students based on their test scores. I completed my Bachelor of Science degree at the University of Chicago in 2014 with a double major in mathematics and computer science.