Laixi Shi is an assistant professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, affiliated with the Data Science and AI Institute. Her research focuses on robust and data-efficient reinforcement learning, situated at the intersection of data science, optimization, and statistics. She has been honored with five Rising Star awards across fields including electrical engineering, computer science, machine learning, signal processing, and computational data science. Her PhD thesis received the CMU ECE A.G. Milnes Award (2024). She earned her PhD from Carnegie Mellon University in August 2023, advised by Professor Yuejie Chi. She was a postdoctoral fellow at the California Institute of Technology, hosted by Professors Adam Wierman and Eric Mazumdar. She holds a bachelor’s degree in Electronic Engineering from Tsinghua University.
