Robust Decision Making Without Compromising Learning Efficiency

Abstract: Decision-making artificial intelligence (AI) has revolutionized human life ranging from manufacturing, healthcare, to scientific discovery. However, current AI systems often lack reliability and are highly vulnerable to small changes in complex, interactive, and dynamic environments. My research focuses on achieving both reliability and learning efficiency simultaneously when building AI solutions. These two goals seem conflicting, as enhancing robustness against variability often leads to more complex problems that requires more data and computational resources, at the cost of learning efficiency. But does it have to? In this talk, I overview my work on building reliable decision-making AI without sacrificing learning efficiency, offering insights into effective optimization problem design for reliable AI. To begin, I will focus on reinforcement learning (RL) — a key framework for sequential decision-making, and demonstrate how distributional robustness can be achieved provably without additional training data cost compared to non-robust counterparts. Next, shifting to decision-making in strategic multi-agent systems, I will demonstrate that incorporating realistic risk preferences—a key feature of human decision-making—enables computational tractability, a benefit not present in traditional models. Finally, I will present a vision for building reliable, learning-efficient AI solutions for human-centered applications.

Bio: Laixi Shi will join the Department of Electrical and Computer Engineering at Johns Hopkins University as an Assistant Professor starting in Fall 2025. She is currently a postdoctoral fellow in the Department of Computing and Mathematical Sciences at the California Institute of Technology (Caltech), hosted by Professors Adam Wierman and Eric Mazumdar. She earned her Ph.D. from Carnegie Mellon University (CMU) in August 2023, under the guidance of Professor Yuejie Chi. She holds a B.S. in Electronic Engineering from Tsinghua University (2014–2018) and has completed internships with the Google Research Brain Team and Mitsubishi Electric Research Laboratories. Her research focuses on robust and data-efficient learning at the intersection of data science, optimization, and statistics, with an emphasis on reinforcement learning and inverse problems, spanning both theoretical foundations and practical applications. 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 Ph.D. thesis received the CMU ECE A.G. Milnes Award (2024).