TALK    [MERL Seminar Series 2026] Laixi Shi presents talk titled Robust Decision Making Without Compromising Learning Efficiency

Date & Time:

Wednesday, January 14, 2026; 1:00 PM

Abstract:

Decision-making artificial intelligence (AI) has revolutionized human life ranging from healthcare, daily life, 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 paying statistical premium (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, though agentic and multi-agentic AI systems.

Speaker:

Laixi Shi
Johns Hopkins University

Laixi Shi is an Assistant Professor of Electrical and Computer Engineering at Johns Hopkins University and is affiliated with the Data Science and Artificial Intelligence Institute. She received her Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University (CMU) in August 2023; her dissertation received the CMU ECE A.G. Milnes Award. Prior to joining Johns Hopkins, she was a postdoctoral fellow in the Department of Computing and Mathematical Sciences at the California Institute of Technology (Caltech). She holds a B.S. in Electronic Engineering from Tsinghua University (2014–2018) and has conducted research with the Google Research Brain Team and Mitsubishi Electric Research Laboratories. Her research focuses on robust and data-efficient decision making at the intersection of data science, optimization, and statistics, with an emphasis on reinforcement learning and inverse problems. Her work spans both theoretical foundations and practical applications. She has been honored with five Rising Star awards across electrical engineering, computer science, machine learning, signal processing, and computational data science. She serves as a Program Committee Member for Learning for Dynamics & Control Conference (L4DC) and area chair for Conference on Parsimony and Learning (CPAL).

MERL Host:

Dehong Liu

Research Areas:

Artificial Intelligence, Control, Machine Learning