Biostatistics Dept Seminar: Robust Decision Making Without Compromising Learning Efficiency
Biostatistics Department Seminar
Title: Robust Decision Making Without Compromising Learning Efficiency
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.
Speakers
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.
Zoom Registration
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2025-2026 Monday Seminar Series
All seminars are held at 12:05 PM via Zoom and onsite. View all seminar information here.