Refreshments are available starting at 10:30 a.m. The seminar will begin at 10:45 a.m.
Abstract
Many scientific and engineering problems are constrained not by model expressiveness, but by the high cost of data acquisition, requiring learning systems to make sequential decisions under uncertainty. In this talk, Quan Nguyen presents a research program on data-efficient learning and decision-making, grounded in probabilistic modeling and decision theory. He will first discuss algorithmic approaches to non-myopic search problems, illustrating how explicit lookahead and long-horizon reasoning improve performance compared to greedy baselines. He will then turn to active learning and information valuation, which are relevant in many scientific discovery settings where the aim is not purely optimization or search, but efficient learning of complex systems. Nguyen will introduce an information-theoretic criterion, Vendi Information Gain, that quantifies the value of candidate observations by jointly capturing informativeness and diversity, and provides stable and interpretable guidance for data acquisition. He will present algorithmic designs and empirical results demonstrating improved data efficiency across synthetic benchmarks and real scientific applications, and conclude by outlining a vision for practical, uncertainty-aware experimentation pipelines that integrate sequential decision-making in AI-driven scientific discovery.
Speaker Biography
Quan Nguyen is a postdoctoral researcher at Princeton University whose work focuses on data-efficient machine learning and sequential decision-making. His research develops principled methods for deciding what data to collect next when observations are costly or limited, with real-world applications in scientific discovery and decision support. His doctoral research received the Washington University in St. Louis Turner Dissertation Award for contributions to learning and decision-making under uncertainty.