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Math & Stats Department Colloquium
Friday, January 31, 2025

Time: 2:30 p.m.  Place: Jeffery Hall, Room 234

Speaker: Xinwei Deng (Virginia Tech)

Title: New Songs for Old Stories: Interface between Experimental Design and Machine Learning

Abstract: Experimental design and machine learning techniques have been widely used in engineering and data science applications. However, these two major areas have not been well integrated, especially on using experimental design thinking to enhance machine learning and using machine learning ideas to improve data collection. In this talk, we will present several recent research on the interface between experimental design and machine learning to facilitate data collection, modeling, and decision-making in the era of data science and AI. Specifically, two research works will be presented. The first part will introduce an active learning approach, called QS-learning, to enable effective modeling and efficient optimization for a new type of data with quantitative-sequence (QS) factors. The QS factor involves a sequence of multiple components associated with their quantities, widely used in health care, logistics, and many other disciplines. The second part will present a variational mutual information (MI) estimator for data and model parameters, leading to a simple and powerful contrastive MI estimator for Bayesian optimal experimental design. The performance of the proposed methods is evaluated by both numerical examples and real applications.

This colloquium will be held concurrently with CANSSI ONTARIO STATISTICS SEMINAR ().