Talk: Machine Learning for Parent-Child Interactions, 3/15

UMBC Mathematics & Statistics Machine Learning Seminar

2-3:00pm ET, Friday March 15, 2024

412 Mathematics/Psychology & via WebEx 

Human-human interactions are fascinating and complex phenomena. However, it is notoriously difficult to study, given the dynamic and multimodal (e.g., audio, verbal, and visual) nature of the interactions. Modern sensors and the computational capability offered by AI/machine learning allow us to process and analyze fine-grained multimodal interaction data at a large scale. In this talk, I will share two studies that characterize and model parent-child interactions using audio/video data recordings, one in math education and another in early childhood parenting intervention contexts. I will discuss the various analytical methods used to derive insights and the implications for designing AI-supported coaching systems for realizing societal impacts at a large scale. I will also examine the challenges of working with multimodal interaction data.

Lujie Karen Chen is an Assistant Professor in the Department of Information Systems at UMBC, where she leads the Laboratory for Informatics for Human Flourishing. She has almost 20 years of academic and real-world experience in applied machine learning, statistics, data mining, analytics, and visualization. Before joining UMBC, she spent about 15 years at the Auton Lab at Carnegie Mellon University, where she received her Ph.D. in Information Systems and was a fellow in the Program of Interdisciplinary Educational Research.