Speaker

Xiaojun Bi

Xiaojun Bi is a Professor in the Department of Computer Science at Stony Brook University. His research focuses on Human–AI interaction, AI-powered input technologies, multimodal interaction, probabilistic modeling of user behavior, and accessible interactive systems. He has authored over 50 publications in premier HCI venues such as CHI and UIST, and has received 10 Best Paper or Honorable Mention awards. He is a two-time Google Faculty Research Award recipient, a recipient of the Google Inclusion Research Award, and an inventor on 33 U.S. patents. Xiaojun Bi earned his Ph.D. in Computer Science from the University of Toronto, and received his Master’s degree in Computer Science and Bachelor’s degree in Automation from Tsinghua University. Please refer to his homepage for detailed information about his research, teaching, and professional service.

Talk title: Generative Interaction Models

Abstract: Generative models of human–computer interaction offer a powerful paradigm for designing, optimizing, and evaluating user interfaces. By simulating realistic human behavior, these models enable scalable, controllable, and reproducible experimentation beyond traditional user studies. In this talk, I will present three classes of generative interaction models developed in our lab. First, I will introduce generative adversarial networks (GANs) that model fine-grained typing behavior on soft keyboards, capturing both tap typing and word-gesture dynamics. Second, I will present a reinforcement learning–based model that simulates how blind users navigate and perform menu selections with screen readers, modeling sequential decision-making in non-visual interaction. Third, I will describe Bayesian hierarchical models of human motor behavior, including probabilistic extensions of Fitts’ law. Together, these approaches demonstrate how generative AI and probabilistic modeling can serve as computational proxies for human behavior, moving interaction design toward a simulation-driven science.

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