Personalized Ubiquitous Computing through Predictive Behavior Modeling

Zoom link for remote participants, passcode: 453192

Abstract: Humans interact extensively with a wide range of computing devices, including smartphones and wearables, generating data streams that can be analyzed to extract cues about their physical and mental states. This latent information can be used to create more intelligent systems that can anticipate users’ needs and provide personalized services and interventions. However, this capability also poses new technical challenges in data collection, processing, and modeling. In this talk, I will discuss my research approaches to address those challenges. I will showcase how we model multimodal data streams from ubiquitous devices to detect and predict human behavior along with strategies to use these models for delivering personalized, adaptive interventions.

Bio: Afsaneh Doryab is an Assistant Professor of Computer Science and Systems Engineering at the University of Virginia. Her research lies at the intersection of Ubiquitous Computing, Artificial Intelligence, Human-Computer Interaction, and Digital Health. Dr. Doryab focuses on developing computational models of human behavior using data from mobile, wearable, and embedded sensors. She has received support from the National Science Foundation and the National Institutes of Health, and her work has been published venues such as IMWUT, CHI, CSCW, and JMIR. Prior to joining UVA, she was a Systems Scientist in the Human-Computer Interaction Institute at Carnegie Mellon University.