Detecting human behavior from longitudinal data streams

Afsaneh Doryab, School of Computer Science, Carnegie Mellon University

Abstract:

Humans today interact frequently and intensively with a wide range of computing devices. These interactions generate data streams that often offer clues as to their physical and mental states. Analyzing and interpreting these data streams helps intelligent systems to adapt and act according to users’ needs and to provide personalized services and interventions. This capability, however, introduces new technical and social challenges to be addressed. In this talk, I will describe methods to computationally model human behavior from diverse data streams to assess the state of individuals' health and wellbeing. Through a series of systems I have built, I will also describe how models of human behavior can contribute to the seamless integration of technology into people’s lives and to connect community members for opportunistic social and economic exchange.

Bio:

Afsaneh Doryab, Ph.D., is a Computer Scientist and a faculty member in the School of Computer Science at Carnegie Mellon University. Her research contributes to the advancement of Intelligent Human-Centered Computing through computational modeling of human behavior from data streams collected from ubiquitous and mobile devices in the wild. Dr. Doryab has more than 10 years of research experience in ubiquitous and mobile computing, machine learning, computational modeling of human behavior, context-aware computing, and human-computer interaction. Her research has been supported by the National Science Foundation and the National Institute of Health.