Distinguished McKnight Professor of Computer Science and Engineering at the University of Minnesota. His research addresses a variety of human-computer interaction and social computing issues, including personalization, eliciting on-line participation, and designing computer systems to improve public health. He is probably best known for his work in collaborative filtering recommenders (the GroupLens project), and for his work in online HIV prevention. He is a Fellow of the ACM and past-President of ACM SIGCHI, the 4500-member Special Interest Group on Human-Computer Interaction. He is a member of the ACM Council, and has served as chair ACM’s Special Interest Group Governing Board, and on ACM’s Executive Committee.
Bridging Computer Science and Behavioral Sciences: Research Examples
Friday, November 6, 10:30 AM
As computers and the Internet become tools and “places” for social interaction, computer scientists and behavioral scientists can work together to explore the social and behavioral aspects of this new environment. In this talk, I will discuss two projects: one that is exploring how to induce people to participate in on-line communities (based on theories of collective action), and another that is exploring the design of online question-answering communities. I will also briefly discuss collaborative research on assessing and reducing HIV risk online.
On-line communities have great potential to rebuild the social capital that has been lost as individuals turn away from structured community events, but experience so far has been discouraging. The vast majority of on-line communities fail–they cease to serve their original goals, whether discussion or content-creation. At the same time, some specific on-line communities have succeeded spectacularly. In this part of the talk, I review the research of CommunityLab, a collaboration that brings together psychology, economics, and computer science to better understand, leverage, and design for user motivation to contribution to on-line communities.
Question-answering sites have emerged as a powerful phenomenon online. These sites serve multiple purposes, from providing a venue for “water-cooler” conversations to providing advice in challenging situations to generating high-quality content worthy of archival. In this part of the talk I review two studies of such sites — one to explore their nature and one to demonstrate the power of machine learning to help improve their usage and design.
This Computer Science Distinguished Lecture is supported by the Google Pittsburgh.
Host: Liz Marai