Faculty profile: Paul Resnick

Paul Resnick

Paul Resnick

Paul Resnick is a professor of information and has been a faculty member at UMSI since 1997. He received his master’s and PhD in electrical engineering and computer science from Massachusetts Institute of Technology and a bachelor’s degree in mathematics from the University of Michigan.

Currently Teaching:

  • SI 182: “Building Applications for Information Environments”
  • SI 429: “eCommunities: Analysis and Design of Online Interaction”

Research Interests:

Professor Resnick’s research focuses on socio-technical capital, productive social relations that are enabled by the ongoing use of information and communication technology.

Resnick was a pioneer in the field of recommender systems. Recommender systems guide people to interesting materials based on recommendations from other people. The GroupLens system he helped develop was awarded the 2010 ACM Software Systems Award. His articles have appeared in Scientific American, Wired, Communications of the ACM, The American Economic Review, Management Science, and many other venues. He just published a book (co-authored with Robert Kraut), titled Building Successful Online Communities: Evidence-based Social Design (MIT Press).

His current research projects include:

  • BALANCE: Enhancing Diversity in News and Opinion Aggregators. Results of this project will provide a better understanding of alternative notions of what it means for a set of items to be diverse or balanced, and the range of reactions that different people have to varying levels and presentations of diversity in the news they read.
  • Rumors. A joint project with UMSI faculty Qiaozhu Mei, Rahul Sami, and Dragomir Radev that is funded by the National Science Foundation. This project will develop tools that help people make personal assessments of credibility on the web.
  • Manipulation-Resistant Recommender Systems. A growing concern is that recommender systems may be manipulated by people with a vested interest in having certain items recommended (or not recommended). The goal of this project is to develop general techniques for the design of manipulation-resistant recommender systems as well as specific solutions for applications in which such a recommender could have a significant impact. This is a joint project with Rahul Sami, and is funded by the NSF.
  • Swellness: Social Approaches to Health and Wellness. The Swellness project is investigating the potential for delivering self-managed health and wellness interventions via existing social websites such as Facebook.

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