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Kevyn Collins-Thompson


Associate Professor of Information
Associate Professor of Computer Science and Engineering
University of Michigan
School of Information and College of Engineering (affiliate)
Phone: +1-734-615-2132
Fax: +1-734-615-3587
Email: kevynct AT umich . edu

Mailing Address:
School of Information
4341 North Quad
105 S. State Street
Ann Arbor, MI 48109-1285


Kevyn Collins-Thompson is an Associate Professor at the University of Michigan (Ann Arbor), with appointments in the School of Information and Dept. of Electrical Engineering and Computer Science, College of Engineering (affiliate, CSE Division).

My lab focuses on developing intelligent information systems that learn when and how to support people's individual information-seeking goals, especially to help people learn and discover. Examples include search engines that can deliver the right kind of personalized information at the right time, and intelligent tutoring systems that learn when and how to be most helpful in teaching a particular student. Building effective, reliable systems like these will require new theoretical, algorithmic, and methodological advances in multiple research areas, including machine learning, optimization, information retrieval, and human-computer interaction. My current application focus is on education, but I'm also very interested in mobile and health-related applications.

Before joining the University of Michigan in Fall 2013, I was a researcher at Microsoft Research, in the Context, Learning, and User Experience for Search (CLUES) Group.

One area of special interest is development of robust algorithms for risk-sensitive information systems that can effectively balance risk and reward, a research direction that I introduced in my PhD thesis. This work connects information retrieval with portfolio theory and other areas of computational finance to arrive at new models, algorithms, and evaluation methods that account for risk. For example, it shows how the reliability of algorithms for core IR tasks like ranking and query expansion can be greatly improved by using learning frameworks that jointly optimize for risk and reward objectives. I'm also interested in large-scale data and text mining, natural language processing, educational applications of IR and machine learning like predicting reading difficulty and computer-assisted language learning, and how the brain acquires language skills.

My Ph.D. is from the School of Computer Science at Carnegie Mellon University, where my advisor was Jamie Callan. I was a member of the Language Technologies Institute. My undergraduate degree (B.Math.) is from the University of Waterloo. Apparently, I'm not the only one who thinks that CMU and Waterloo are a great combination!
















Other Activities


  • WSDM 2013 Crowdsourced Pairwise Preferences for Readability (.csv file, 9.1Mb): 13857 judged pairs (trusted and untrusted), ~50-word text passages, grades 1-12. Column descriptions are here.
    If you use this dataset, please cite: X. Chen, P.N. Bennett, K. Collins-Thompson, E. Horvitz. Pairwise Ranking Aggregation in a Crowdsourced Setting. Proceedings of WSDM 2013. 193-202.
  • Other Links

    Context, Learning, and User Experience for Search Group's home page.

    DBLP Bibliography

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