Kevyn Collins-Thompson is an Associate Professor with appointments in the School of Information, and Electrical Engineering and Computer Science, College of Engineering (by courtesy).
I do research at the intersection of information retrieval, human-computer interaction (HCI) and machine learning. My primary research goal is to develop algorithms for improving information systems that can reliably and automatically adapt to users and their information needs, especially to help human learning. A key component of my work involves modeling and understanding users' information-seeking behavior and how people interact with information in different contexts.
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.
Areas of interest
Information retrieval, text mining, machine learning, natural language processing, cognitive psychology, computational finance
Honors and awards
SIGIR 2013 Best Student Paper Award
SIGIR 2013 Best Paper Honorable Mention Award