His research explores theoretical models, algorithms, and software systems for optimally connecting people with information, especially toward educational goals. His research on personalization has been applied to real-world systems ranging from intelligent tutoring systems to commercial Web search engines. Kevyn has also pioneered techniques for modeling the reading difficulty of text, creating risk-averse search engines that maximize effectiveness while minimizing worst-case errors, and understanding and supporting how people learn language. He received his Ph.D. from the Language Technologies Institute at Carnegie Mellon University, where his advisor was Jamie Callan. Before joining the University of Michigan in 2013, he was a researcher for five years in the Context, Learning, and User Experience for Search (CLUES) group at Microsoft Research.