Clayton Thomas is an assistant professor in the Department of Computer Science at RPI. Previously, he was a postdoc at Yale and Microsoft Research, and a student at Princeton and Purdue. He studies the design of collective decision-making algorithms for settings such as student-school matching, high-stakes auctions, and multi-candidate voting. He is a theorist using principled approaches from computer science and economics to reason about incentives and computation.

A major theme of Clayton's research is how these algorithms can be made explainable. Can matching algorithms be better conveyed through the set of options available to students? How many bids must be disclosed to explain the results of complex auctions? Which voting rules allow for easy election-night explanations of why a candidate won? Clayton's ongoing research agenda addresses all these questions.