B.S., Computer Science, Physics (minor), with Distinction, University of Minnesota, 2021.
Thomas Kagan is an Associate Researcher at SIFT. His academic interests include automated reasoning, automated game playing, stochastic and emergent systems, software and information languages, and combinatoric search; his applied interests include information storage and retrieval, data trust and privacy, and semantic web. After presenting his senior thesis—a genetic algorithm to empirically and adversarially train automated players at poker-like gambling game—Kagan joined SIFT to advance both with some of the best researchers in the field of AI.
Kagan previously worked in R&D for machine learning startup Edammo, Inc. to develop a statistics product that detects Parkinson's disease as early as possible based on how patients perform exercises while attached to motion sensors. He's alumni, co-founder, and former President of UMN.CPP, the University of Minnesota's competitive programming and programming interview club.