Kevin Slagle's research studies deep learning and the theory of emergent phenomena in quantum matter and fundamental physics. On the physics side, this includes the theoretical study of superconductivity and topological orders, along with their experimental realizations within quantum simulators and other highly tunable platforms (such as Rydberg arrays and moire materials). Slagle is also interested in using quantum computers to perform fundamentally new tests of quantum mechanics. On the machine learning front, Slagle investigates neural network architecture improvements.

Slagle joined the Electrical and Computer Engineering Department at Rice University as an Assistant Professor in 2022. He received B.S. degrees in Physics and Mathematics at the University of California Irvine in 2011 and a Ph.D. in theoretical condensed matter physics at the University of California Santa Barbara in 2016 with Cenke Xu. Before joining Rice University, he was a Postdoctoral Fellow at the University of Toronto and a Sherman Fairchild Postdoctoral Scholar at Caltech.