Geoff Pleiss is an assistant professor in the Department of Statistics at the University of British Columbia, as well as a Canada CIFAR AI Chair
affiliated with the Vector Institute in Ontario. He earned a Ph.D. in Computer Science from Cornell University under the supervision of Prof.
Kilian Weinberger, and worked with Prof. John Cunningham at the Zuckerman Institute of Columbia University. Geoff’s research group specializes in
machine learning methods for scientific applications, emphasizing decision making, robust predictions, uncertainty quantification, and
scalability. His most notable research contributions include work on neural network calibration, ensemble methods, and scalable Gaussian
processes. Additionally, Geoff has co-founded many widely-used open source software projects, including the GPyTorch, LinearOperator, and CoLA
libraries.