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. 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 uncertainty quantification in machine learning, especially within the contexts of decision making, optimal experimental design, and scientific discovery. His most notable research contributions include work on neural network calibration, scalable Gaussian processes, and ensemble methods. Additionally, Geoff has co-founded many widely-used open source software projects, including the GPyTorch, LinearOperator, and CoLA libraries.