Geoff Pleiss

Geoff Pleiss
Assistant Professor, UBC Department of Statistics
CIFAR AI Chair, Vector Institute
geoff.pleiss <at>

I am an assistant professor in the Department of Statistics at the University of British Columbia, where I am an inaugural member of CAIDA's AIM-SI (AI Methods for Scientific Impact) cluster. I am also a Canada CIFAR AI Chair and a faculty member at the Vector Institute.

Previously, I was a postdoc at Columbia University with John P. Cunningham. I received my Ph.D. from the CS department at Cornell University in August 2020. where I was advised by Kilian Weinberger and also worked closely with Andrew Gordon Wilson.

My research interests intersect deep learning and probablistic modeling. Major focuses of my work include:

  1. neural networks,
  2. uncertainty quantification,
  3. probabilistic modeling, and
  4. Bayesian optimization.

More specifically, some recent interests include

  1. "reliable" neural networks,
  2. ensemble methods, and
  3. Gaussian processes.

I am also an active open source contributior. Most notably, I co-created and maintain the GPyTorch Gaussian process library with Jake Gardner.

Interested in joining my lab? I am looking for prospective M.S. students, Ph.D students, and postdocs with research interests similar to my own. While I am open to strong students with any ML/stats interests, I am particularly hoping to hire lab members for the following research topics:

  • Bayesian optimization,
  • spatiotemporal modeling, and
  • neural network uncertainty quantification.
See the page on joining my lab for information on how to apply/contact me.

Recent and Selected Publications

For a full list of publications, please see my CV or my Google Scholar page.

Recent and Selected Talks

Selected Open Source

For a full list of respositories I actively contribute to, please see my Github page.

  • GPyTorchv1.11 Release

    A implementation of Gaussian processes in PyTorch, designed for speed, modularity, and prototyping.
    Coauthors: Jacob R. Gardner
  • CoLA (Compositional Linear Algebra)Beta Release

    A library for structured linear algebra operations in JaX and PyTorch.
    Coauthors: Andres Potapczynski, Marc Anton Finzi