Scholar
Geoff Pleiss
Google Scholar ID: XO8T-Y4AAAAJ
Assistant Professor, University of British Columbia
Citations & Impact
Citations
15,248
H-index
25
i10-index
33
Publications
20
Co-authors
22
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Publications include: 'Asymmetric Duos: Sidekicks Improve Uncertainty' (2025), 'Theoretical Limitations of Ensembles in the Age of Overparameterization' (2025), 'Approximation-Aware Bayesian Optimization' (2024), 'Deep Ensembles Work, But Are They Necessary?' (2022), 'Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization' (2020), 'GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration' (2018). For a full list, see his CV or Google Scholar page.
Research Experience
- Assistant Professor in the Department of Statistics at the University of British Columbia, inaugural member of CAIDA's AIM-SI (AI Methods for Scientific Impact) cluster, Canada CIFAR AI Chair, and faculty member at the Vector Institute.
Education
- Ph.D. from Cornell University's CS department (2020), advised by Kilian Weinberger and worked closely with Andrew Gordon Wilson; Postdoc at Columbia University, mentored by John P. Cunningham.
Background
- Research interests intersect deep learning and probabilistic modeling, specifically focusing on uncertainty quantification, Bayesian optimization, Gaussian processes, and ensemble methods. Co-creator and maintainer of the GPyTorch Gaussian process library.
Miscellany
- Active open source contributor, co-created and maintains the GPyTorch Gaussian process library.
