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.

My research interests intersect deep learning and probablistic modeling. More specifically, I'm interested in heuristic and approximate notions of uncertainty from machine learning models, and how they can inform reliable and optimal downstream decisions within the contexts of experimental design and scientific discovery. Major focuses of my work include:

1. neural network uncertainty quantification,
2. Bayesian optimization,
3. Gaussian processes, and
4. ensemble methods.

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

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 2020 where I was advised by Kilian Weinberger and also worked closely with Andrew Gordon Wilson.

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 interested in theoretical or applied work on Bayesian optimization or neural network uncertainty quantification.

See the page on joining my lab for information on how to apply/contact me.