Sotabase

Career

· Professor, New York University2024–
· Associate Professor (with tenure), New York University2022–2024
· Amazon Scholar, Supply Chain Optimization Technologies (SCOT), Amazon2020–
· Assistant Professor, New York University2019–2022
· Assistant Professor, Cornell University2016–2019
· Researcher, Carnegie Mellon University2014–2016
· PhD in Machine Learning, University of Cambridge2009–2014
· BSc (Hons) in Mathematics and Physics, University of British Columbia2008–

Publications (281)

Averaging Weights Leads to Wider Optima and Better Generalization
Conference on Uncertainty in Artificial Intelligence · 2018
1,903
cited
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Neural Information Processing Systems · 2018
1,313
cited
Deep Kernel Learning
International Conference on Artificial Intelligence and Statistics · 2015
995
cited
A Simple Baseline for Bayesian Uncertainty in Deep Learning
Neural Information Processing Systems · 2019
925
cited
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Neural Information Processing Systems · 2018
856
cited
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
Neural Information Processing Systems · 2020
746
cited
Simple Black-box Adversarial Attacks
International Conference on Machine Learning · 2019
669
cited
Gaussian Process Kernels for Pattern Discovery and Extrapolation
International Conference on Machine Learning · 2013
647
cited
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)
International Conference on Machine Learning · 2015
545
cited
What Are Bayesian Neural Network Posteriors Really Like?
International Conference on Machine Learning · 2021
443
cited
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations
International Conference on Learning Representations · 2022
431
cited
A Cookbook of Self-Supervised Learning
arXiv.org · 2023
366
cited
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
International Conference on Machine Learning · 2020
361
cited
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Neural Information Processing Systems · 2020
319
cited
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
International Conference on Learning Representations · 2019
291
cited
Stochastic Variational Deep Kernel Learning
Neural Information Processing Systems · 2016
283
cited
Does Knowledge Distillation Really Work?
Neural Information Processing Systems · 2021
260
cited
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
International Conference on Learning Representations · 2018
258
cited
Exact Gaussian Processes on a Million Data Points
Neural Information Processing Systems · 2019
248
cited
Bayesian Optimization with Gradients
Neural Information Processing Systems · 2017
229
cited
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