Assistant Professor
Department of Statistics and Actuarial Science
University of Waterloo
Vector Institute
Biography
I am an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, and a faculty affiliate at the Vector Institute. I completed my PhD in Statistical Sciences as a Vanier Scholar at the University of Toronto. During my PhD I was also affiliated with the Vector Institute. My research interests focus around computational statistics and statistical learning theory. Before joining UWaterloo, I was a Postdoctoral Research Scholar at the University of Chicago’s Data Science Institute.
Interests
- Online Learning
- Computational Statistics
- Statistical Learning Theory
- Applied Probability
- Markov Chains & Processes
Education
-
PhD in Statistical Sciences, 2022
University of Toronto
-
MSc in Statistical Sciences, 2017
University of Toronto
-
BMath Mathematical Finance, 2014
University of Waterloo
Publications
(2024).
Concept algebra for (score-based) text-controlled generative models.
Advances in Neural Information Processing Systems.
(2023).
Relaxing the i.i.d. assumption: Adaptively minimax optimal regret via root-entropic regularization.
The Annals of Statistics.
(2021).
Approximations of Geometrically Ergodic Reversible Markov Chains.
Advances in Applied Probability.
(2021).
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers.
Advances in Neural Information Processing Systems.
(2020).
In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors.
Proceedings of the 37th International Conference on Machine Learning.
(2020).
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms.
Advances in Neural Information Processing Systems.
(2019).
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates.
Advances in Neural Information Processing Systems.