My motivation for research is primarily “the pleasure of finding things out”. I prioritize beauty and surprise over journal prestige or publication quantity. You can visit my Google Scholar profile for a comprehensive list of publications.

My recent research lies at the intersection of computational statistics and machine learning.

Preprints and working papers:

• Stereographic Barker’s MCMC Proposal: Efficiency and Robustness at Your Disposal
  (with Krzysztof Łatuszyński, Gareth O. Roberts, and Jeffrey S. Rosenthal), 2024+. (abstract)
• Adaptive Langevin Monte Carlo Methods for Heavy-tailed Sampling via Weighted Functional Inequalities
  (with Tyler Farghly, Ye He, and Patrick Rebeschini), 2024+. (abstract)
• Gaussian Approximation and Output Analysis for High-dimensional MCMC
  (with Ardjen Pengel and Zhou Zhou), 2024+. (abstract)
• Stereographic Markov Chain Monte Carlo
  (with Krzysztof Łatuszyński and Gareth O. Roberts). (arXiv:2205.12112)

Research articles:

• State-domain Change Point Detection for Nonlinear Time Series Regression
  (with Yan Cui and Zhou Zhou)
  Journal of Econometrics, 234(1):3-27, 2023. (arXiv:1904.11075)
• Complexity Results for MCMC derived from Quantitative Bounds
  (with Jeffrey S. Rosenthal)
  The Annals of Applied Probability, 33(2):1459-1500, 2023. (arXiv:1708.00829)
• Dimension-free Mixing for High-dimensional Bayesian Variable Selection
  (with Quan Zhou, Dootika Vats, Gareth O. Roberts, and Jeffrey S. Rosenthal)
  Journal of the Royal Statistical Society, Series B, 84(5):1751-1784, 2022. (arXiv:2105.05719)
• Spectral Inference under Complex Temporal Dynamics
  (with Zhou Zhou)
  Journal of the American Statistical Association, 117(537):133-155, 2022. (arXiv:1812.07706)
• Optimal Scaling of Random-walk Metropolis Algorithms on General Target Distributions
  (with Gareth O. Roberts and Jeffrey S. Rosenthal)
  Stochastic Processes and their Applications, 130(10):6094-6132, 2020. (arXiv:1904.12157)
• Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes
  (with Shengyang Sun and Daniel M. Roy)
  in Advances in Neural Information Processing Systems (NeurIPS), 2019. (arXiv:1908.07585)
• Lower Bounds on the Probability of a Finite Union of Events
  (with Fady Alajaji and Glen Takahara)
  SIAM Journal on Discrete Mathematics, 30(3):1437-1452, 2016. (arXiv:1401.5543)

PhD thesis:

• Approximating Bayes: Inference and Modeling
  PhD thesis, University of Toronto, 2020.

Other (unpublished) manuscripts:

• Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics
  (with Jeffrey Negrea, Haoyue Feng, Daniel M. Roy, and Jonathan H. Huggins). (arXiv:2207.12395)
• Drift, Minorization, and Hitting Times
  (with Robert M. Anderson, Haosui Duanmu, and Aaron Smith). (arXiv:1910.05904)
• A Bayesian decision-theoretic analysis of Bayesian model misspecification
  (with Daniel M. Roy). (Chapter 4 of PhD thesis)
• A Short Survey on Bounding the Union Probability Using Partial Information
  (with Fady Alajaji and Glen Takahara). (arXiv:1710.07576)
• On Bounding the Union Probability using Partial Weighted Information
  (with Fady Alajaji and Glen Takahara). (arXiv:1506.08331)
  short version in Statistics & Probability Letters, 116:38-44, 2016.