My motivation for research is primarily “the pleasure of finding things out”. I prioritize beauty and surprise over journal prestige or publication quantity. My recent research lies at the intersection of computational statistics and machine learning (see “My Research” in MATH newsletter).

Working papers

• Stereographic Barker’s MCMC Proposal: Efficiency and Robustness at Your Disposal
  (with Cameron Bell, Krzysztof Łatuszyński, Gareth O. Roberts, and Jeffrey S. Rosenthal). (talk at INI, slides)
• Adaptive Langevin Monte Carlo Methods for Heavy-tailed Sampling via Weighted Functional Inequalities
  (with Tyler Farghly, Ye He, and Patrick Rebeschini). (abstract, Tyler’s slides)
• Wasserstein and Convex Gaussian Approximations for Non-stationary Time Series of Diverging Dimensionality
  (with Miaoshiqi Liu and Zhou Zhou). (abstract)

2014-present

• Gaussian Approximation and Output Analysis for High-dimensional MCMC
  (with Ardjen Pengel and Zhou Zhou). (arXiv:2407.05492, Ardjen’s talk at INI and PhD thesis)
• 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)
• Stereographic Markov Chain Monte Carlo
  (with Krzysztof Łatuszyński and Gareth O. Roberts)
  The Annals of Statistics, 52(6):2692-2713, 2024. (arXiv:2205.12112, published, online talk, slides)
• 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, published)
• Drift, Minorization, and Hitting Times
  (with Robert M. Anderson, Haosui Duanmu, and Aaron Smith). (arXiv:1910.05904)
• 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, published)
• 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, published)
• 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, published)
• Spectral Inference under Complex Temporal Dynamics
  (with Zhou Zhou)
  Journal of the American Statistical Association, 117(537):133-155, 2022. (arXiv:1812.07706, published)
• 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, published)
• A Bayesian Decision-theoretic Analysis of Bayesian Model Misspecification
  (with Daniel M. Roy). (Ch.4 of PhD thesis)
• On Bounding the Union Probability using Partial Weighted Information
  (with Fady Alajaji and Glen Takahara)
  Statistics & Probability Letters, 116:38-44, 2016. (arXiv:1506.08331, published)
• 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, published)

Before 2014

• I was trained as an electronic engineer and primarily worked on wireless communications. While I no longer work in this area, my past publications can be found on Google Scholar.

PhD thesis

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