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 work lies at the intersection of computational statistics and machine learning.

Working papers

• Markov Chain Monte Carlo with Diffusion Paths
  (with Han Chen and Sifan Liu).
• Couplings of Stereographic MCMC Algorithms
  (with Zhihao Wang, Shiva Darshan, and Pierre Jacob).
• Rapid Mixing of Stereographic MCMC for Heavy-tailed Sampling
  (with Federica Milinanni and Tyler Farghly).
• 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)

Preprints

• Metropolis-Adjusted Diffusion Models
  (with Kevin H. Lam, Tyler Farghly, Christopher Williams, Yee Whye Teh, and Arnaud Doucet). (arXiv:2605.09654)
• Sub-Cauchy Sampling: Escaping the Dark Side of the Moon
  (with Sebastiano Grazzi, Sifan Liu, and Gareth O. Roberts). (arXiv:2601.11066, Xi’an’s blog)
• Wasserstein and Convex Gaussian Approximations for Non-stationary Time Series of Diverging Dimensionality
  (with Miaoshiqi Liu and Zhou Zhou). (arXiv:2506.08723)
• Stereographic Multiple-Try Metropolis
  (with Zhihao Wang). (arXiv:2505.12487, Zhihao’s slides)
• Gaussian Approximation and Output Analysis for High-dimensional MCMC
  (with Ardjen Pengel and Zhou Zhou). (arXiv:2407.05492, Ardjen’s talk at INI)
• Tuning Stochastic Gradient MCMC 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)

Publications

• 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)
• 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)
• State-Domain Change Point Detection for Nonlinear Time Series Regression
  (with Yan Cui and Zhou Zhou)
  Journal of Econometrics, 234(1):3-27, 2023. Lead Article. (arXiv:1904.11075, published)
• 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)
• Spectral Inference under Complex Temporal Dynamics
  (with Zhou Zhou)
  Journal of the American Statistical Association, 117(537):133-155, 2022. (arXiv:1812.07706, 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)
• 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)

PhD thesis

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

Others

• In a previous life, I was trained as an electronic engineer and primarily worked on wireless communications. Publications from that period can be found on Google Scholar.