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 bridges high-dimensional sampling, computational statistics, and generative modeling.
Working Papers
- Couplings of Stereographic MCMC Algorithms
(with Zhihao Wang, Pierre Jacob, and Shiva Darshan). - 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, and Gareth O. Roberts). (talk at INI, slides)
Research Articles
- Markov Chain Monte Carlo with Diffusion Paths
(with Han Chen and Sifan Liu). (arXiv:2607.11631) - 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, slides) - 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) - 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. Lead Article. (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)
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.