About Me
I am a second-year Ph.D. student in Statistics at the University of Toronto, advised by Radu Craiu and Monica Alexander. I am also a Doctoral Student Fellow at Data Sciences Institute (DSI) and an Affiliate Researcher at Vector Institute.
I approach Bayes as both a computational problem and a service to natural and social sciences:
- On the methodological side, I study scalable Bayesian computation, with particular interest in simulation-based methods, amortized inference, and the two-way interaction between Bayesian computation and deep generative modeling: using generative models to scale approximate Bayesian inference, and using Bayesian principles to make generative modeling more reliable.
- On the applied side, I care about tools that serve scientific discovery rather than merely decorate it. I develop statistical models that help domain experts extract decision-oriented insight from complex data through principled uncertainty quantification.
I initiate and co-organize the department’s Bayesian reading group. If you’d like to collaborate, chat about research/life, or give a guest talk at our reading group, please feel free to drop me an email!
Back in the day, I received my M.S. in Statistics from the University of Chicago in 2024, where I spent two intellectually stimulating years, and was fortunate to be mentored by Dacheng Xiu and Per Mykland. Even earlier, I received my B.S. in Statistics and Financial Mathematics from UofT. Influenced by my prior experiences, I have continued to follow the literature on machine learning in the study of financial markets.
Outside of research, I spend a decent amount of time behind the lens and on the court. I enjoy the theory and practice of photography, and I carry the spirit of sportsmanship in the game of basketball and life.
News
- 2026-08: Will give a contributed poster presentation, “Hierarchical Bayesian Copula Model for Probabilistic Population Projection”, at JSM in Boston. Stop by my poster and say hi!
- 2026-07: Will give a tutorial on Bayesian Workflow using PyMC in STATSTRO: Sampling, Simulation, and Scientific Discovery.
- 2026-06: Attended Uncertainty in AI workshop in Montreal.
- 2026-05: Was awarded the DSI Doctoral Student Fellowship.
- 2026-04: Presented “Hierarchical Bayesian Copula Model for Probabilistic Population Projection” at DoSS Research Day.
- 2026-01: Presented Neural Methods for Amortized Inference at Bayesian Reading Group.
- 2025-12: Contributed Discussion of Model Uncertainty and Missing Data: An Objective Bayesian Perspective was published in Bayesian Analysis.
- 2025-11: Presented Conformal Prediction as Bayesian Quadrature at Bayesian Reading Group.
- 2025-11: Presented A Gentle Introduction to Conformal Prediction at Applied Statistics Reading Group.
- 2025-09: Served as the local organizing committee of the conference Fast and Curious 2: MCMC in Action.
- 2025-06: Served as the UTSSRP student ambassador and instructor, and led 2 tutorial sessions on Applied Statistics.
- 2024-09: Started my PhDeep dive:D