I
am a statistician working on Bayesian methods for problems where identity, treatment,
and physical structure are all uncertain — entity resolution via partition-valued models,
bandit algorithms for optimal treatment regimes under surrogate outcomes, and
Gaussian-process emulation of high-energy hydrodynamic simulations. M.Sc. in
Statistical Science at Duke, advised by Eric B. Laber and Rebecca C. Steorts.
B.Sc. magna cum laude in Mathematics & Data Science from NYU Shanghai.
§01Research threadsDuke · ongoing
I.
Bayesian Microclustering for Entity Resolution
Bounded microclustering models that match identities across noisy administrative records — partition-valued priors with calibrated cluster-size behavior. With R. C. Steorts.
p(Λ | X) ∝ p(X | Λ) · p(Λ)
II.
Bandits with Surrogate Outcomes
Bandit algorithms for estimating optimal treatment regimes when only partially-ordered surrogate outcomes are observed. With E. B. Laber.
π* = argmaxπ E[Y(π) | S]
III.
Gaussian-Process Emulation for Physics
Modeling outputs of hydrodynamic simulations with Gaussian processes; quantifying uncertainty in Bayesian parameter estimation. With S. Mak and the JETSCAPE collaboration.
f(x) ∼ GP(μ(x), k(x, x′))
IV.
Random Forest Theory
Investigated consistency and asymptotic normality of random-forest estimators (NYU Shanghai senior thesis, with W. Wu and C. Gu).
√n (f̂n − f) ⇒ N(0, σ²)
§02Honors & awards
2023
Dean's Research Award for Master's Students
Duke
2022
Major Honors in Mathematics — top mathematics major
NYU Shanghai
2022
NYU Shanghai Excellence Award — top 20% of class
NYU Shanghai
2018–22
Dean's List, every semester
NYU Shanghai
§03TeachingDuke · NYUSH
2023 Su
Bayesian Inference for Nuclear Physics — workshop TA