I
am a Ph.D. candidate in Statistical Science at Duke, working on scalable
Bayesian and variational methods for entity resolution — graphical models,
de-duplication, and streaming record linkage — and on tree embeddings for
learning monotone functions under partial-order constraints. Advised by
Eric B. Laber and Rebecca C. Steorts. M.Sc. in Statistical Science from Duke
(2024); B.Sc. magna cum laude in Mathematics & Data Science from NYU Shanghai (2022).
§01Research threadsDuke · ongoing
I.
Bayesian & Variational Entity Resolution
Scalable Bayesian and variational methods for matching identities across noisy administrative records — graphical entity resolution, de-duplication, and streaming record linkage. With R. C. Steorts.
q*(Λ) = argminq∈Q KL[ q(Λ) ‖ p(Λ | X) ]
II.
Monotone Tree Embeddings
A tree-embedding method for learning monotone functions under partial-order constraints — structured nonparametric regression. With E. B. Laber.
f̂ : 𝒳 → ℝ s.t. x ⪯ x′ ⟹ f̂(x) ≤ f̂(x′)
III.
Synthetic Data & Privacy
A review of how synthetic data acts as both a privacy-preserving release mechanism and a tool for empowering downstream models. With J. P. Reiter, in Annual Review of Statistics.
x̃ ∼ p(x | θ), θ | D ∼ πε,δ
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
2024
Master's Portfolio Award
Duke
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
2024 Fa
STA 240L — Probability for Statistical Inference, Modeling & Data Analysis
Duke
2023 Su
Bayesian Inference for Nuclear Physics — workshop TA