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I am an associate professor in statistics, in economics (adjunct) at the University of Washington, and an affiliated investigator in Fred Hutchinson Cancer Research Center. I obtained my Ph.D. (Biostatistics) from Johns Hopkins University in 2015. The last two years of my graduate study were supported by a Google Ph.D. Fellowship. Previously, I received my B.S. (Mathematics) from Peking University and M.S. (Biostatistics) from University of Minnesota.
I am a current associate editor for Bernoulli (01/2022-present). My research is supported by NSF DMS-1712536, SES-2019363, and DMS-2210019.
I am a mathematical statistician.
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(Beginning 2022 Spring, we fully switch to Canvas and the following websites will no longer be updated.)
- Rank- and graph-based methods - On the failure of the bootstrap for Chatterjee's rank correlation On regression-adjusted imputation estimators of the average treatment effect Azadkia-Chatterjee's correlation coefficient adapts to manifold data Limit theorems of Chatterjee's rank correlation Estimation based on nearest neighbor matching: from density ratio to average treatment effect On Azadkia-Chatterjee's conditional dependence coefficient On boosting the power of Chatterjee's rank correlation (program code) Robust functional principal component analysis via a functional pairwise spatial sign operator On the power of Chatterjee's rank correlation On extensions of rank correlation coefficients to multivariate spaces
On rank estimators in increasing dimensions High dimensional consistent independence testing with maxima of rank correlations ECA: high dimensional elliptical component analysis in non-Gaussian distributions (program code) On inference validity of weighted U-statistics under data heterogeneity
Distribution-free tests of independence in high dimensions Robust inference of risks of large portfolios High dimensional semiparametric scale-invariant principal component analysis Scale-invariant sparse PCA on high dimensional meta-elliptical data CODA: high dimensional copula discriminant analysis High dimensional semiparametric Gaussian copula graphical models - Statistical optimal transport (OT) and OT-induced ranks - Center-outward sign- and rank-based quadrant, Spearman, and Kendall tests for multivariate independence On universally consistent and fully distribution-free rank tests of vector independence Distribution-free consistent independence tests via center-outward ranks and signs (program code) - Mixture models - Nonparametric mixture MLEs under Gaussian-smoothed optimal transport distance Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics A composite likelihood approach to latent multivariate
Gaussian modeling of SNP data with application to genetic association testing - Nonparametric and semiparametric regressions - Adaptive estimation of high dimensional partially linear model (program) (supplement) On a phase transition in general order spline regression Optimal estimation of variance in nonparametric regression with random design On estimation of isotonic piecewise constant signals A provable smoothing approach for high dimensional generalized regression with applications in genomics - Time series analysis - Tail behavior of dependent V-statistics and its applications Estimation and inference on Granger causality in a latent high-dimensional Gaussian vector autoregressive model Probability inequalities for high dimensional time series under a triangular array framework Moment bounds for large autocovariance matrices under dependence Exponential inequalities for dependent V-statistics via random Fourier features An exponential inequality for U-statistics under mixing conditions Joint estimation of multiple graphical models from high dimensional dependent data A direct estimation of high dimensional stationary vector autoregressions - Random matrix theory - Robust scatter matrix estimation for high dimensional distributions with heavy tail Asymptotic joint distribution of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model An extreme-value approach for testing the equality of large U-statistic based correlation matrices On Gaussian comparison inequality and its application to spectral analysis of large random matrices Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution - Others - Sparse median graphs estimation in a high dimensional semiparametric model Challenges of big data analysis Searching for differentially expressed genes by PLS-VIP method - Applications - The Baltimore declaration toward the exploration of organoid intelligence First organoid intelligence (OI) workshop to form an OI community Shell microelectrode arrays (MEAs) for brain organoids Individual level differential expression analysis for single cell RNA-seq data Genome-wide
profiling of multiple histone methylations in olfactory cells: further implications for
cellular susceptibility to oxidative stress in schizophrenia Automated diagnoses of attention defficit hyperactive disorder using MRI Powerful multi-marker association tests: unifying genomic distance-based regression and logistic regression A data-adaptive sum test for disease association with multiple common or rare variants Test selection with application to detecting disease association with multiple SNPs
Robust portfolio optimization Robust estimation of transition matrices in high dimensional heavy-tailed vector autoregressive processes Context aware group nearest shrunken centroids in large-scale genomic studies
Robust sparse principal component regression under the high dimensional elliptical model Transition matrix estimation in high dimensional vector autoregressive models Sparse principal component analysis for high dimensional multivariate time series Principal component analysis on non-Gaussian dependent data Transelliptical component analysis Semiparametric principal component analysis Transelliptical graphical models The nonparanormal SKEPTIC
Kolmogorov dependence theory Transelliptical graphical modeling under a hierarchical latent variable framework |
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