Fang Han



About me

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. from the Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health 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 and NSF SES-2019363. An invited short review summarizing ideas presented in my 2021 Bernoulli Society New Researcher Award lecture appeared in Bernoulli News.

Contact


Research Interest

  • Rank- and graph-based methods
  • Statistical optimal transport
  • Mixture models
  • Nonparametric and semiparametric regressions
  • Time series analysis
  • Random matrix theory


Teaching

(Beginning 2022 Spring, we fully switch to Canvas and the following websites will no longer be updated.)



Journal Publications

- Graph-based methods -

Limit Theorems of Chatterjee’s Rank Correlation
Zhexiao Lin and Fang Han

Estimation based on Nearest Neighbor Matching: from Density Ratio to Average Treatment Effect
Zhexiao Lin, Peng Ding, and Fang Han

On Boosting the Power of Chatterjee's Rank Correlation (program code)
Zhexiao Lin and Fang Han

On Azadkia-Chatterjee's Conditional Dependence Coefficient
Hongjian Shi, Mathias Drton, and Fang Han


- Statistical optimal transport (OT) and OT-induced ranks -

Center-Outward Sign- and Rank-Based Quadrant, Spearman, and Kendall Tests for Multivariate Independence
Hongjian Shi, Marc Hallin, Mathias Drton, and Fang Han

On Universally Consistent and Fully Distribution-free Rank Tests of Vector Independence
Hongjian Shi, Marc Hallin, Mathias Drton, and Fang Han
The Annals of Statistics (in press).
(used to titled "Rate-optimality of Consistent Distribution-free Tests of Independence based on Center-outward Ranks and Signs")

Distribution-free Consistent Independence Tests via Center-outward Ranks and Signs
Hongjian Shi, Mathias Drton, and Fang Han
Journal of the American Statistical Association - Theory and Methods, 117(537): 395-410, 2022.


- Rank-based methods -

Robust Functional Principal Component Analysis via Functional Pairwise Spatial Signs
Ken Wang, Sisheng Liu, Fang Han, and Chongzhi Di
Biometrics (in press).

On the Power of Chatterjee's Rank Correlation
Hongjian Shi, Mathias Drton, and Fang Han
Biometrika (in press).

High Dimensional Consistent Independence Testing with Maxima of Rank Correlations
Mathias Drton, Fang Han, and Hongjian Shi
The Annals of Statistics, 48(6):3206-3227, 2020.

ECA: High Dimensional Elliptical Component Analysis in non-Gaussian Distributions
Fang Han and Han Liu
Journal of the American Statistical Association - Theory and Methods, 113(521):252-268, 2018.
(Winner of the 2013 ICSA/ISBS Student Paper Award)

Distribution-Free Tests of Independence in High Dimensions
Fang Han, Shizhe Chen, and Han Liu
Biometrika, 104(4):813-828, 2017.

Robust Inference of Risks of Large Portfolios
Jianqing Fan, Fang Han, Han Liu, and Byron Vickers
Journal of Econometrics, 194(2):298-308, 2016.

High Dimensional Semiparametric Scale-Invariant Principal Component Analysis
Fang Han and Han Liu
IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(10):2016-2032, 2014.

Scale-Invariant Sparse PCA on High Dimensional Meta-Elliptical Data
Fang Han and Han Liu
Journal of the American Statistical Association - Theory and Methods, 109(505):275-287, 2014.

CODA: High Dimensional Copula Discriminant Analysis
Fang Han, Tuo Zhao, and Han Liu
Journal of Machine Learning Research, 14:629-671, 2013.

High Dimensional Semiparametric Gaussian Copula Graphical Models
Han Liu, Fang Han, Ming Yuan, John Lafferty, and Larry Wasserman
The Annals of Statistics, 40(4):2293-2326, 2012.
(Winner of the 2013 David P. Byar Young Investigator Travel Award Sponsored by ASA Biometrics Section)


- Mixture models -

Nonparametric Mixture MLEs under Gaussian-smoothed Optimal Transport Distance
Fang Han, Zhen Miao, and Yandi Shen

Fisher-Pitman Permutation Tests based on Nonparametric Poisson Mixtures with Application to Single Cell Genomics
Zhen Miao, Weihao Kong, Ramya Korlakai Vinayak, Wei Sun, and Fang Han

A Composite Likelihood Approach to Latent Multivariate Gaussian Modeling of SNP Data with Application to Genetic Association Testing
Fang Han and Wei Pan
Biometrics, 68(1):307-315, 2011.


- Nonparametric and semiparametric regressions -

Adaptive Estimation of High Dimensional Partially Linear Model (program) (supplement)
Fang Han, Zhao Ren, and Yuxin Zhu

On a Phase Transition in General Order Spline Regression
Yandi Shen, Qiyang Han, and Fang Han
IEEE Transactions on Information Theory, 67(8): 5283-5304, 2021.

Optimal Estimation of Variance in Nonparametric Regression with Random Design
Yandi Shen, Chao Gao, Daniela Witten, and Fang Han
The Annals of Statistics, 48(6):3589-3618, 2020.

On Estimation of Isotonic Piecewise Constant Signals
Chao Gao, Fang Han, and Cun-Hui Zhang
The Annals of Statistics, 48(2):629-654, 2020.

On Rank Estimators in Increasing Dimensions
Yanqin Fan, Fang Han, Wei Li, and Andrew Zhou
Journal of Econometrics, 214(2):379-412, 2020.

A Provable Smoothing Approach for High Dimensional Generalized Regression with Applications in Genomics
Fang Han, Hongkai Ji, Zhicheng Ji, and Honglang Wang
Electronic Journal of Statistics, 11(2):4347-4403, 2017.


- Time series analysis -

Estimation and Inference on Granger Causality in a Latent High-dimensional Gaussian Vector Autoregressive Model
Yanqin Fan, Fang Han, and Hyeonseok Park

Probability Inequalities for High Dimensional Time Series under a Triangular Array Framework
Fang Han and Wei Biao Wu

Tail Behavior of Dependent V-statistics and its Applications
Yandi Shen, Fang Han, and Daniela Witten

Moment Bounds for Large Autocovariance Matrices under Dependence
Fang Han and Yicheng Li
Journal of Theoretical Probability, 33:1445-1492, 2020.

Exponential Inequalities for Dependent V-statistics via Random Fourier Features
Yandi Shen, Fang Han, and Daniela Witten
Electronic Journal of Probability, 25(7):1-18, 2020.

An Exponential Inequality for U-Statistics under Mixing Conditions
Fang Han
Journal of Theoretical Probability, 31:556-578, 2018.

Joint Estimation of Multiple Graphical Models from High Dimensional Dependent Data
Huitong Qiu, Fang Han, Han Liu, and Brian Caffo
Journal of Royal Statistical Society, Series B, 78(2):487-504, 2016.
(Winner of the 2014 ENAR Distinguished Student Paper Award)

A Direct Estimation of High Dimensional Stationary Vector Autoregressions
Fang Han, Huanran Lu, and Han Liu
Journal of Machine Learning Research, 16:3115-3150, 2015.


- Random matrix theory -

Robust Scatter Matrix Estimation for High Dimensional Distributions with Heavy Tail
Junwei Lu, Fang Han, and Han Liu
IEEE Transactions on Information Theory, 67(8):5283-5304, 2021.

Asymptotic Joint Distribution of Extreme Eigenvalues and Trace of Large Sample Covariance Matrix in a Generalized Spiked Population Model
Zeng Li, Fang Han, and Jianfeng Yao
The Annals of Statistics, 48(6):3138-3160, 2020.

An Extreme-Value Approach for Testing the Equality of Large U-Statistic based Correlation Matrices
Cheng Zhou, Fang Han, Xin-Sheng Zhang, and Han Liu
Bernoulli, 25(2):1472-1503, 2019.

On Gaussian Comparison Inequality and Its Application to Spectral Analysis of Large Random Matrices
Fang Han, Sheng Xu, and Wen-Xin Zhou
Bernoulli, 24(3):1787-1833, 2018.

Statistical Analysis of Latent Generalized Correlation Matrix Estimation in Transelliptical Distribution
Fang Han and Han Liu
Bernoulli, 23(1):23-57, 2017.


- Others -

On Inference Validity of Weighted U-statistics under Data Heterogeneity
Fang Han and Tianchen Qian
Electronic Journal of Statistics, 12(2):2637-2708, 2018.

Sparse Median Graphs Estimation in a High Dimensional Semiparametric Model
Fang Han, Xiaoyan Han, Han Liu, and Brian Caffo
The Annals of Applied Statistics, 10(3):1397-1426, 2016.
(Winner of the 2014 David P. Byar Young Investigator Travel Award Sponsored by ASA Biometrics Section)

Challenges of Big Data Analysis
Jianqing Fan, Fang Han, and Han Liu
National Science Review, 1(3):293-314, 2014.
(Most Read Article in the Journal, NSR 2015 Best Paper)

Searching for Differentially Expressed Genes by PLS-VIP Method
Fang Han, Jingchen Wu, Jiangfeng Xu, and Minghua Deng
Acta Scientiarum Naturalium Universitatis Pekinensis, 45(1):1-5, 2010.


- Applications -

Shell Microelectrode Arrays (MEAs) for brain organoids
with Qi Huang, David Gracias, and et al.

Individual Level Differential Expression Analysis for Single Cell RNA-seq Data
with Mengqi Zhang, Wei Sun, and et al.
Genome Biology, 23:33, 2022.

Genome-Wide Profiling of Multiple Histone Methylations in Olfactory Cells: Further Implications for Cellular Susceptibility to Oxidative Stress in Schizophrenia
with Shinichi Kano, Akira Sawa, and et al.
Nature: Molecular Psychiatry, 18(7):740-742, 2013.

Automated Diagnoses of Attention Defficit Hyperactive Disorder using MRI
with Ani Eloyan, Brian Caffo, and et al.
Frontiers in Systems Neuroscience, 6:61, 2012.
(Winner of the ADHD-200 Global Competition for Achieving the Highest Prediction Performance of Imaging-Based Diagnostic Classification Algorithm)

Powerful Multi-Marker Association Tests: Unifying Genomic Distance-Based Regression and Logistic Regression
Fang Han and Wei Pan
Genetic Epidemiology, 34(7):680-688, 2010.

A Data-Adaptive Sum Test for Disease Association with Multiple Common or Rare Variants
Fang Han and Wei Pan
Human Heredity, 70:42-54, 2010.

Test Selection with Application to Detecting Disease Association with Multiple SNPs
Wei Pan, Fang Han, and Xiaotong Shen
Human Heredity, 69:120-130, 2010.



Peer-Reviewed Conference Publications

Robust Portfolio Optimization
Huitong Qiu, Fang Han, Han Liu, and Brian Caffo
Neural Information Processing Systems (NIPS), 28, 2015.
(Winner of the 2014 Student/Young Researcher Paper Award Sponsored by ASA Risk Analysis Section)

Robust Estimation of Transition Matrices in High Dimensional Heavy-Tailed Vector Autoregressive Processes
Huitong Qiu, Sheng Xu, Fang Han, Han Liu, and Brian Caffo
International Conference on Machine Learning (ICML), 32, 2015.

Context Aware Group Nearest Shrunken Centroids in Large-Scale Genomic Studies
Juemin Yang, Fang Han, Rafael Irizarry, and Han Liu
Journal of Machine Learning Research (AISTATS track), 17, 2014.

Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model
Fang Han and Han Liu
Neural Information Processing Systems (NIPS), 26, 2013. (Spotlight Presentation)

Transition Matrix Estimation in High Dimensional Vector Autoregressive Models
Fang Han and Han Liu
International Conference on Machine Learning (ICML), 30, 2013.

Sparse Principal Component Analysis for High Dimensional Multivariate Time Series
Zhaoran Wang, Fang Han, and Han Liu
Journal of Machine Learning Research (AISTATS track), 16, 2013.
(Winner of the 2013 AISTATS Notable Paper Award)

Principal Component Analysis on non-Gaussian Dependent Data
Fang Han and Han Liu
International Conference on Machine Learning (ICML), 30, 2013.
(Winner of the 2013 ENAR Distinguished Student Paper Award)

Transelliptical Component Analysis
Fang Han and Han Liu
Neural Information Processing Systems (NIPS), 25, 2012. (Oral Presentation). R package SMART available online

Semiparametric Principal Component Analysis
Fang Han and Han Liu
Neural Information Processing Systems (NIPS), 25, 2012.

Transelliptical Graphical Models
Han Liu, Fang Han, and Cun-hui Zhang
Neural Information Processing Systems (NIPS), 25, 2012.

The Nonparanormal SKEPTIC
Han Liu, Fang Han, Ming Yuan, John Lafferty, and Larry Wasserman
International Conference on Machine Learning (ICML), 29, 2012.



Unpublished Technical Reports

Kolmogorov Dependence Theory
Huitong Qiu, Fang Han, Han Liu, and Brian Caffo

Transelliptical Graphical Modeling under A Hierarchical Latent Variable Framework
Han Liu, Fang Han, and Cun-hui Zhang