Fang Han
Department of Biostatistics
Johns Hopkins University

Room E3640
615 N.Wolfe St.
Baltimore, MD 21205
fanghan AT UW DOT edu

I am an assistant professor at Department of Statistics, University of Washington, and currently serve as a visiting assistant professor at the Hopkins biostatistics department.

I study high dimensional statistics and learning theories, including estimation, prediction, and testing in sparse settings and in time series. I am particularly interested in exploring, understanding, and predicting large, complex, noisy, temporally/spatially correlated data. I am also interested in applications to functional MRI and equity data.

I was a finalist of 2016 Forbes 30 under 30 in Science, and a recipient of the Google Ph.D. Fellowship in Statistics from 2013-2015.


Publications:

My Google Scholar Profile

Preprints:

Technical Reports on arXiv

An Exponential Inequality for U-Statistics under Mixing Conditions
Fang Han

On Gaussian Comparison Inequality and Its Application to Spectral Analysis of Large Random Matrices
Fang Han, Sheng Xu, and Wen-Xin Zhou

Rate-Optimal Estimation of High Dimensional Time Series
Fang Han, Sheng Xu, and Han Liu

Peer-Reviewed Journal Publications:

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

Robust Inference of Risks of Large Portfolios
Jianqing Fan, Fang Han, Han Liu, and Byron Vickers
Journal of Econometrics (to appear).

Statistical Analysis of Latent Generalized Correlation Matrix Estimation in Transelliptical Distribution
Fang Han and Han Liu
Bernoulli (to appear).

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.

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.

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)

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)

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.

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.

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.

Peer-Reviewed Journal Publications (Collaborative Work):

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

Automated Diagnoses of Attention Defficit Hyperactive Disorder using MRI
with Ani Eloyan 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)

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.






Last updated: Mar 8, 2016