Fang Han
Doctoral Candidate
Department of Biostatistics
Johns Hopkins University

Room E3039
615 N.Wolfe St.
Baltimore, MD, 21205
fhan AT jhsph DOT edu

I am a fourth-year student in the Hopkins biostatistics department . My advisors are Han Liu and Brian Caffo. 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, and noisy data. I am also interested in applications to functional MRI and equity data.

Before coming to Hopkins, I earned my B.S. degree in probability and statistics from Peking University, and my M.S. degree in biostatistics from University of Minnesota.

My research is supported by Google Research Fellowship in Statistics (2013) .
Thanks, Google!

Publications:

Preprints:

Joint Estimation of Multiple Graphical Models from High Dimensional Dependent Data
Huitong Qiu, Fang Han, Han Liu, and Brian Caffo
on the arXiv:1311.0219, 2013.

Sparse Median Graphs Estimation in a High Dimensional Semiparametric Model
Fang Han, Han Liu, and Brian Caffo
on the arXiv:1310.3223, 2013.

Optimal Sparse Principal Component Analysis in High Dimensional Elliptical Model
Fang Han and Han Liu
on the arXiv:1310.3561, 2013.
(Winner of the 2013 ICSA/ISBS Student Paper Award)

Transition Matrix Estimation in High Dimensional Vector Autoregressive Models
Fang Han and Han Liu
on the arXiv:1307.0293, 2013.

Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models
Zhaoran Wang, Fang Han, and Han Liu
on the arXiv:1307.0164, 2013.

Optimal Rates of Convergence of Transelliptical Component Analysis
Fang Han and Han Liu
on the arXiv:1305.6916, 2013.

2013:

Challenges of Big Data Analysis
Jianqing Fan, Fang Han, and Han Liu
National Science Review, to appear, 2013.

Scale-Invariant Sparse PCA on High Dimensional Meta-elliptical Data
Fang Han and Han Liu
Journal of the American Statistical Association (Theory and Methods) (JASA), to appear, 2013.

Robust Sparse Principal Component Regression
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)

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

Principal Componenet 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)

2012:

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.

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

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

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)

Genome-wide profiling of multiple histone methylations in olfactory cells: further implications for cellular susceptibility to oxidative stress in schizophrenia
with Shin-ichi Kano et al.
Nature: Molecular Psychiatry, 2012.

2011 and Earlier:

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.



Last updated: Sep 11, 2013