Chong Wu

Portrait of Chong Wu 

Chong Wu

Assistant Professor
Department of Statistics
Florida State University

Research interest: Statistical genetics/genomics, integrative analysis, big data, machine learning, causal inference, and high-dimensional inference.

Curriculum Vitae (Oct 2021)


Google Scholar

Short Bio

I am an Assistant Professor in the Department of Statistics at Florida State University. Prior to FSU, I was a biostatistics Ph.D. student at the University of Minnesota, co-advised by Profs. Weihua Guan and Wei Pan. I received my bachelor's degree in Applied Math from Huazhong University of Science and Technology in 2013.

My research interests are in statistical genetics and genomics, statistical learning, and machine learning. I have obtained diverse training and experiences in developing statistical methods and algorithms for analyzing multiple types of genetic and genomic data, including genome-wide association study (GWAS) data, DNA methylation data, and human microbiome data. My work includes gene- and pathway-based association testing, integrative -omics analysis, new algorithms in clustering, Mendelian randomization, and polygenic risk scores. To practice and facilitate reproducible research, I have developed and currently maintain several software (most in R packages) and their online manuals. My recent work involves integrative analysis of GWAS with multiple sources of omics data.


  • 2022-08: We will join MD Anderson Biostatistics this September. We have enjoyed our time at Tallahassee and appreciate the support from the department! We will continue to collaborate with colleagues at FSU, and we look forward to our new journey at MD Anderson.

  • 2022-07: Our Prostate Cncer R01 (PI: Lang Wu and Chong Wu) has been funded by NCI! This grant aims to identify likely causal proteins and improve the risk prediction for prostate cancer across Africans and Europeans. We are honored to lead this big project with Lang together.

  • 2021-09: Our SUMMIT paper won a poster talk (top 10% among all posters) at ASHG 2021. Congraulations, Zichen!

  • 2021-09: Our polygenic risk score in prostate cancer paper has been published in Cancer Communications.

  • 2021-05: Our COVID-19 integrative analysis paper has been accepted by Genetics in Medicine.

  • 2021-01: Our R03 proposal (Novel Statistical Methods for Multi-omics Data Integration in Alzheimer's Disease) has been funded by NIA.

Selected Publications [by topic]

* Corresponding author

  1. He, Y., Xu, G., Wu, C., and Pan, W. (2021)
    Asymptotically Independent U-Statistics in High-Dimensional Testing. Annals of Statistics, 49(1), 154-181.

  2. Wu, C.*, Bradley J., Li, Y., Wu, L., and Deng, HW. (2021)
    A gene-level methylome-wide association analysis identifies novel Alzheimer’s disease genes. Bioinformatics.

  3. Wu, C.*, Xu, G., Shen, X., and Pan, W.* (2020)
    A regularization-based adaptive test for high-dimensional generalized linear models. Journal of Machine Learning Research, 21(128), 1-67.

  4. Wu, C. (2020)
    Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank. Genetics, 215(4), 947-958.
    (Highlight in Genetics August issue; presented as a platform presentation at ASHG 2019 Annual Meeting)

  5. Wu, C.* and Pan, W.* (2019)
    Integration of methylation QTL and enhancer-target gene maps with schizophrenia GWAS summary results identifies novel genes. Bioinformatics, 35(19), 3576-3583.

  6. Wu, C., Chen, J., Kim, J., and Pan, W. (2016) An adaptive association test for microbiome data. Genome Medicine, 8(1), 1-12.

  7. Wu, C.#, Kwon, S.#, Shen, X., and Pan, W. (2016). A new algorithm and theory for penalized regression-based clustering. Journal of Machine Learning Research, 17(1), 6479-6503. #, co-first author

Selected Software

We put all recent developed software into a lab github page.

  • prclust: Penalized Regression-Based Clustering Methods;

  • MiSPU: Microbiome Based Sum of Powered Score (MiSPU) Tests;

  • GLMaSPU: Adaptive Tests on High Dimensional Parameters in Generalized Linear Models;

  • IWAS: Imaging-Wide Association Study;

  • TWAS: Integrating eQTL and GWAS data;

  • aSPUpath2: Integrating eQTL data with GWAS summary statistics in pathway-based analysis.


  • R03 AG070669, NIA
    Contact PI, (Wu and Bradley), 01-01-21 – 12-31-22
    Novel Statistical Methods for Multi-omics Data Integration in Alzheimer's Disease

  • The Committee on Faculty Research Support, FSU
    PI, 05-07-20 – 06-30-20
    Novel Machine Learning Methods for Alzheimer’s Disease

  • First Year Assistant Professor Grant, FSU
    PI, 05-08-19 - 08-06-19
    Novel Statistical Methods for Transcriptome-wide Association Studies


The opportunity for teaching is one of the key reasons I love academia. For me, teaching can be described as a rewarding and fulfilling experience. I am the instructor for the following courses.

Selected Presentations

I enjoy sharing ideas and presenting my works. I attend JSM, ENAR, and ASHG regularly.


  • Dean's Faculty Travel Award, Florida State University, 2020

  • ENAR Distinguished Student Paper Award, 2019

  • James R. Boen Student Achievement Award, Division of Biostatistics, University of Minnesota, 2018

  • Pre-Doctoral Trainee Award, Association of Chinese Geneticists in America (ACGA), 2017

  • Elected to Delta Omega (Public Health Honorary Society), 2017

  • Elected to Simga Xi (The Scientific Research Society), 2017

  • JSM Distinguished Student Paper Award on Statistics in Genomics and Genetics Section, 2016

  • Doctoral Dissertation Fellowship, University of Minnesota, 2016

  • Travel Award, Computational Neuroscience Summer School, Statistical and Applied Mathematical Sciences Institute, 2015

  • Dean's Ph.D. Scholarship, University of Minnesota, 2013

  • Honorable Mention in Mathematical Contest in Modeling, Consortium for Mathematics and Its Application, 2012

  • National Scholarship, Ministry of Education, China, 2011


Chong Wu
314 OSB, 117 N. Woodward Ave.
P.O. Box 3064330, Tallahassee, FL, 32306-4330