Chong Wu

Chong Wu Biostatistics Ph.D. Candidate Division of Biostatistics University of Minnesota Advisor: Prof. Weihua Guan & Prof. Wei Pan Research interest: high-dimensional data, statistical genetics, big data, and machine learning Curriculum Vitae GitHub Google Scholar

Short Bio

Before coming to Minnesota, I received my bachelor's degree in Applied Math the from Huazhong University of Science and Technology. Now, I am a fifth-year Ph.D. candidate in the Division of Biostatistics at the University of Minnesota. I am co-advised by Prof. Weihua Guan and Prof. Wei Pan and plan to graduate in May, 2018.

Publications [by topic]

1. Wu, C.*, Xu, G., and Pan, W.* (2017+).
   An adaptive test on high dimensional parameters in generalized linear models. Accepted by Statistica Sinica. (* Corresponding author)

2. Zhu, L., Li, Y., Chen, Y., Carrera, C., Wu, C., and Fork, A. (2018).
   Comparison between two post-dentin bond strength measurement methods. Scientific Reports, 8(1):2350. (IF: 4.3)

3. Wu, C. and Pan, W. (2018).
   Integrating eQTL data with GWAS summary statistics in pathwaybased analysis. Accepted by Genetic Epidemiology, early online.
   (This paper won a poster talk (top 24 posters among about 3000 posters) at ASHG 2017 Annual Meeting.)

4. Park, J.Y., Wu, C., Basu, S., McGue, M., and Pan, W. (2018).
   Adaptive SNP set association testing in generalized linear mixed models with application to family studies. Behavior Genetics, 48(1):55–66. (IF: 2.4)

5. Xu, Z., Wu, C., Wei, P., and Pan, W. (2017+).
   A powerful framework for integrating eQTL and GWAS summary data. Accepted by Genetics, early online. (IF: 4.6)

6. Liu, B., Wu, C., Shen, X., and Pan, W. (2017).
   A novel and efficient algorithm for de novo discovery of mutated driver pathways. Annals of Applied Statistics, 17(3):1481–1512.

7. Xu, Z., Wu, C., Pan, W., and Alzheimer’s Disease Neuroimaging Initiative (ADNI). (2017).
   Imaging-wide association study: integrating imaging endophenotypes in GWAS. NeuroImage, 159:159–169.
   (IF: 5.8. This paper won a platform presentation at the American Society of Human Genetics (ASHG) 2017 Annual Meeting.)

8. 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(188):1–25.
   (* Co-first author. IF: 5.0, a leading journal in machine learning area.)

9. Wu, C., Chen, J., Kim, J., and Pan, W. (2016).
   An adaptive association test for microbiome data. Genome Medicine, 8(1):1–12.
   (IF: 7.1. This paper won the 2016 Joint Statistical Meetings (JSM) Distinguished Student Paper Award on Statistics in Genomics and Genetics Section.)

10. Wu, C., Demerath, E. W., Pankow, J. S., Bressler, J., Fornage, M., Grove, M. L., Chen, W., and Guan, W. (2016).
    Imputation of missing covariate values in epigenome-wide analysis of DNA methylation data. Epigenetics, 11(2):132–139.
    (IF: 4.4, the official journal of the DNA Methylation Society.)

11. Bose, M., Wu, C., Pankow, J. S., Demerath, E. W., Bressler, J., Fornage, M., Grove, M. L., Mosley, T. H., Hicks, C., North, K., Kao, W. H., Zhang, Y., Boerwinkle, E., and Guan, W. (2014).
    Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) Study. BMC Bioinformatics, 15(1):1–10. (IF: 2.4)

Selected Manuscripts [by topic]

1. Wu, C. and Pan, W. (2018+).
   Integration of enhancer-promoter interactions with GWAS summary results identifies novel schizophrenia-associated genes and pathways. Submitted.

2. Wu, C., Xu, G., Shen, X., and Pan, W. (2018+).
   An adaptive test on a high-dimensional parameter in the presence of a high-dimensional nuisance parameter in GLM with application to detect gene-environment interactions. Manuscript.
   (Job talk manuscript, to be submitted to Journal of the American Statistical Association.)

Selected Software

• 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.

Teaching

For me, teaching can be described as a rewarding and fulfilling experience. I was a teaching assistant in two courses, Biostatistical Methods I and II, and a guest instructor in Statistical Learning and Data Mining. Along with Prof. Wei Pan, I mentor a Master’s student and a Ph.D. student on some statistical genetics projects.

Codes for Statistical Learning and Data Mining

Selected Presentations

I enjoy sharing ideas and presenting my works. I usually atend JSM, ENAR, and ASHG each year.

• An adaptive association test for microbiome data

  • Presented at 2016 JSM, ENAR

• An adaptive test on high dimensional parameters in GLMs

  • Presented at 2017 JSM, ENAR

Selected Awards

• 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

Contact

Chong Wu
420 Delaware St SE MMC 303
Minneapolis, MN 55455
E-mail: chongwu@umn.edu