Rollins School of Public Health | Faculty Profile
Emory Rollins School of Public Health

Hao  Wu

Associate Professor

Faculty, Biostatistics and Bioinformatics

I am an associate professor at the Department of Biostatatstics and Bioinformatics, Emory University. My research has been mainly focused on bioinformatics and computational biology. I'm particularly interested in developing statistical methods and computational tools for interpreting large scale genomic data from high-throughput technologies such as microarrays and second generation sequencing.

I am also interested in general machine learning, pattern recognition and large scale data mining methods with applications to biomedical data. I collaborate closely with researchers working on epigenetics to characterize DNA and different types of histone methylations.

I joined the Department of Biostatatstics and Bioinformatics in 2010 as an assistant professor, after obtaining my Ph.D. degree from Johns Hopkins University. I become an associate professor (with tenure) in September 2016. 

For more details please visit my personal webpage at http://haowulab.org.

Contact Information

1518 Clifton Rd., NE ,

Atlanta , GA 30322

Phone: (404)727-8633

Fax: (404)727-1370

Email: hao.wu@emory.edu

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Areas of Interest

  • Bioinformatics
  • Mental Health
  • Statistical Modeling
  • Biostatistics
  • Epigenetics
  • Genetics
  • Genomics

Education

  • Ph.D. in Biostatistics 2010, Johns Hopkins University
  • MHS in Bioinformatics 2010, Johns Hopkins University
  • M.S. in Electrical Engineering 2000, Iowa State University
  • B.S. in Electrical Engineering 1996, Tsinghua University

Courses Taught

  • BIOS 545: R Programming (BIOS Students)
  • BIOS 731: Advanced Statistical Computing
  • BIOS 555: High Th-Put Data Analysis R&B

Publications

  • , , Intensity normalization improves color calling in SOLiD sequencing, Nature Method, 7, 336–337
  • , , Redefining CpG Islands Using a Hidden Markov Model, Biostatistics, 11(3), 499–514
  • , , A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data, Biostatistics, 14(2), 232-43
  • , , Large histone H3 lysine-9 dimethylated chromatin blocks distinguish differentiated from embryonic stem cells, Nature Genetics, 41, 246–250
  • , , Genome-scale epigenetic reprogramming during epithelial to mesenchymal transition, Nature Structural & Molecular Biology , 18(8), 867-74
  • , , Subtelomeric hotspots of aberrant 5-hydroxymethylcytosine-mediated epigenetic modifications during reprogramming to pluripotency, Nature Cell Biology, 15(6), 700-11
  • , , A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data, Nucleic Acid Research, 42(8), e69
  • , , PROPER: Comprehensive Power Evaluation for Differential Expression using RNA-seq, Bioinformatics, ,
  • , , Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicate, Nucleic Acid Research, ,
  • , , Differential methylation analysis for BS-seq data under general experimental design, Bioinformatics, 32(10), 1446-1453