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Hao  Wu

Assoc Professor

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

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Contact Information

1518 Clifton Rd., NE ,

Atlanta , GA 30322

Phone: (404)727-8633

Fax: (404)727-1370



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Areas of Interest

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


  • 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 731: Advanced Statistical Computing
  • BIOS 555: High Th-Put Data Analysis R&B
  • BIOS 516: Intr.Lrge Scale Biomed Data An


  • , , Dissecting differential signals in high-throughput data from complex tissues , Bioinformatics, 35, 3898-3905
  • , , TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis, Genome Biology, 20,
  • , , Disease prediction by cell-free DNA methylation , Briefings in bioinformatics, ,
  • , , Two-phase differential expression analysis for single cell RNA-seq , Bioinformatics, ,
  • , , Estimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studies, Genome Biology, ,
  • , , Differential methylation analysis for BS-seq data under general experimental design, Bioinformatics, 32(10), 1446-1453
  • , , Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicate, Nucleic Acid Research, ,
  • , , 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, ,
  • , , Subtelomeric hotspots of aberrant 5-hydroxymethylcytosine-mediated epigenetic modifications during reprogramming to pluripotency, Nature Cell Biology, 15(6), 700-11
  • , , A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data, Biostatistics, 14(2), 232-43
  • , , Genome-scale epigenetic reprogramming during epithelial to mesenchymal transition, Nature Structural & Molecular Biology , 18(8), 867-74
  • , , 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
  • , , Large histone H3 lysine-9 dimethylated chromatin blocks distinguish differentiated from embryonic stem cells, Nature Genetics, 41, 246–250