Haiyan Huang

Photo of Haiyan Huang

Haiyan Huang

Professor
Status
Current
Website
http://www.stat.berkeley.edu/~hhuang
Office / Location
3383 Gateway Building
Email
hhuang@stat.berkeley.edu
Research Expertise and Interests

high-dimensional and integrative genomic data analysis, network modeling, hierarchical classification, translational bioinformatics, Computational design of synthetic polymers

Research Description
http://vcresearch.berkeley.edu/faculty/haiyan-huang

My research spans applied statistics, computational biology, and computational materials science. I am particularly interested in addressing challenging problems arising from emerging data-intensive scientific applications, while developing the theoretical foundations needed to understand and improve the statistical and computational methods used to solve them. My current research focuses on three interconnected directions: (1) developing high-dimensional statistical and computational methods that integrate statistical modeling with the underlying scientific structure of the data; (2) developing robust methods for analyzing noisy and heterogeneous datasets, particularly those collected across different experimental settings and data sources; and (3) understanding and addressing the instability and unreliability of statistical estimation in high-dimensional, structured data. These methodological developments are motivated by applications in biomedical genomics, pharmacogenomics, and the data-guided design of synthetic polymers.

Research Areas
Applications in Biology and Health Sciences
High-Dimensional Data Analysis
Select Publications

 

  1.  Ye Y, Ho C, Jiang CR, Lee WT, Huang H* (2025). Ranking hierarchical multi-label classification results with mLPRs. Electronic Journal of Statistics 19 (2), 5551-5576. *corresponding author
  2. Hu ZT, Yu Y, Chen R, Yeh SJ, Chen B*, Huang H* (2025). Large-Scale Information Retrieval and Correction of Noisy Pharmacogenomic Datasets through Residual Thresholded Deep Matrix Factorization. Briefings in Bioinformatics. 26 (3), bbaf226. *co-corresponding author
  3. Ruan Z, Li S, Grigoropoulos A, Amiri H, Hilburg SL, Chen H, Jayapurna I, Jiang T, Gu Z, Alexander-Katz A, Bustamante C, Huang H, Xu T (2023). Population-based heteropolymer design to mimic protein mixtures. Nature. 615 (251–258).
  4. Li S, Ruan Z, Shen A, Jayapurna I, Xu T, Huang H* (2023). DeepRHP: A hybrid variational autoencoder for designing random heteropolymers as protein mimics.  AAAI workshop on AI to Accelerate Science and Engineering. 2023. *corresponding author
  5. Chi C, Ye Y, Chen B, Huang H* (2021). Bipartite graph-based approach for clustering of cell lines by gene expression-drug response associations. Bioinformatics. 2021 Mar 3. *corresponding author
  6. Liu K, Theusch E, Zhou Y, Ashuach T, Dose AC, Bickel PJ*, Medina MW*, Huang H* (2019). GeneFishing to reconstruct context specific portraits of biological processes. Proceedings of the National Academy of Sciences. 2019 Sep 17;116(38):18943-50.  *co-corresponding authors
  7.  Wang YXR, Liu K, Theusch E, Rotter JI, Medina MW, Waterman M*, Huang H* (2017). Generalized correlation measure using count statistics for gene expression data with ordered samples. Bioinformatics.  2017 Oct 12;34(4):617-24.  *co-corresponding authors
  8. Wang YXR, Jiang K, Feldman LJ, Bickel PJ, Huang H* (2015).g Inferring Gene-Gene Interactions and Functional Modules Using Sparse Canonical Correlation Analysis. Annals of Applied Statistics. 9(1) 300-323. *corresponding author
  9. Wang YXR, Waterman MS*, Huang H* (2014). Gene Coexpression Measures in Large Heterogeneous Samples Using Count Statistics. Proc Natl Acad Sci. USA. 111(46):16371-6. *co-corresponding authors
  10. Li JJ, Huang H*, Bickel PJ*, Brenner S* (2014). Comparison of D. Melanogaster and C. Elegans Developmental Stages, Tissues, and Cells by modENCODE RNA-seq Data. Genome Research. 24: 1084-1101. g*co-corresponding authors
  11. Jiang CR, Liu CC, Zhou XJ, Huang H* (2014). Optimal Ranking in Multi-label Classification Using Local Precision Rates. Statistica Sinica. g24: 1547-1570. *corresponding author
  12. Kim K, Teng S, Jiang K, Feldman L, Huang H* (2012). Using biologically interrelated experiments to identify pathway genes in arabidopsis. Bioinformatics. 28(6), 815-822. *corresponding author
  13. Li JJ, Jiang CR, Brown BJ, Huang H*, Bickel PJ* (2011). Sparse Linear Modeling of RNA-seq Data for Isoform Discovery and Abundance Estimation. Proc Natl Acad Sci. USA. 108 (50) 19867-19872. *co-corresponding authors
  14. Li Q, Brown JB, Huang H, Bickel PJ. (2011). Measuring Reproducibility of High-throughput Experiments. Annals of Applied Statistics. 5(3), 1752-1779.
  15. Huang H*, Liu C, Zhou XJ* (2010). Bayesian Approach to Transforming Public Gene Expression Repositories into Disease Diagnosis Databases. Proc Natl Acad Sci. USA. 107 (15) 6823-6828. *co-corresponding authors
  16. Bickel PJ, Boley N, Brown JB, Huang H, Zhang NR (2010). Subsampling Methods for Genomic Inference. Annals of Applied Statistics. 4(4) 1660-1697.
  17. Bickel P, Brown B, Huang H, Li Q (2009). An overview of recent developments in genomics and associated statistical methods. Philosophical Transactions of the Royal Society A 367, 4313-4337.
  18. Teng S, Huang H (2009). A statistical framework to infer functional gene associations from multiple biologically interrelated microarray experiments. Journal of the American Statistical Association, June 2009, Vol. 104, No. 486.