Haiyan Huang
Visiting Professor of Statistics and Data Science
Research Interests
Professor Huang's teaching and research interests span statistical inference, statistical modeling, and data science, with a strong focus on developing, refining, and applying quantitative methods that advance scientific discovery across diverse fields. She is particularly committed to building methodological frameworks that illuminate complex patterns in high-dimensional data and support robust, interpretable scientific conclusions.
Professor Huang's work engages deeply with a wide range of application domains, including genomics, pharmacogenomics, biomedical research, and materials science. In these areas, she leverages rigorous statistical and machine-learning approaches to uncover underlying biological mechanisms, improve analytical accuracy, and facilitate the development of new scientific insights and technologies. Her interdisciplinary contributions aim to accelerate progress in both fundamental research and real-world applications, bridging methodological innovation with impactful scientific outcomes.
Email
In addition to her position at MBZUAI, Professor Huang is a Professor at the University of California, Berkeley, USA.
- Postdoctoral Fellow, Harvard University.
- Research Fellow, University of California.
- Ph.D. in Applied Mathematics from the University of Southern California.
- Bachelor of Science in Mathematics from Peking University.
- IMS elected fellow, 2022.
- ASA elected fellow, 2022.
- Myra Samuels Memorial Lecture, Statistics, Purdue University, 2020.
- Ye Y., Ho C., Jiang C.R., Lee W.T., Huang H.: "Ranking Hierarchical Multi-label Classification Results with mLPRs." Electronic Journal of Statistics. 19(2) 5551-5576, 2025.
- Hu Z.T., Yu Y., Chen R., Yeh S.J., Chen B., Huang H.: "Large-Scale Information Retrieval and Correction of Noisy Pharmacogenomic Datasets through Residual Thresholded Deep Matrix Factorization." Briefings in Bioinformatics. 26 (3), bbaf226, 2025.
- Ruan Z., Li S., Grigoropoulos A., Amiri H., Hilburg S.L., Chen H., Jayapurna I., Jiang T., Gu Z., Alexander-Katz A., Bustamante C., Huang H., Xu T.: "Population-based heteropolymer design to mimic protein mixtures." Nature. 615 (251-258), 2023.
- Li S., Ruan Z., Shen A., Jayapurna I., Xu T., Huang H.: "DeepRHP: A hybrid variational autoencoder for designing random heteropolymers as protein mimics. 2023 AAAI workshop on AI to Accelerate Science and Engineering, 2023. Selected for oral presentation.
- Mcloughlin A., Huang H.: "Shared Differential Expression-Based Distance Reflects Global Cell Type Relationships in Single-Cell RNA Sequencing Data." Journal of Computational Biology 29 (8), 867-879, 2022.
- Vlassakis J., Hansen L.L., Higuchi-Sanabria R., Zhou Y., Tsui C.K., Dillin A., Huang H., Herr A.E.: "Measuring expression heterogeneity of single-cell cytoskeletal protein complexes." bioRxiv. 2021 Jan 1:2020-09. Nature Communications. 12 (1), 4969, 2o21.
- Chi C., Ye Y., Chen B., Huang H. "Bipartite graph-based approach for clustering of cell lines by gene expression-drug response associations." Bioinformatics, 2021.
- Geldert A., Huang H., Herr A.E.: "Probe-target hybridization depends on spatial uniformity of initial concentration condition across large-format chips." Scientific Reports (Nature Publisher Group), 2020.
- Jiang T., Hall A., Eres M., Hemmatian Z., Qiao B., Zhou Y., Ruan Z., Couse A.D., Heller W.T., Huang H., de la Cruz M.O., Rolandi M., Xu T.: "Single-chain heteropolymers transport protons selectively and rapidly." Nature, 2020.
- Liu K., Theusch E., Zhou Y., Ashuach T., Dose A.C., Bickel P.J., Medina M.W., Huang H.: "GeneFishing to reconstruct context specific portraits of biological processes." Proceedings of the National Academy of Sciences, USA, 2o19.
- Wang Y.X.R,. Jiang K., Feldman L., Bickel P.J., Huang H.: "Inferring Gene Association Networks Using Sparse Canonical Correlation Analysis." Annals of Applied Statistics, 2015.
- Wang Y.X.R., Waterman M., Huang H.: "Gene coexpression measures in large heterogenous samples using count statistics." Proceedings of the National Academy of Sciences, 2014.
- Jiang C.R., Liu C.C., Zhou X.J., Huang H.: "Optimal Ranking in Multi-label Classification Using Local Precision Rates." Statistica Sinica, 2014.
- Wang Y.X.R., Huang H.: "Review on statistical methods for gene network reconstruction using expression data." Journal of Theoretical Biology, 2014.
- Kim K., Jiang K., Teng S., Feldman L.J., and Huang H.: "Using biologically interrelated experiments to identify pathway genes in arabidopsis." Bioinformatics, 2012.
- Li J.J., Jiang C.R., Brown J.B., Huang H., Bickel P.J.: "Sparse Linear Modeling of RNA-seq Data for Isoform Discovery and Abundance Estimation." Proceedings of the National Academy of Sciences, 2011.
- Li Q., Brown J.B., Huang H. and Bickel P.J.: "Measuring reproducibility of high-throughput experiments." Annals of Applied Statistics, 2011.
- Huang H., Liu C., Zhou X.J.: "Bayesian Approach to Transforming Public Gene Expression Repositories into Disease Diagnosis Databases." Proceedings of the National Academy of Sciences, 2010.