Haiyan Huang

Haiyan Huang is a Professor and Chair of the Department of Statistics at the University of California, Berkeley. She is affiliated with the Center for Computational Biology and the Graduate Group in Biostatistics. Her research focuses on computational biology, applied statistics, and high-dimensional genomic data analysis, with an emphasis on network modeling and translational bioinformatics.

Education details are not explicitly listed, but her academic background includes a PhD in Statistics. She has advised numerous graduate students and postdocs, contributing to impactful research in statistical methods for genomics and bioinformatics.

Her work spans statistical methodology development, including sparse canonical correlation analysis, biclustering, and single-cell RNA sequencing analysis. Notable projects include predicting tumor heterogeneity, designing random heteropolymers, and linking protein expression to cell motility.

Publications highlight her contributions to gene network inference, reproducibility in high-throughput experiments, and integrating multi-platform genomic data. Awards include recognition by Faculty of 1000 Biology for her 2010 PNAS work.

Huang teaches advanced courses in computational biology, statistical theory, and consulting. Her lab collaborates with experts in bioengineering, chemistry, and pediatrics, fostering interdisciplinary research. Current projects include precision medicine, deep learning applications, and probabilistic modeling of biological systems.

Her team uses variational inference, hidden Markov models, and machine learning to address challenges in single-cell analysis and pharmacogenomics. The lab’s work has been published in top journals like Nature, PNAS, and Bioinformatics, reflecting her leadership in computational genomics.