High-Dimensional Data Analysis

Chengzhong Ye is a BCBI Postdoctoral Fellow. His research focuses on developing AI/ML and statistical methods for analyzing large-scale genomic data. He is particularly interested in building deep learning models of genomic sequences that can interpret the functional and phenotypic consequences of genetic variation at scale, with the broader goal of advancing our understanding of human disease and complex traits. His recent work has centered on genomic language models that learn patterns of functional constraint and representations of genomic features through self-supervised learning from...

Beril Erdogdu is a BCBI Postdoctoral Fellow whose research focuses on developing computational and statistical approaches to understand how transcriptomic regulation shapes human brain development, aging, and disease. She is particularly interested in alternative splicing and transcript isoform usage, and in using machine learning to uncover regulatory patterns in large-scale RNA sequencing data. Her recent work identified widespread, age-dependent shifts in transcript isoform usage across the human lifespan and demonstrated that these patterns can accurately predict brain age. She also...

Sandrine Dudoit is Professor in the Department of Statistics and the Division of Biostatistics in the School of Public Health, as well as core faculty member of the Center for Computational Biology. She is also serving as Executive Associate Dean for the College of Computing, Data Science, and Society.

Dudoit's research and teaching activities broadly concern the development and application of statistical learning methods and software for the analysis of high-throughput -omic data in both basic biology and precision health and medicine.

Statistical methodology and theory. Her methodological...

Bin Yu is Chancellor’s Distinguished Professor in the Departments of Statistics and of Electrical Engineering & Computer Sciences, and Center for Computational Biology, and Senior Advisor at The Simons Institute for the Theory of Computing, at the University of California at Berkeley. Her current research interests focus on AI risk and evaluation, veridical data science, explainable and trustworthy AI, statistical machine learning algorithms and theory (e.g. deep learning and decision trees),

interdisciplinary data problems from precision medicine, genomics, and neuroscience. She pioneered the...

Yun Song is a professor of Computer Science and Statistics. He received BS degrees in mathematics and physics from MIT, and a PhD in physics from Stanford University. His current research lies at the intersection of AI/ML, statistics, and biology. He is generally interested in developing robust and efficient computational tools and statistical methods to facilitate the research of the broad biomedical community, while also getting deeply involved in data analysis and interpretation.

He is currently serving as the Faculty Director of the Center for Computational Biology, and a co-Director of...

Ryan Tibshirani is CDSS Chancellor's Professor and Chair of the Department of Statistics at UC Berkeley. From 2011-2022, he was a faculty member in Statistics and Machine Learning at Carnegie Mellon University. He did his Ph.D. in Statistics at Stanford University (2011), with Jonathan Taylor as his thesis advisor.

His research interests lie broadly in statistics, machine learning, and optimization; and he likes to think about problems from different angles: applied, computational, theoretical. More specifically, his interests include high-dimensional statistics, nonparametric estimation...