The Ma Lab at Fred Hutchinson Cancer Research Center specializes in statistical and computational methods for big biomedical data. We employ a variety of statistical learning methods, ranging from dimensionality reduction, graphical models, and high-dimensional data analysis, to address the analytical challenges faced with interpreting complex health data. The long-term goals of our research are to enhance biomarker discoveries through powerful and robust statistical inference, and to translate these findings to advance clinical research.
Our current research interests include statistical learning, network analysis, and systems biology, with applications to cancer biology and aging.
Advising: We are recruiting motivated and hard-working students interested in statistical learning for bimedical data. If you are an undergrad or graduate student at the University of Washington, and you are interested in any of the papers or projects listed on this website, send me an email with your interests and CV.
Her paper “REHE: Fast Variance Components Estimation for Linear Mixed Models” proposes a new method for estimating the variance components in linear mixed models.
Jing received an award from The Pathogen Associated Malignancies Integrated Research Center for the pilot project on “Statistical Methods for Network-based Analysis of the Colorectal Cancer Microbiome”.
This project is in collaboration with on Dr. Amanda Phipps (associate professor of epidemiology, University of Washington, and associate professor in the Public Health Sciences Division), Dr. Sam Minot (associate director of data science applications, Hutch Data Core) and Dr. Neelendu Dey (assistant professor in the Clinical Research Division).
Ilias Moysidis obtained his Ph.D. in Statistics from Penn State University in 2021. Welcome, Ilias!