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 dimension 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.
Our lab collaborates with Dr. Daniel Promislow on the Dog Aging Project, supported by U19 grant AG057377 from the National Institute of Aging, a part of the National Institutes of Health.
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.
Ilias Moysidis obtained his Ph.D. in Statistics from Penn State University in 2021.
Congratulations, Yue! Yue has done amazing work in dimensionality reduction of structured microbiome data and high-dimensional inference. Check out his paper here and here.
We are looking for dogs for the largest-ever study of aging in canines! You can find out more about the Dog Aging Project here or from this article in New York Times.
I will be visiting Department of Statistics at Texas A&M University as Assistant Professor, during the academic year 2019-2020.
Joint work with Drs. Alla Karnovsky, Farsad Afshinnia and George Michailidis on differential network enrichment analysis is featured at Fred Hutch Science Spotlight.