Statistical Learning for Big Biomedical Data

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.

Congratulations, Yue!

Posted 15 Aug 2020 by Jing Ma

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.

Posted 20 Nov 2019 by Jing Ma

I will be visiting Department of Statistics at Texas A&M University as Assistant Professor, during the academic year 2019-2020.

Posted 05 Aug 2019 by Jing Ma

Joint work with Drs. Alla Karnovsky, Farsad Afshinnia and George Michailidis on differential network enrichment analysis is featured at Fred Hutch Science Spotlight.

Posted 20 May 2019 by Jing Ma

Jing received award from The Jayne Koskinas Ted Giovanis Foundation for Health and Policy, joint with Minjoung Kyoung, Michael Konopka, Tara Sigdel, Young Hwan Chang on a proposal titled ``Understanding Therapeutics Failures Through 4D Single-cell Analysis of Metabolic Heterogeneityā€¯.

Posted 25 Jul 2018 by Jing Ma
Published 11 Jun 2021
Published 16 Apr 2021
Networks for compositional data
Ma et al. (2021). Statistical Analysis of Microbiome Data.
Published 20 Feb 2021
Published 05 Feb 2021
Published 07 Sep 2020