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 address statistical challenges faced with understanding and interpreting complex health data, and develop novel statistical methods to enhance reproducible biomedical discoveries. Our current research interests include microbiome data analysis and statistical data integration 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.

Postdoc Position Available

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.

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

Position Title: Postdoctoral Researcher in Biostatistics

Summary: The research group led by Dr. Jing Ma in the Division of Public Health Sciences at Fred Hutchinson Cancer Research Center and Prof. Ali Shojaie in the Department of Biostatistics at the University of Washington is seeking for a highly motivated postdoctoral researcher to develop novel statistical methods for problems in genomics and microbiome. The project will focus on methods for analysis of complex biological/ecological systems using novel statistical machine learning tools, including network analysis, high-dimensional regression and inference. This is a unique opportunity for someone who is interested in a collaborative environment and interacting with investigators with diverse experiences. The position is available for 2 years from August 1st 2018 (or by negotiation).

Duties and Responsibilities: The successful candidate will work with Dr. Jing Ma and Prof. Ali Shojaie in developing novel statistical/computational methods in the context of genomics and microbiome. He/she will have opportunities to join collaborative research projects within Fred Hutch and UW as well as external collaborations.

Position Qualifications: The successful candidate will have

  • completed (or nearly completed) a PhD in Statistics/Biostatistics or a closely related area prior to their appointment;
  • demonstrated a capacity to produce high quality research.

In addition, the ideal candidate will also have

  • expertise in statistical machine learning and high-dimensional inference;
  • applied work in genomics and microbiome data analysis.

Salary Range: Competitive.

How to Apply: Please email the following materials to

  • CV;
  • two publication samples or preprints;
  • a one page statement of research interests;
  • contact information of three references.

Application is closed.

Posted 16 Feb 2018 by Jing Ma