I am an Assistant Professor in Department of Statistics at Texas A&M University. The best way to contact me is through email.
I am a statistician with broad interests in statistical machine learning and high-dimensional data. The goal of my research is to develop new statistical methods for problems in the emerging ‘omics’ field, including genomics, metabolomics and microbiome, and address the associated algorithmic and inference issues. These new methods and computational tools have the potential for accelerating mechanistic understanding of the complex biological processes and developing vital resources for enabling systematic achievement of many biological, clinical, and public health problems. Networks are important in this learning process because they are well-suited to representing interactions between biomolecules/microbes. Some projects that I’m currently working on are:
For a complete list of publications, see my Google Scholar. For more details about my background, read my CV.
I will join Department of Statistics at Texas A&M University as Assistant Professor, starting from Fall 2019.
Joint work with Drs. Alla Karnovsky, Farsad Afshinnia and George Michailidis on differential network enrichment analysis is featured at Fred Hutch Science Spotlight.
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 Heterogeneity”.
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
In addition, the ideal candidate will also have
Salary Range: Competitive.
How to Apply: Please email the following materials to jingma@fredhutch.org:
Application is closed.
Fred Hutch is excited to host the 2018 Microbiome Symposium on March 16th, 2018! Sessons include