Loading...

Ming Li

Title(s)Professor Of Research Preventive Medicine
Address2001 N. Soto Street
Health Sciences Campus
Los Angeles CA 90033
vCardDownload vCard

    Collapse Biography 
    Collapse Education and Training
    University of Michigan, Ann Arbor, MIPhD11/2002Statistics and Management Science
    University of Michigan, Ann Arbor, MIMS05/1999Statistics
    Nankai University, Tianjin, ChinaBA07/1995Mathematical Statistics

    Collapse Overview 
    Collapse Overview
    Dr. Ming Li is a Professor in the Division of Biostatistics of the Department of Preventive Medicine starting January 2020. Dr. Li now serves as the Director for Data Science Core at Norris Comprehensive Cancer Center.

    Prior to joining USC, Dr. Li was an Associate Professor in the Department of Population and Quantitative Health Sciences and a faculty biostatistician at Center for Proteomics and Bioinformatics at Case Western Reserve University (CWRU) since 2014. During year 2014 to 2019, she was the Director for Biostatistics and Bioinformatics Shared Resource at Case Comprehensive Cancer Center (Case CCC) and served as a full member on the Case CCC Protocol Review and Monitoring Committee. Dr. Li was also the Director for Biostatistics Core in Department of Population and Quantitative Health Sciences. Dr. Li was the primary statistician for CWRU Center for Multimodal Evaluation of Engineered Cartilage.

    Dr. Li’s research interests include proteomic data analysis, cancer biostatistics, statistical and bioinformatics methods for high dimensional data and statistical education and consulting. With more than 18 years working in biostatistics field, Dr. Li has devoted her efforts to two major areas: (1) collaborative research with principle investigators, during the collaboration, Dr. Li played a key role in multiple aspects, including designing experiments, analyzing data, supervising staff statisticians, interpreting results, drafting manuscripts, and writing statistical sections for grants; and (2) high dimensional data analysis, especially methods and software development for proteomics data.