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Current PhD Students

(This is an opt-in listing and does not include all students in the department)

PhD Candidates

  • Selected publications:

    • Lu Q, Li B, Ou D, Erlendsdottir M, Powles RL, Jiang T, Hu Y, Chang D, Jin C, Dai W, He Q, Liu Z, Mukherjee S, Crane PK, Zhao H. A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics, The American Journal of Human Genetics 2017;101:939-964. 
    • Li B, Lu Q, Zhao H. An evaluation of noncoding genome annotation tools through enrichment analysis of 15 genome-wide association studies, Briefings in Bioinformatics 2017:bbx131-bbx131.
  • Jessica Rothman

    I am interested in the mathematical modeling of infectious diseases. Using spatial statistical techniques to incorporate big data such as weather and climate, geographical features like proximity to water, and human travel patterns, I hope to better predict the spread and virulence of disease outbreaks as well as how to best utilize resources to prevent and/or minimize impact. The goals of my research and career are to develop mathematical models that accurately predict infection rates and the spread of diseases so that prevention and treatment efforts can be more focused, effective, and reduce morbidity and mortality.

    Website: LinkedIn

  • My research interests include computational methodologies in cancer genomics and immunomics, particularly, how to jointly leverage genomics and immune information to help us better understanding tumor and therapeutic response. I also work closely with YSM community as a statistical consultant. 

    Selected publications:

    • Tang, D., Park, S. and Zhao, H., 2019. NITUMID: Nonnegative Matrix Factorization-based Immune-TUmor
         MIcroenvironment Deconvolution. Bioinformatics. 
    • Presley, C., Tang, D., Pamela R S, Cary P Gross., 2018. Broad-Based Genomic Sequencing, Treatment      Outcomes, and Survival in Advanced Non-Small Cell Lung Cancer Patients Treated in the Community            Setting, The Journal of the American Medical Association 
    •  Wu, P., Li, T., Li, R., Jia, L., Zhu, P., Liu, Y., Chen, Q., Tang, D., Yu, Y. and Li, C., 2017. 3D    Genome of Multiple Myeloma Reveals Spatial Genome Disorganization Associated with Copy Number Variations. Nature communications, 8, p.1937.

    Website: Twitter