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Current PhD Students

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

PhD Candidates

  • Xiaoxuan Cai is currently a 5th-year Ph.D. student working with Forrest W. Crawford

    I am interested in developing statistical tools to solve real-world problems in epidemiology and public health. My current research projects center around evaluating infectious disease outcomes, and use methods including causal inference, survival analysis, and non-parametric estimation.

    Publications: 

    Xiaoxuan Cai, Wen Wei Loh, and Forrest W. Crawford (2019) Identification of causal intervention effects under contagion – Submitted 

    Regina Melendez, Xiaoxuan Cai, Cristine Hine, et al. (2015) Correlates of Cocaine Use in Pregnancy. Yale Medicine Thesis Digital Library

    Personal website: https://xiaoxuan-cai.github.io/

  • Boyang Li

    I am interested in developing statistical methodologies to address scientific problems in medicine and public health, providing insights for more effective disease monitoring, prevention, and treatment. I am particularly drawn to the applications to human genetics and complex diseases. My recent projects focus on identifying the underlying genetic architecture of psychiatric disorders.  

    Papers and citations: https://scholar.google.com/citations?user=JTNog-QAAAAJ&hl=en

  • Mo is currently a 5th year PhD student in the Department of Biostatistics. She is interested in developing statistical methods for analyzing genetic data, including genetic risk prediction and gene-based association analysis.

  • 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

  • Chang Su

    Chang Su is currently a 2nd year doctoral student in the Department of Biostatistics. She is interested in developing statistical methods for analyzing high dimensional data, especially in the field of human genetics. In her recent projects, she is exploring new matrix decomposition methods for detecting patterns and signals in gene expressions.

  • 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