2021.09 -  present

University of Pittsburgh, School of Public Health

I'm currently a fourth year PhD student in biostatistics at the University of Pittsburgh.
Click here to view my publications from my Ph.D. studies at Pitt.

Research Positions:

  • University of Pittsburgh Medical Center (UPMC) Division of Pulmonary, Sep.2022-present.
    Position: Graduate Student Researcher
    Supervisor: Dr. Sally E. Wenzel
    • Coordinated single-cell data collection from 8 severe asthma patients and 5 healthy controls
    • Conducted statistical analyses on gene expression across disease statuses and authored
      comprehensive reports to support data-driven insights
    • Delivered monthly presentations to interdisciplinary teams, contributing to research strategy
  • UPMC Division of Gastroenterology, Jan.2022-present.
    Position: Graduate Student Researcher
    Supervisor: Dr. Richard H. Duerr
    • Established statistical analysis pipelines for cutting-edge single-cell multi-omics data, such as
      DOGMA-seq data and RNA isoform data from long-read sequencing
    • Investigated factors promoting or inhibiting CD4 T-cell activation in Crohn’s disease patients
  • University of Pittsburgh Department of Biostatistics, Sep.2021-present.
    Position: Graduate Student Researcher and Ph.D. student
    Supervisor: Dr. Wei Chen
2018.09 - 2020.05

Columbia University, Mailman School of Public Health

Received M.S. in Biostatistics in 2020
Research Project:
  • Relationship of education level and Apoe4 gene status with the rate of decline
    in cognitive level    , Jun.2019-May.2020.  Mentored by Dr. Zhezhen Jin.
    • Processed data for modeling, along with exploratory analysis
    • Investigated associations between risk factors (educational level and ApoE4 gene status)
      and changes in a variety of cognitive outcomes, including short term memory, recognition
      memory, etc., adjusting for age, gender, etc.
    • Fitted GEE models and included risk factor-by-time interaction terms to evaluate
      associations stated above
    • Checked linearity assumption by plotting marginal residuals vs. explanatory variables
    • Assessed the effects of missing data by multiple imputation and pooled analysis
    • Wrote statistical reports, and presented results to professor and collaborators
Course projects:
  • Introduction to RCT course project, Apr.2019
    • Prepared proposal for a hypothetical NIH phase III two-parallel-group randomized
      clinical trial evaluating efficacy and safety of LY3298176, a promising novel intervention for
      type 2 diabetes mellitus. (Excitingly, LY3298176 is now widely recognized as the phenomenal
      drug tirzepatide!)
    • Addressed key issues related to randomized clinical trials: safety, outcome variables, hypothesis
      formulation, power, stopping rules, etc.
  • Data Science II course project, May.2019
    • Performed descriptive analysis to explore the associations between cardiovascular disease status
      and predictors
    • Built and compared several classifiers, including regularized logistic regression, linear
      discriminant analysis, Naive Bayes, tree-based models and support vector machine to classify
      the subjects based on their cardiovascular disease status
2014.09 - 2018.06

South China University of Technology, School of Mathematics

Received B.S. in Mathematics in 2018
Research project:
  • Predicting miRNA-Disease associations based on similarity networks, Jan-Feb.2017
    Mentored by Dr. Xing Chen
    • Read and presented papers on computational methods for microRNA-disease association
      prediction
    • Based on algorithms provided by the papers, replicated results in the papers through
      Matlab programming
    • Proposed an improved RWRMDA algorithm by using semantic similarity of diseases to construct
      disease similarity matrix
    • Validated accuracy of improved random walk through Matlab programming
Undergraduate Thesis
  • Predicting Safety Level of Underground Gas Pipes, Jun. 2018
    • Applied logistic regression, BP neural network, and fuzzy neural network to predict
      underground gas pipeline safety level
    • Compared performance of these three methods using Matlab and Stata
    • Defended paper and won Outstanding Undergraduate Graduation Thesis Award