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Biostatistician with experience in clinical trial programming, statistical methodology, and computational biology.

General Information

Full Name Moran Guo (he/him/his)
Languages Chinese (Native), English (Fluent), Japanese (Intermediate)

Profile

  • Biostatistician with experience in clinical trial programming and statistical analysis using SAS and R.
  • Analysis datasets, tables, listings, and figures (TLFs), quality control, and reproducible programming workflows.

Skills and Certification

Certification SAS Certified Specialist: Base Programming Using SAS 9.4
Programming SAS, R (including package development), Python, SQL, LaTeX
Clinical Data Standards CDISC SDTM, ADaM, TLF programming, QC programming
Statistical Methods GLMs, mixed models, Bayesian inference, Monte Carlo simulation, power analysis
Reproducibility Git/GitHub, Quarto/Markdown, CI/testing
Visualization ggplot2, Plotly, R Shiny, matplotlib, seaborn
Computing Linux/Unix, Bash, HPC/Slurm, Docker

Experience

  • Jul 2026 - present

    Waltham, MA

    Biostatistician I
    Quantive Intelligence (formerly LLX Solutions)
    • Support analysis plans, SAS programming of tables, listings, and figures, and report writing under supervision.
    • Debug and review SAS programs, including DATA NULL and PROC REPORT.
    • Work to analysis plans, standard operating procedures, and study guidelines.
    • Interpret and summarize statistical results and prepare written reports under supervision.
  • Mar 2025 - May 2026

    Baltimore, MD

    Graduate Research Assistant, Biostatistics (Chen Lab)
    Johns Hopkins Bloomberg School of Public Health
    • Designed simulation studies of prediction-powered inference to assess bias, variance, and confidence interval coverage.
    • Co-developed the pppower R package for power and sample size planning, with CI-tested functions and documentation.
    • Supervisor: Dr. Yiqun T. Chen
  • Jul 2025 - May 2026

    Baltimore, MD

    Graduate Research Assistant, Neuroimmunology
    Johns Hopkins School of Medicine
    • Led statistical analysis of paired CSF/serum immunoprofiling data with quality control for plate effects, missingness, and outliers.
    • Collaborated with clinicians and immunologists to refine analysis questions and communicate results.
    • Supervisors: Dr. David Acero-Garcés, Dr. Carlos Pardo-Villamizar, Dr. Ingo Ruczinski
  • Aug 2025 - May 2026

    Baltimore, MD

    Graduate Teaching Assistant, Biostatistics
    Johns Hopkins University
    • AS.280.345 Public Health Biostatistics, Fall 2025 and Spring 2026.
    • Led weekly discussion sections (two sections of 30 students each) and office hours in Fall 2025.
    • Graded problem sets, projects, and exams in Spring 2026.
  • Aug 2022 - May 2024

    Rochester, NY

    Undergraduate Research Assistant, Computational Biology (Werren Lab)
    University of Rochester

Education

  • Aug 2024 - May 2026

    Baltimore, MD

    ScM in Biostatistics
    Johns Hopkins Bloomberg School of Public Health
    Cumulative GPA: 3.84/4.0
  • Feb 2021 - May 2024

    Rochester, NY

    B.S. in Biological Sciences: Computational Biology
    University of Rochester
    Cumulative GPA: 3.96/4.0
    • Minors: Mathematics and Japanese
    • Magna Cum Laude and Honors in Research
    • Phi Beta Kappa (2024), Dean’s List

Publications

  • Chen, Y. T., Guo, M., Li, S. Power Analysis for Prediction-Powered Inference. Manuscript under review. [arXiv](https://arxiv.org/abs/2603.16041).
  • Chen, Y. T., Lu, S., Li, S. J., Guo, M., Li, S. Y. Efficient Inference for Noisy LLM-as-a-Judge Evaluation. Accepted at ICML 2026. [arXiv](https://arxiv.org/abs/2601.05420) · [Poster](https://icml.cc/virtual/2026/poster/61131).
  • Acero-Garcés, D., Guo, M., Lopez, D., Moyano, M., et al. High-Throughput Profiling of Anti-Viral and Self-Antigen Antibodies to Identify Etiology in Encephalitis. Abstract accepted for the American Academy of Neurology Annual Meeting, 2026.

Projects

  • Jan 2026 - Mar 2026
    THRIVE (Food Intervention Pilot RCT)
    • Provided statistical consulting in collaboration with clinical researchers.
  • Dec 2025 - Feb 2026
    debiasLLMReporting
    • Compared Rogan–Gladen and PPI/PPI++ methods using over 1,000 Monte Carlo runs per setting, evaluating bias, coverage, and interval width.
    • Project documentation
  • Sep 2025 - Oct 2025
    PlanMCI: Budget-Constrained Study Design Tool
    • Developed an R package for ANCOVA-based power analysis and minimum detectable effect.
    • Built an interactive Shiny application for clinical trial design under budget constraints.
    • R package · Shiny application

Professional Memberships

  • American Statistical Association (ASA)
  • International Chinese Statistical Association (ICSA)