BDSY 2025

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Mentored Research

During BDSY, students are organized into teams of around 10, each working on a distinct project in biomedical or public health research. Each team is guided by one or more faculty mentors and graduate student assistants who provide support throughout the project. For the 2025 program, topics included causal inference, genetics, and public health modeling.

Causal Inference

Instructors:
Lee Kennedy-Shaffer, PhD and Fan Li, PhD

Graduate Student Instructors:
Xi Fang, PhD and Jiaqi Tong

  • Using the SUPPORT observational dataset, this project focuses on estimating causal effects of right heart catheterization on mortality outcomes. Students will apply methods such as propensity score weighting, outcome regression, and doubly robust approaches to compare estimates of treatment effects. They will explore individualized treatment effects using causal machine learning methods like DR-learner, R-learner, and BART. Sensitivity analyses will be performed to assess the impact of unmeasured confounding on causal conclusions. Students will critically evaluate assumptions behind different causal inference methods. The project offers rigorous training in modern causal inference techniques and their application to real-world health data.

  • Causal Inference Research Poster - Group 1 [Download]

    Causal Inference Research Poster - Group 2 [Download]

    Causal Inference Research Poster - Group 3 [Download]

  • Groups 1-3 Presentation: [Download]

Genetics

Instructor:
Hongyu Zhao, PhD

Graduate Student Instructors:
Leqi Xu and Jiaqi Hu

  • This project explores the genetic basis of disease comorbidity through integrative analyses of genome-wide, transcriptome-wide, and proteome-wide association studies. Students will learn to identify shared genetic variants across multiple diseases and quantify their impact on disease pathways. Through hands-on analysis, they will gain skills in genetic epidemiology, bioinformatics, and statistical genetics. Computational tools will be used to interpret complex genetic data and uncover biological mechanisms of disease. Students will work in teams to develop reports and presentations based on their findings. This experience prepares students for future careers in biomedical data science and genetics research.

  • Genetics Research Poster - Group 1 [Download]

    Genetics Research Poster - Group 2 [Download]

    Genetics Research Poster - Group 3 [Download]

  • Groups 1-3 Presentation: [Download]

Public Health Modeling

Instructors:
Stephanie Perniciaro, PhD, MPH and Shelby Golden, MS

Lecture Topics
by Week

  • Introduction to Cluster, R and Tidyverse Shelby Golden, MS

    Probability Sean McGrath, PhD

    Basic Statistics Sean McGrath, PhD

    Sources of Bias in Observational Data Fan Li, PhD

    Git and GitHub Shelby Golden, MS

    Study Design & Estimation Fan Li, PhD

    Linear Regression Yuki Ohnishi, PhD

    Parameter Estimation/Likelihood Melody Owen, PhD Candidate

    AI for Community Health Ruchit Nagar, MD, MPH

    Introduction to Python Justin DeMayo

  • Data Mining I Johan Ugander, PhD

    Python I Shivam Sharma

    Logistic Regression Jingyu Cui, PhD

    Data Mining II Johan Ugander, PhD

    Empirical Bayes Zhou Fan, PhD

    Linear Algebra Melody Owen, PhD Candidate

    Generative AI and Foundation Models Arman Cohan, PhD

    Python II Shivam Sharma

    Prediction for Beginners Sean McGrath, PhD

    Cancer Epidemiology Xiaomei Ma, PhD; Leah Ferrucci, PhD, MPH

    GenAI in Biomedical Research Hua Xu, PhD

    HPC Training Aya Nawano, PhD

    Selection Bias and Representativeness Haidong Lu, PhD

    Visualizing Data in R with ggplot2 Shelby Golden, MS

    Imposter Syndrome Karin Gosselink, PhD

    Data Science in Astronomy Priyamvada Natarajan, PhD

  • Variable Selection Shuangge Steven Ma, PhD

    RSV and Vaccine Daniel Weinberger, PhD

    Introduction to Bayesian Statistics Yiran Wang, PhD

    Prediction Leying Guan, PhD

    Randomized Trials in Economics A. Mushfiq Mobarak, PhD

    Causal Inference Lee Kennedy-Shaffer, PhD

    Bayesian Computation Yiran Wang, PhD

    Machine Learning Leying Guan, PhD

  • Design and Analysis of Clinical Trials Denise Esserman, PhD

    Research at the Intersection of Statistics and Medicine Elizabeth Claus , PhD

    Preparing for Graduate School in Biostatistics Elizabeth Claus, PhD

    AI and Medicine Rohan Khera, MD, MS

    Infectious Disease Modeling Virginia Pitzer, ScD

    Collaboration in the Life of a Statistician Denise Esserman, PhD

    Data Integration Emma Zang, PhD

    Precision Medicine Brian Tom, PhD

    Ethics and Questions of AI Consciousness John Pittard, PhD

  • Genetics and Genomics Smita Krishnaswamy, PhD

    Climate Modeling and Environmental Health Kai Chen, PhD

    AI in Global Health Brian Wahl, PhD, MPH

    Data Privacy Hyunghoon Cho, PhD

    Large Language Models Shivam Sharma

    Poster & Presentation for Biostatistics/Genetics Michael Sweeney

    Data Science in Social & Behavioral Sciences Ijeoma Opara, PhD, LMSW, MPH

    Scientific Communications: Writing & Presentation Elizabeth Bailey

    Working at Meta - FAIR Koustuv Sinha, PhD

    Writing Your CV Kelly Shay, MS

  • Biobank Analysis Bhramar Mukherjee, PhD

    Graduate Studies in EMD Virginia Pitzer, ScD

Lectures Week 1

Lectures Week 2

Lectures Week 3

Lectures Week 4

Lectures Week 5

Lectures Week 6