BDSY 2026

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 2026 program, topics include causal inference, genetics, and electronic health records.

Causal Inference

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

Graduate Student Instructors:
Zihan Zhu, PhD, Hao Wang, MS, and Changjun Li

  • Using data from three recently completed pragmatic clinical trials at the Yale School of Medicine (the ELAIA 1, ELAIA 2, and KAT trials), which evaluated electronic health record-based alert interventions for kidney care, this project focuses on exploring treatment effect heterogeneity with machine learning and discovering meaningful subgroups. Students will be organized into small working groups, each focusing on one trial. They will apply state-of-the-art causal machine learning methods, such as the Meta Learner, Bayesian Additive Regression Trees, and Bayesian Causal Forest, to explore individualized treatment effects and assess whether certain patient subpopulations benefit or do not benefit from alert-based healthcare interventions. Students will critically evaluate the assumptions behind different subgroup identification approaches and compare findings across methods. They will also collaborate across working groups to understand how trial settings affect appropriate methods and results. This is an open-ended pursuit designed to encourage innovative thinking, teamwork, and hands-on implementation, simulating real-world team science in clinical research. The project offers rigorous training in modern causal inference techniques for heterogeneous treatment effects and their application to clinical trial 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:
Yueqian Jing and Haoran Shao

  • 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]

Electronic Health Records

Instructors:
Cheng-Han Yang, PhD and Henan Xu, PhD

Graduate Student Instructors:
Waveley Qiu

  • This project uses the MIMIC-IV dataset and focuses on prediction using inpatient electronic health record data, particularly repeated biomarker measurements as outcomes. Students will begin with basic regression models and linear mixed-effects models for repeated biomarker measurements, and then explore more flexible approaches such as penalized regression, random forests, XGBoost, and selected sequence models such as RNNs and LSTMs. They will learn how to construct features from longitudinal EHR data and evaluate predictive performance using measures such as prediction error, discrimination, and calibration. The project will also examine how to deal with missing data in lab measurements, including imputation-based approaches, and how these choices affect model performance and interpretation. Students will critically evaluate the strengths and limitations of statistical and machine learning methods for longitudinal EHR data. The project offers rigorous training in modern EHR data analysis and predictive modeling using real-world inpatient data.

  • EHR Research Poster - Group 1 [Download]

    EHR Research Poster - Group 2 [Download]

    EHR Research Poster - Group 3 [Download]

  • Groups 1-3 Presentation: [Download]

Lecture Topics
by Week

  • Intro to programming in R, tidyverse, and leveraging AI-assisted coding 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

    Linear Regression Ruyi Liu, PhD Candidate

    Study Design & Estimation Fan Li, PhD

    Parameter Estimation/Likelihood Hao Wang, PhD Candidate

    Coding with Claude Ted Ellsworth, PhD

    How to conduct a literature review Kate Nyhan, MLS

    Introduction to Python Justin DeMayo

  • Data Mining I Zhenia Rudyk, PhD Candidate

    Python I Shivam Sharma

    Logistic Regression Henan Xu, PhD

    Sports Statistics Brian Macdonald, PhD

    Applying to the UK for graduate studies and personal journey Sam Lambert, PhD

    Refresher on Linear Algebra Henan Xu, PhD

    Fairness/ethics in Genetics and Risk Prediction Sam Lambert, PhD

    Python II Shivam Sharma

    Prediction for Beginners Lillian Rountree, PhD Candidate

    Data Mining II Zhenia Rudyk, PhD Candidate

    Visualization using ggplot2 Shelby Golden, MS

    HPC Training Aya Nawano, PhD and Michael Rothberg, PhD

    Machine Learning I Zhenia Rudyk, PhD Candidate

    Using AI for Visualization Justin DeMayo

    Imposter Syndrome Karin Gosselink, PhD

    AI in Genetics and Genomics Qiao Liu, PhD

  • Machine Learning II Zhenia Rudyk, PhD Candidate

    Causal Inference in Decision Making Harsh Parikh, PhD

    Statistical Challenges in Firearm Injury and Gun Violence Guangyu (Tony) Tong, PhD

    Cancer Epidemiology Leah Ferrucci, PhD, MPH

    Data Equity in Data Science and Artificial Intelligence Bhramar Mukherjee, PhD

    Causal Inference Lee Kennedy-Shaffer, PhD

    Case Analysis of Biomarker PFAS Exposure and Health Outcomes Zeyan Liew, PhD, MPH

    Introduction to DAGS Peter Tennant, PhD

  • Variable Selection Shuangge (Steven) Ma, PhD

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

    Preparing for Graduate School in Biostatistics Elizabeth Claus, PhD, MD

    User-Centered Design of Digital Health Tools Terika McCall, PhD, MPH, MBA

    Design and Analysis of Clinical Trials Denise Esserman, PhD

    Collaboration in the Life of a Statistician Denise Esserman, PhD

    Infectious Disease Modeling Abhishek Pandey, PhD

    Data Privacy Hyunghoon (Hoon) Cho, PhD

  • Ethics and Questions of AI Consciousness John Pittard, PhD

    RSV and Vaccine Dan Weinberger, PhD

    AI for Community Health Ruchit Nagar, MD, MPH

    AI in Global Health Brian Wahl, PhD, MPH

    Differential Privacy Kumar Kshitij Patel, PhD

    Large Language Models Shivam Sharma

    Poster and Presentation for Biostatistics/Genetics Mike Sweeney, PhD Candidate

    Randomized Trials in Economics Mushfiq Mobarak, PhD

    Scientific Communication: Writing and Presentation Jackson Higginbottom, MPH

    Critical Computing Julian Posada, PhD

    Writing Your CV Felicia Spencer

  • Selection Bias and Representativeness Haidong Lu, PhD

    Graduate Studies in CDE and EMD Albert Ko, MD and Andrew DeWan, PhD, MPH

Lectures Week 1

Lectures Week 2

Lectures Week 3

Lectures Week 4

Lectures Week 5

Lectures Week 6