Applied data scientist working on causal inference, machine learning at scale, model fairness, and language model evaluation. Master of Applied Data Science, University of Michigan (2026).
| Project | What it does | Techniques |
|---|---|---|
| Sleep Health Prediction & Phenotyping | Predicts sleep duration and night-to-night consistency for 45,259 Fitbit users in the NIH All of Us cohort, identifies four sleep phenotypes, and audits where model accuracy breaks down by race and age. I managed the project and authored both notebooks, owning extraction through modeling. Live dashboard · slides | BigQuery at 39M-row scale, gradient boosting, KMeans phenotyping, permutation importance, subgroup fairness evaluation, Streamlit |
| Michigan Automatic Voter Registration | Difference-in-differences study of whether Michigan's AVR law raised youth registration and turnout, using CPS microdata. Reports fragile, directionally mixed results rather than overstating them. | Causal inference, two-way fixed effects, event study, clustered SEs |
| Teaching GPT-2 to Answer Questions | Compares three ways of adapting gpt2-medium to SQuAD — prompting, QA fine-tuning, and instruction tuning — evaluated zero-shot and few-shot on token-F1. |
Transformers, fine-tuning, instruction tuning, LLM evaluation |
| Ocean Health Monitoring | Predicts dissolved oxygen across 600k NOAA World Ocean Database records (R2 = 0.98) and clusters global measurements into water-mass regimes. I owned the unsupervised pipeline end to end. | Clustering (KMeans, HDBSCAN, Agglomerative), PCA/t-SNE, random forests, ablation and failure analysis |
| Climate Change and Global Mortality | Integrates WHO mortality, CRU climate, and World Bank population data into a country-year panel covering ~130 countries and a century of records, then tests how temperature relates to mortality across age groups and causes of death. | Multi-source integration, entity harmonization, correlation analysis, interactive visualization |
Languages — Python, SQL
ML & deep learning — PyTorch, scikit-learn, Hugging Face Transformers, gradient boosting, clustering, dimensionality reduction, fairness evaluation
Data & analysis — pandas, NumPy, statsmodels, GeoPandas, statistical modeling, causal inference, experimental design
Tools — Jupyter, Git, BigQuery, Streamlit, Matplotlib, Plotly