📊 I’m a Senior at Pomona College, majoring in Mathematics & Statistics with a minor in Data Science.

🔍 I’m interested in Bayesian methods, probabilistic modeling, and applied statistics, with projects spanning healthcare, biomechanics, and baseball analytics.

• Senior Thesis (2025–26): Bayesian Joint Modeling of Pain and Depression

  • Developing a hierarchical Bayesian framework to jointly model chronic pain and depression outcomes in transgender healthcare.
  • Comparing empirical Bayes vs. full Bayes approaches, with EB+ correction.
  • Running simulation studies on posterior contraction for small subgroups and building decision-focused MCID calibration curves.

• Data Science Capstone (2025–26): Cloud-Based Baseball Analytics Infrastructure

  • Designing a cloud-based architecture to ingest and persist tracking API data using PostgreSQL and Python ETL pipelines.
  • Building automated statistical reporting workflows (Python, SQL, CI) to deliver reproducible summaries of pitch-level data.
  • Prototyping a Streamlit app for interactive data visualization and model exploration.

• Quantitative Analyst Associate, Philadelphia Phillies (Summer 2025)

  • Directed independent research on the Automated Ball-Strike Challenge System (AAA level).
  • Built SQL + Python pipelines (BigQuery, nested CTEs, window functions) to compute per-pitch challenge run values and estimate opportunity cost ($L$).

• Biomechanical Drivers of Pitch Velocity (2024)

  • Analyzed Driveline OpenBiomechanics and TrackMan datasets to identify biomechanical predictors of velocity.
  • Built nonlinear models (XGBoost) with tuned CV, achieving ~2–3 mph prediction error.
  • Applied feature importance analysis to study energy transfer through the pitching kinetic chain.

• Pomona-Pitzer Baseball Analytics (2023–present)

  • Co-Director of Analytics & Data Engineering.
  • Developed Stuff+/Pitching+/Location+ models, opponent scouting pipelines, and automated workflows to support NCAA DIII decision-making.

Programming: Python, R, SQL, Bash
Statistical & ML Methods: Bayesian inference, probabilistic modeling, calibration, cross-validation, feature engineering
Libraries: XGBoost, scikit-learn, tidymodels, tidyverse, pandas, NumPy, Matplotlib, SHAP
Databases & Workflow: Google BigQuery, PostgreSQL, Git/GitHub, LaTeX