Selected technical work in statistical computing, machine learning, and data engineering.
Causal inference framework for geo-based incrementality experiments: study design, sample size determination, power analysis, and counterfactual estimation across geographic markets. Includes a Python RAG assistant for non-technical users.
Reusable Python ML pipeline for cross-validated model training, evaluation, and ensembling using XGBoost, LightGBM, CatBoost, and regularized logistic regression. Applied to real-world datasets in the NESS Statathon, earning top placements in 2023, 2024, and 2025.
Headless automation system on Linux using Python, OpenCV, OCR, and CNN-based screen parsing. Graph-based state controller for real-time perception and decision-making. Reduced manual supervision from hours per day to ~15 minutes; sustained >99% uptime over 6+ months.
Time-to-event analysis of clinical outcomes using Kaplan–Meier estimation and Cox proportional hazards modeling. Assessed associations between treatment exposure and survival while accounting for censored observations. Produced reproducible statistical reports with hazard ratios and confidence intervals.