Netflix user activity analysis: cleaned a multi-file dataset with missing values and duplicate entries, engineering custom features for a binary classification model.

bank customer churn predictor: built a neural network with PyTorch to predict churn on an imbalanced dataset, cleaning and feature-engineering with Pandas/NumPy and evaluating with precision-recall curves, Youden's J statistic, and f1-scores (~69%/79% precision across classes).

high school: salutatorian, cum laude society junior inductee, admission ambassador, model un club prez.

awards: united states academic decathlon individual competition two-time winner; john locke global essay finalist (econ category); national latin exam golds & silvers.

internships: climate change research @ both nasa's gsfc and smithsonian science education center NESST summer internships.