* Architected offline and online ML pipelines to optimize ad targeting criteria, enhancing ad placement relevancy and driving a ~$950M annual revenue lift for the Google Display Network (GDN)

* Leveraged Gemini generative models to derive higher-quality targeting criteria for both ad requests and ad groups

Website: https://globalhealth.stanford.edu/

PI: Eran Bendavid

* Led end-to-end data engineering and technical analysis for two major global health studies, culminating in co-authored publications in Nature Communications Medicine and the American Journal of Public Health

* Trained and evaluated ML models (e.g., random forests, logistic regression) on large-scale, multi-national Demographic and Health Survey (DHS) datasets to identify predictors of HIV infection across sub-Saharan Africa

* Architected a human-in-the-loop data ingestion pipeline using Amazon Mechanical Turk to clean, standardize, and fuse highly heterogeneous survey datasets from 45 countries for downstream analysis

Website: https://web.stanford.edu/class/cs142/

Instructor: Mendel Rosenblum

* TAed this course for 5 quarters (Win 2016, Spr 2016, Win 2017, Spr 2017, Spr 2018)

* Modernized the course curriculum by migrating instructional materials and core projects from Ruby on Rails to the MEAN stack

Website: https://web.stanford.edu/class/archive/cs/cs332/cs332.1182/#!index.md

Instructor: Emma Brunskill

Mentored students through coursework, exam prep and final projects, providing technical guidance via office hours, email, and online forums

Website: https://cs145.stanford.edu/

Instructor: Christopher Ré

Authored homework and exam questions and led discussion sections