I build recommender systems end to end, from raw event data to production ranking.
M.S. Data Science (Machine Learning), Stony Brook University · New York, US
At Nam Viet Media I owned recommendation from the storage layer up: the lakehouse, the features, and the models that rank news and music.
Multi-stage news recommender in production: DSSM two-tower retrieval with a BERT-style item tower, NRMS/LSTUR ranking, LLM summaries, recency-aware re-ranking.
Spotify-style music recommender that tags songs from their waveforms (MuQ, MERT, AST, PANNs, CLAP) and models listening sessions with SASRec and BERT4Rec.
Data platform under both recommenders: GA4 events into S3, Bronze/Silver/Gold on Spark + Iceberg, Airflow 3 and Prefect on Kubernetes, on-prem Linux servers.
B.S. thesis. Removes specific users' data from a trained recommender without full retraining: matches retraining accuracy (~69.3%) with ~5× less compute.