Research / Paper
Deduplication and Probabilistic Record Linkage
Comparative study of Transformer-based, Gradient Boosted, and Fellegi-Sunter models for identity resolution.
Built during a summer research fellowship, this project combines record linkage, evaluation design, and applied identity reasoning.
Result
The composite scoring protocol — combining the three approaches rather than choosing between them — outperformed each individual model as well as common industry methods for the same task.
Why it matters
Record linkage decides whether two records describe the same person. Getting it wrong in either direction has real consequences: duplicates corrupt downstream analysis, and false merges collapse distinct people into one identity.