Laura Ham
ML Engineer and technical writer at Weaviate. Author of accessible explainers on neural reranking architectures for vector search practitioners.
Key Contributions
- Bi-encoder vs Cross-encoder: Popularized the “fisherman” analogy for two-stage retrieval — bi-encoder ANN as the wide net for fast candidate recall, cross-encoder as the slow precise sorting step. Made the architectural tradeoffs accessible to practitioners.