As a relevancy engineer and data architect, Elizabeth can engineer a solution to your data goals. With 12 years experience in high-performance systems, she has worked with a spectrum of data transformation needs from high-rate, high-precision time-series sensor data to terabyte-scale text and image retrieval systems for the US Patent and Trademark Office.

At a semantic level, she has worked at person identification and classification systems both in public and private-facing systems. Her work with Duke Health system helped patients better locate doctors by name, location and treatment programs. She has worked with Retail Relay to prototype internal-facing customer identification for retail fraud prevention.

When you ask Google What is learning to rank?, Liz has the first blog post to come up!

Liz’s Master thesis was in early work with image-based recommender systems.