Teresa Mondría Terol

Teresa Mondría Terol leads AI products and platforms at NPR, where she was the first hire on the AI Labs team, leading how NPR uses generative AI to enrich its content metadata and power better search and personalization. Previously, she was an engineer on The New York Times’ A.I. Initiatives team, helping build Cheatsheet, the newsroom’s investigative LLM tool and recent winner of a WAN-IFRA Digital Media Award for Best AI-Driven News Product. She was part of the engineering team of Times’ Epstein Files coverage, and she worked with reporters and bureau chiefs across desks worldwide. Before The Times, she worked on product operations and model behavior at The Browser Company, specializing in AI quality and LLM evaluations for Dia Browser. She has held roles in data science and A.I. at Chartbeat and the United Nations, and performed machine learning research with the Brown Institute for Media Innovation and Columbia Climate School. She graduated from Columbia University with a master’s in quantitative methods, supported by the “la Caixa” Fellowship, from Miguel Hernández University with a master’s in journalism innovation, and from University Carlos III with a dual bachelor’s in journalism and media studies. She has spoken at national and international conferences. As a Shorenstein fellow, she will be researching artificial intelligence implementation in journalism and investigative reporting.