Laura Chiticariu is the Chief Architect of Watson Natural Language Understanding (NLU), where she builds NLU systems that are accurate, scalable and transparent. She believes in the notion of "Transparent Machine Learning" in NLU: leveraging machine learning techniques, while ensuring that the NLU system remains transparent - easy to comprehend, debug and enhance.
Previously, Laura was a Researcher in the Scalable Natural Language Processing (NLP) group in IBM Research - Almaden, where she built and led the transfer of NLP technologies to multiple IBM products, including IBM BigInsights, IBM Streams and Watson Knowledge Studio, and completed multiple customer engagements with Fortune 100 and multi-national organizations.
Laura has been teaching NLP in universities within and outside the U.S., and developed two online courses in the process.
Laura holds a Ph.D. in Computer Science from University of California, Santa Cruz, and a B.S. in Computer Engineering with a major in Automation and Industrial Informatics from Politehnica University of Bucharest. In her spare time, she enjoys introducing kids to computer programming.
- Declarative Information Extraction, Hands-on Tutorial, KDD 2019
- Transparent Machine Learning for Information Extraction: State-of-the-Art and the Future, EMNLP 2015
- Enterprise Information Extraction: Recent Advances and Open Challenges, SIGMOD 2010
- An algebraic approach to declarative information extraction, Stanford - Logic Seminar, January 2017
- Declarative Multilingual Information Extraction, UC Berkeley - ICSI, November 2016
- An algebraic approach to declarative information extraction, MIT, March 2016
- More than 10+ talks in U.S. universities