Overview

Gender

male

Jason Chaw received his Ph.D. in Computer Science from the University of Texas at Austin. His research interests include question answering, knowledge representation and reasoning, natural language processing, and machine learning. Dr. Chaw was a key contributor to Vulcan's Project Halo and DARPA's machine reading projects. His dissertation studied methods at addressing the problem faced by users in using unfamiliar knowledge bases to answer questions. During this time, he built the ASKME research prototype, which is an integral component to support question-answering in Project Halo. External evaluations have shown the work to make significant progress towards helping users use unfamiliar knowledge bases to answer questions.

After graduation, Dr. Chaw joined IBM Research to work on extending the capabilities of the Watson question-answering system. He contributed to a computational resource of common sense knowledge to help Watson answer questions. In particular, Dr. Chaw investigated methods that learn to extract knowledge from text, a key requirement for the Watson system. A while back, the Watson system was showcased on national television by competing as a contestant on the Jeopardy! game-show.

Along with three other systems, Project Halo and Watson were both selected by AI Magazine(Fall 2010) as representing the state of the art for automatic question-answering systems.

Dr. Chaw is currently a member of the Core Relevance and Ranking team at Microsoft Bing. He has significant practical experience with search relevance and has contributed techniques that use machine learning and online metrics to improve customer experience and the bottom line.