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Tao Yang

Speaker Photograph of Tao Yang

Tao Yang received his BSc in Computer Science in 1984 and his MEng in Artificial Intelligence in 1987, both from Zhejiang University, China. He received an MSc and PhD in Computer Science from Rutgers University in 1990 and 1993. He joined the Department of Computer Science at UCSB in 1993. His research has been in the areas of parallel and distributed systems, Internet search, cluster-based services, and high performance scientific computing with over eighty refereed conference and journal papers. He is currently a full professor.

He was the founding Chief Scientist and Vice President of Research and Development in 2000 for Teoma Technologies , a startup company for Internet search which was acquired by Ask.com in 2001. He has led teams of scientists and engineers for the design and implementation of Teoma/Ask Jeeves search engines running on a giant PC/SUN SMP cluster, and has co-invented and architected scalable algorithms and systems support for page indexing, retrieval, ranking, and classification. He was the head of Ask Jeeves search development and engineering (Teoma) from 2003 - 2005. He is currently the Chief Scientist and Senior Vice President, co-leading web search and overseeing technology R&D at Ask Jeeves.

Dr. Yang received the Research Initiation Award from NSF in 1994, UC Regents' Junior Faculty Award in 1994, the Computer Science Faculty Teacher Award in 1995 from the UCSB College of Engineering, and the CAREER Award from NSF in 1997, and Noble Jeeviant Award from AskJeeves, 2002.


Ask.com has been developing a comprehensive suite of search and question-answering technology and differentiated products to help users to find what they are looking for faster. Ask.com's ExpertRank algorithm provides relevant search results by identifying topics and the most authoritative sites on the Web through query-specific clustering and expert analysis. This talk gives an overview of Ask.com's search engine and ExpertRank algorithm, and describes some of infrastructure support for high scalability and availability in searching billions of documents for tens of millions of users everyday.

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