Michael Pazzani
Michael Pazzani researches machine learning and intelligent information systems. He joined Irvine’s faculty in 1988 and chaired its computing department from 1995 to 2000. With Pedro Domingos he analyzed Bayesian classification in 1997, and with Daniel Billsus he studied learning user interests for web recommendations. He co-founded AdaptiveInfo and later led NSF information-systems research. AAAI elected him a Fellow in 2003. His later career includes Rutgers research leadership from 2005 to 2012 and a UCR role from 2012.
- Works
- 271
- Citations
- 27,920
- h-index
- 59
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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1988–2000
A computing research career at Irvine
Pazzani served on Irvine’s faculty from 1988 to 2005 and chaired its department from 1995 to 2000. He earned a UCLA doctorate in computing. His subsequent work connected machine-learning methods with information systems and the organization of academic research.
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1997
Bayesian-classifier analysis
With Pedro Domingos, Pazzani published an analysis of the simple Bayesian classifier under zero-one loss in 1997. The original paper shows that classification can be optimal under some conditions even when attributes are dependent. It distinguishes classification decisions from accurate probability estimation.
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1997–2002
Learning user interests and adaptive information
With Daniel Billsus, Pazzani published Learning and Revising User Profiles in 1997. The original paper describes using user ratings to learn interests and help identify relevant websites. He was AdaptiveInfo’s co-founder and CEO from 2000 to 2002. The research and company work connect learning with personalized information.
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2002–2012
Research leadership and recognition
Pazzani directed NSF’s Information and Intelligent Systems Division from 2002 to 2006. AAAI elected him a Fellow in 2003. Pazzani led research at Rutgers from 2005 to 2012 and moved to a UCR research-leadership role in 2012. These historical roles connect technical research with institutional and public research programs.
Sources and credits
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