PEDRO DOMINGOS



Professor Emeritus

Address:
Allen School of Computer Science & Eng.
University of Washington
Box 352350
Seattle, WA 98195-2350

Telephone: (206) 543-4229
Fax: (206) 543-2969
Email: pedrod@cs.washington.edu
Office: 466 Allen Center

Twitter: @pmddomingos

Brief Bio

I'm a professor emeritus of computer science and engineering at the University of Washington and the author of 2040 and The Master Algorithm. I'm a winner of the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI. I'm a Fellow of the AAAS and AAAI, and I've received an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, several best paper awards, and other distinctions. I received an undergraduate degree (1988) and M.S. in Electrical Engineering and Computer Science (1992) from IST, in Lisbon, and an M.S. (1994) and Ph.D. (1997) in Information and Computer Science from the University of California at Irvine. I'm the author or co-author of over 200 technical publications in machine learning, data science, and other areas. I'm a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. I was program co-chair of KDD-2003 and SRL-2009, and I've served on the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others. I've written for the Wall Street Journal, Spectator, Scientific American, Wired, and others. I helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks.

Vita

Research Interests

My main research interests are in the fields of machine learning and data mining. I'd like to make computers do more with less help from us, learn from experience, adapt effortlessly, and discover new knowledge. We need computers that reduce the information overload by extracting the important patterns from masses of data. This poses many deep and fascinating scientific problems: How can a computer decide autonomously which representation is best for target knowledge? How can it tell genuine regularities from chance occurrences? How can pre-existing knowledge be exploited? How can a computer learn with limited computational resources? How can learned results be made understandable by us?

My research addresses these and related questions. Research topics that I'm vworking on, or have recently worked on, include:

Current Projects

Students and Postdocs


Software

Selected Talks

Books

Selected Book Chapters

Selected Essays

Selected Preprints

Selected Journal Papers

Selected Conference Papers


Teaching


Other Interests


Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle

Last modified: September 27, 2024