PEDRO DOMINGOS



Professor



Address:

Paul G. Allen School of Computer Science & Engineering

University of Washington

Box 352350

Seattle, WA 98195-2350



Telephone: (206) 543-4229

Fax: (206) 543-2969

Email: pedrod at cs dot washington dot edu

Office: 648 Allen Center



Twitter: @pmddomingos



For speaking engagements, please contact:

Tom Neilssen at BrightSight Speakers

Tel.: 609-924-3060 x16

Email: tom@brightsightspeakers.com Read the prologue.

Check out my online machine learning class.

Research Interests

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

Learning concepts represented by sets of rules

Using examples as implicit definitions of concepts

Using probabilistic representations and analyses to address the uncertainty inherent in learning

Automating the process of selecting representations for concepts

Learning several models and combining them to improve accuracy and stability

Evaluating and selecting candidate models to avoid "overfitting" (i.e., to distinguish between genuine regularities and chance occurrences)

Learning models that can be easily understood by people

Using pre-existing knowledge to guide and improve learning

Developing knowledge discovery algorithms that run in linear or near-linear time, and so scale up to large databases

Using subsampling techniques to scale up pre-existing approaches

Developing algorithms that take into account the costs of decisions

Understanding the probabilistic properties and foundations of data mining algorithms

Developing techniques for mining semi-structured data sources (e.g., text, the Web)

Current Projects

Brief Bio

Current Students

Alumni

Software

Alchemy: Statistical relational AI.

SPN: Sum-product networks for tractable deep learning.

RDIS: Recursive decomposition for nonconvex optimization.

BVD: Bias-variance decomposition for zero-one loss.

NBE: Bayesian learner with very fast inference.

RISE: Unified rule- and instance-based learner.

VFML: Toolkit for mining massive data sources.

Selected Talks

Books

Selected Book Chapters

Selected Essays

Selected Journal Papers

Selected Conference Papers

Teaching

Other Interests

Literature, cinema, music, travel. Sports: swimming, long-distance running.

Last modified: July 9, 2019