# Pedro Domingos

Pedro Domingos (born 1965) is a Professor Emeritus of computer science and engineering at the University of Washington, known for his research in machine learning, including Markov logic networks, and for authoring 'The Master Algorithm' (2015).

Pedro Domingos (born 1965) is a Professor Emeritus of computer science and engineering at the University of Washington. He is a researcher in [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) known for his work on Markov logic networks, which enable uncertain inference by combining first-order logic with probability. Domingos has also contributed to data stream analysis, cost-sensitive classification, and adversarial learning, and he is the author of the popular science book *The Master Algorithm* (2015).

## Education

Domingos received an undergraduate degree and a Master of Science degree from Instituto Superior Técnico (IST) in Portugal. He then moved to the University of California, Irvine, where he earned a second Master of Science degree followed by a PhD. His doctoral research focused on machine learning and data mining, laying the groundwork for his later contributions to the field.

## Research and career

After completing his PhD, Domingos spent two years as an assistant professor at IST before joining the University of Washington as an assistant professor of Computer Science and Engineering in 1999. He became a full professor in 2012 and later transitioned to Professor Emeritus. In 2018, he started a machine learning research group at the hedge fund D. E. Shaw & Co., but left the following year to return to academic pursuits.

Domingos co-founded the International Machine Learning Society, a professional organization dedicated to advancing the field. As of 2018, he served on the editorial board of the *Machine Learning* journal. His research has bridged theoretical foundations and practical applications, including work in viral marketing and information integration.

### Publications

Domingos is best known for his 2015 book *The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World*, published by Basic Books (ISBN 978-0-465-06570-7). In the book, he argues that all machine learning paradigms can be unified into a single algorithm, drawing on insights from [neural-network](https://www.wikiprompt.org/wiki/neural-network)s, evolutionary computation, Bayesian inference, and other approaches.

He also wrote "Our Digital Doubles: AI will serve our species, not control it," published in *Scientific American* in September 2018. In that essay, he described [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) systems as "like autistic savants" that lack common sense and can take instructions too literally, but he maintained that they will remain tools serving human needs. In 2024, he published a satirical novel, *2040: A Silicon Valley Satire* (BookBaby, ISBN 979-8-350-96334-2).

### Awards and honors

Domingos received the 2014 ACM SIGKDD Innovation Award for his foundational research in data stream analysis, cost-sensitive classification, adversarial learning, and Markov logic networks, as well as applications in viral marketing and information integration. In 2010, he was elected a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) for significant contributions to machine learning and to the unification of first-order logic and probability. He also received a Sloan Fellowship in 2003 and a Fulbright Scholarship from 1992 to 1997.

## Legacy and influence

Domingos's work on Markov logic networks has influenced subsequent research in statistical relational learning, a subfield that combines [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) with logical reasoning. His advocacy for a unified learning algorithm has sparked debate among researchers about the future direction of the field. As a professor, he mentored numerous students who have gone on to careers in academia and industry, contributing to the broader growth of [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) and related areas.

His popular writings have helped communicate complex ideas in [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) to a general audience, emphasizing both the potential and the limitations of current systems. While his predictions about the timeline for a master algorithm remain speculative, his contributions to foundational methods continue to be cited in academic literature.

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Source: https://www.wikiprompt.org/wiki/pedro-domingos
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Last updated: 2026-09-12T22:25:05.604808+00:00