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Pedro Domingos

Pedro Domingos
Nationality
Portuguese
Residence
Seattle, Washington, USA
Occupation
Professor, Researcher, Writer, Computer Scientist
Known for
Author of "The Master Algorithm” development of Markov logic networks contributions to machine learning and AI
Accolades
ACM SIGKDD Innovation Award (2014) Fellow of the Association for the Advancement of Artificial Intelligence (2010) Sloan Fellowship (2003) Fulbright Scholarship (1992-1997) NSF CAREER Award IBM Faculty Award IJCAI John McCarthy Award Several best paper awards
Education
University of California, Irvine (MS, PhD) Instituto Superior Técnico - University of Lisbon (MS, Licentiate)
Social Media
Summary

Pedro Domingos (born 1965) is a Professor Emeritus of computer science and engineering at the University of Washington, known for his pioneering work in machine learning and the development of Markov logic networks. He graduated in Electrical Engineering and Computer Science from the Technical Superior Institute of Lisbon in 1988 and earned his Ph.D. in Information and Computer Science from the University of California, Irvine, in 1997. Domingos authored the influential book "The Master Algorithm" (2015), which was recommended by Bill Gates as an essential reading on AI and machine learning. 

Domingos has published over 200 technical papers and has received numerous prestigious awards, including the SIGKDD Innovation Award, AAAI Fellowship, Sloan Fellowship, NSF CAREER Award, Fulbright Scholarship, and IBM Faculty Award. He co-founded the International Machine Learning Society and has served on the editorial board of the Machine Learning Journal. He has also been involved in various program committees for leading AI and machine learning conferences.

His research covers a wide range of topics, including scaling learning algorithms to big data, statistical relational learning, and deep learning. Domingos has been a member of the Portuguese Diaspora Council since 2016, contributing significantly to the global AI and machine learning community.


 

Biography

Pedro Domingos was born in 1965 in Lisbon, Portugal. He completed his undergraduate degree in Electrical Engineering and Computer Science at the Instituto Superior Técnico (IST) in Lisbon in 1988. He continued his education at IST, earning a Master of Science in 1992. Domingos then moved to the United States to pursue further studies at the University of California, Irvine, where he obtained another Master of Science in 1994, followed by a Ph.D. in Information and Computer Science in 1997. His doctoral thesis was titled "A Unified Approach to Concept Learning," under the guidance of advisor Dennis F. Kibler.

Domingos began his academic career as an assistant professor at IST, where he spent two years before joining the University of Washington in Seattle in 1999. He quickly rose through the ranks, becoming a full professor in 2012. As of 2018, he held the title of Professor Emeritus of computer science and engineering.

In 2018, Domingos started a machine learning research group at the hedge fund D. E. Shaw & Co., though he left the position in 2019.

Pedro Domingos is a prominent figure in the fields of machine learning, artificial intelligence (AI), and data science. His research interests are diverse, encompassing areas such as scaling learning algorithms to big data, maximising word of mouth in social networks, unifying logic and probability, and deep learning. He has contributed significantly to the development of Markov logic networks, which enable uncertain inference.

Domingos' work focuses on enabling computers to learn from experience, adapt, and discover new knowledge with minimal human intervention. He addresses problems such as autonomous representation selection, distinguishing genuine regularities from chance, exploiting pre-existing knowledge, learning with limited computational resources, and making learned results understandable.

  • Some of his notable research topics include:
  • Learning concepts represented by sets of rules
  • Using probabilistic representations and analyses
  • Automating representation selection
  • Combining models to improve accuracy and stability
  • Avoiding overfitting
  • Making models understandable
  • Scaling knowledge discovery algorithms
  • Developing cost-aware algorithms

Domingos is the author or co-author of over 200 technical publications in machine learning, data science, and related areas. His influential book, "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World" (2015), has been highly praised and recommended by Microsoft founder Bill Gates as essential reading on AI and machine learning. Another notable publication is his article "Our Digital Doubles: AI will serve our species, not control it," featured in Scientific American in 2018.

Throughout his career, Domingos has received numerous awards and honours, including the 2014 ACM SIGKDD Innovation Award for his foundational research in data stream analysis, cost-sensitive classification, adversarial learning, and Markov logic networks. He was elected a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 2010 for his significant contributions to machine learning and the unification of first-order logic and probability. He has also been awarded the 2003 Sloan Fellowship, the NSF CAREER Award, the IBM Faculty Award, and the IJCAI John McCarthy Award. Additionally, Domingos received a Fulbright Scholarship from 1992 to 1997 and has won several best paper awards.

Domingos is a co-founder of the International Machine Learning Society and has been a member of the editorial board of the Machine Learning journal. He has also served as an associate editor of the Journal of Artificial Intelligence Research (JAIR). He has been program co-chair of significant conferences such as KDD-2003 and SRL-2009 and has participated in the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others.

Domingos has written for various high-profile publications, including the Wall Street Journal, Spectator, Scientific American, and Wired. He has also been a member of the Portuguese Diaspora Council since 2016.

Current Projects: Domingos' current projects include:

  • Statistical Relational Learning: Learning from noisy data in rich representations
  • Tractable Deep Learning: Learning deep models where inference is tractable
  • Machine Reading: Extracting knowledge bases from text
  • Collective Knowledge Bases: Merging knowledge from multiple sources
  • Large-Scale Machine Learning: Mining massive data streams

Pedro Domingos has made substantial contributions to the fields of machine learning and artificial intelligence. His work has not only advanced theoretical understanding but also paved the way for practical applications that have significantly impacted various industries. Through his research, teaching, and writing, Domingos continues to influence the next generation of scientists and engineers, leaving a lasting legacy in the world of AI and machine learning.


 

Vision

Pedro Domingos envisions a future where computers can autonomously learn, adapt, and discover new knowledge with minimal human intervention. He aims to develop intelligent systems that can extract important patterns from vast amounts of data, reducing information overload and enhancing human decision-making. His work strives to create AI that not only advances technology but also serves humanity by addressing complex problems and improving everyday life. Domingos is committed to unifying logic and probability, scaling learning algorithms to handle big data, and making AI systems both powerful and understandable, ensuring they can be trusted and effectively utilised across various domains.

Recognition and Awards
Pedro Domingos has garnered numerous accolades throughout his distinguished career in machine learning and artificial intelligence. He received the ACM SIGKDD Innovation Award in 2014, recognising his significant contributions to the field. Domingos is also a Fellow of the Association for the Advancement of Artificial Intelligence since 2010, highlighting his leadership and impact in advancing AI research. His early career achievements include being awarded the Sloan Fellowship in 2003 and a Fulbright Scholarship spanning from 1992 to 1997, facilitating his groundbreaking research endeavours. Domingos has also been honoured with the NSF CAREER Award and an IBM Faculty Award, further underscoring his influential role in academia and industry. Moreover, he received the prestigious IJCAI John McCarthy Award and several best paper awards at prominent conferences, reflecting his exceptional contributions to the field's theoretical and applied advancements. In addition to these accolades, Domingos has earned recognition for his groundbreaking research contributions through numerous distinguished paper awards. He co-authored "Recursive Decomposition for Nonconvex Optimization," which received the Distinguished Paper Award at the 2015 International Joint Conference on Artificial Intelligence in Buenos Aires. His collaboration with Robert Gens on "Discriminative Learning of Sum-Product Networks" garnered the Outstanding Student Paper Award at NIPS 25 in 2012, demonstrating his innovative approach to advancing machine learning paradigms. Furthermore, Domingos and Hoifung Poon were honoured with the Best Paper Award for "Sum-Product Networks: A New Deep Architecture" at the 2011 Conference on Uncertainty in Artificial Intelligence in Barcelona. His early contributions with Parag Singla on "Object Identification with Attribute-Mediated Dependences" were recognised with the Best Paper Award at ECML PKDD 2005 in Porto, Portugal. Additionally, his foundational work with Geoff Hulten on "Mining High-Speed Data Streams" received the 2015 SIGKDD Test of Time Award, underscoring its enduring impact on the field. Domingos' consistent recognition for fundamental research, including Best Paper Awards at KDD 1999 and 1998, further solidifies his legacy as a pioneering figure in the field of machine learning and AI.
Pedro Domingos
Nationality
Portuguese
Residence
Seattle, Washington, USA
Occupation
Professor, Researcher, Writer, Computer Scientist
Known for
Author of "The Master Algorithm” development of Markov logic networks contributions to machine learning and AI
Accolades
ACM SIGKDD Innovation Award (2014) Fellow of the Association for the Advancement of Artificial Intelligence (2010) Sloan Fellowship (2003) Fulbright Scholarship (1992-1997) NSF CAREER Award IBM Faculty Award IJCAI John McCarthy Award Several best paper awards
Education
University of California, Irvine (MS, PhD) Instituto Superior Técnico - University of Lisbon (MS, Licentiate)
Social Media