Raymond J. Mooney

Professor of Computer Sciences, University of Texas at Austin.

B.S. in Computer Engineering, University of Illinois at Urbana/Champaign, 1983
M.S. in Computer Science, University of Illinois at Urbana/Champaign, 1985
Ph.D. in Computer Science, University of Illinois at Urbana/Champaign, 1988

Research

Teaching

Personal

Contact

Note to Grad-Student Applicants

Research Interests and Publications

As a member of the Artificial Intelligence Laboratory I lead the Machine Learning Research Group where our recent work focuses on:

Research Areas (click on an area for related publications):

Natural Language Learning: Learning for syntactic and semantic parsing, lexicon acquisition, information extraction, language generation, machine translation, and word sense disambiguation.
Statistical Relational Learning : Learning methods that combine the strengths of predicate logic and probability.
Transfer Learning : Using previously acquired knowledge to aid learning on new related problems.
Connecting Language and Perception: Learning language given only its perceptual context as supervision.
Information Extraction: Identifying specific pieces of structured data in web pages or natural-language documents.
Text Categorization and Clustering : Supervised and unsupervised classification of documents and web pages.
Text Data Mining: Discovering knowledge from text using information extraction and rule induction.
Record Linkage & Duplicate Detection : Identifying textually similar but distinct database records that refer to the same entity.
Bioinformatics : Learning to extract knowledge from biomedical literature.
Semi-Supervised Learning : Learning from a mixture of labeled and unlabeled data.
Ensemble Learning : Learning effective committees of hypotheses.
Active Learning : Automated selection of good training examples for supervised or semi-supervised learning.
Learning for Recommender Systems: Content-based and collaborative recommending.
Advice-Taking Learners : Reinforcement learning systems that take advice in natural language.
Autonomic Computing : Using machine learning to make traditional computer systems more adaptive and self-managing.
Inductive Logic Programming: Learning Prolog programs (rules in first-order predicate logic) from examples.
Knowledge-Base and Theory Refinement: Automatically modifying rule bases and Bayesian networks to fit empirical data.

For a complete list of areas and publications, see the UT Machine Learning Research Group home page.

Current Multi-Investigator Research Projects:

NLL: Natural Language Learning
CLAMP: Connecting Language and Perception
TL: Transfer Learning
CogSys: Cognitive Systems (Autonomic Computing)
BioDM: Biological Data Mining

Additional Affiliations:

I am President of the International Machine Learning Society
I am also a member of the UT Computational Linguistics Lab .
I am also a member of the UT Data Mining Group.
I am also affiliated with the UT Center for Computational Biology and Bioinformatics.
I was program co-chair (with Yolanda Gil) of the Twenty-First National Conference on Artificial Intelligence (AAAI-06) in Boston, July 16-20, 2006.
I was general chair of the joint Human Language Technology Conference / Empirical Methods in Natural Language Processing Conference for 2005 (HLT/EMNLP-05).
I was elected a Fellow of the American Association for Artificial Intelligence (AAAI) in 2005 ``For significant contributions to machine learning, particularly explanation-based learning, theory refinement, and learning for natural-language processing.''
I served on the technical advisory board and worked part time for Whizbang! Labs, a former Internet company that specialized in using machine learning to develop systems for extracting and integrating information on the web (such as the FlipDog job search site).

Research Demos:

See neat demos of learning natural-language database interfaces called Geoquery and RestaurantQuery.

Vita:

See my complete vita (in PDF).

Research Talks:

See Powerpoint presentations for some of my recent talks.

Course Information

Personal History

I grew up in the 60's and 70's in the small town of O'Fallon Illinois where starting in 1967 I attended St. Clare grade school and, starting in 1975, O'Fallon Township Highschool. See a scanned version of a paper I wrote for a high-school English class when I was only 17 years old entitled "High-level Artificial Intelligence: An Imminent Possibility with an Enormous Potential for Good". My enthusiasm for AI started early and has not waned; however, my expections about AI's rate of progress and its positive social implications have matured and (hopefully) become more realistic.

In the fall of 1979, I went to the University of Illinois in Champaign-Urbana to obtain all of the degrees listed above. In December 1987, I completed my Ph.D. thesis under the direction of Prof. Gerald DeJong and then began as a faculty member here in the Department of Computer Sciences at the University of Texas at Austin where I am enjoying the start of the third decade of a hopefully long academic career.

See more information on my academic genealogy.

Contact Information

Office:: 1.102 CSA , (512) 471-9558
Email address:: mooney@cs.utexas.edu (Finger)
Fax:: (512) 471-8885
Postal address:: Department of Computer Sciences
The University of Texas at Austin
1 University Station C0500
Austin, Texas 78712-0233
U.S.A.
Home address:: 4707 Eby Lane
Austin, Texas 78731-4507
U.S.A.

Note to Potential Grad-Student and Internship Applicants

Unfortunately, I am unable to personally respond to email requests regarding application to our graduate program or other solicitations for positions in my research lab. I encourage potential graduate-student applicants to see the department information on applying to our graduate program. Graduate-student applicants interested in my research are encouraged to clearly state such interests in their "statement of purpose." I currently have no funding to support summer internships.

mooney@cs.utexas.edu