Jesse Davis

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Jesse Davis

Post-Doc, University of Washington

Office: 356 Allen Center
Telephone: (206) 616 1798
Email: jdavis at cs dot wisc dot edu

Mailing Address
Department of Computer Science and Engineering
University of Washingoton
Box 352350
Seattle, WA 98195-2350

News

I am now an assistant professor at KU Leuven in Belgium. This webpage is no longer being actively maintained. Please visit my new page

Research

I was co-advised by C. David Page Ph.D and Dr. Elizabeth Burnside. I also worked closely with Ines Dutra Ph.D and Vitor Santos Costa Ph.D who spent 2004-2005 in Madison as visiting faculty. They were on leave from the Federal University of Rio de Janeiro. My research focuses on solving problems in biological and clinical domains through the development of novel machine learning and data mining techniques. I have developed a state-of-the-art Statistical Relational Learning framework that is broadly applicable to clinical and biological problems. The framework has been shown to increase the accuracy of learned models and has made novel clinical discoveries. Thus far, I have focused on two application domains: providing a decision support system for radiologists who read mammograms and predicting three-dimensional Quantitative Structure-Activity Relationships (3D-QSAR) for drug design.

Software

Mark Goadrich and I have written a java package for generating ROC and Precision Recall curves. It can also calculate the area under the curve. The jar file is available here. For more information about ROC and PR curves, see our paper at ICML 2006.

Publications

Journals

Elizabeth S. Burnside, Jesse Davis, Jagpreet Chhatwal, Oguzhan Alagoz, Mary J. Lindstrom, Berta M. Geller,Benjamin Littenberg, Katherine A. Shaffer, Charles E. Kahn, Jr, and C. David Page (2009). Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings. In Radiology. Available here.

Refereed Conferences

Jan Van Haaren and Jesse Davis (2012). Markov Network Structure Learning: Randomized Feature Generation Approach. To appear in the Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12). Will be available shortly.

Guy Van Den Broeck and Jesse Davis (2012). Conditioning in First-Order Knowledge Compilation and Lifted Probabilistic Inference. To appear in the Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12). Will be available shortly.

NIma Taghipour, Daan Fierens, Jesse Davis and Hendrik Blockeel (2012). Lifted Variable Elimination with Arbitrary Constraints. To appear in the Proceedings of Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012) . Will be available shortly.

Guy Van Den Broeck, Nima Taghipour, Wannes Meert, Jesse Davis and Luc De Raedt (2011). Lifted Probabilistic Inference by First-Order Knowledge Compilation. Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI). Available as PDF.

Daniel Lowd and Jesse Davis (2010). Learning Markov Network Structure with Decision Trees. Proceedings of IEEE International Conference on Data Mining (ICDM 2010). Available as PDF PDF.

Stefan Schoenmackers, Jesse Davis, Oren Etzioni, and Daniel S. Weld (2010). Learning First-Order Horn Clauses from Web Text. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP 2010). Available as PDF

Jesse Davis and Pedro Domingos (2010). Bottom-Up Learning of Markov Network Structure. In the Proceedings of the 27th International Conference on Machine Learning (ICML). Available as PDF.

Jesse Davis and Pedro Domingos (2009). Deep Transfer via Second-Order Markov Logic. Proceedings of the 26th International Conference on Machine Learning (ICML). Available as PDF.

Jesse Davis, Vitor Santos Costa, Soumya Ray and David Page (2007). An Integrated Approach to Feature Construction and Model Building for Drug Activity Prediction. To appear in the Proceedings of the 24th International Conference on Machine Learning (ICML). Available as PDF.

Jesse Davis, Irene Ong, Jan Struyf, Elizabeth Burnside, David Page and Vitor Santos Costa (2007). Change of Representation for Statistical Relational Learning. In the Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI). Available as PDF.

Jesse Davis and Mark Goadrich (2006). The Relationship Between Precision-Recall and ROC Curves. In the Proceedings of the 23rd International Conference on Machine Learning (ICML). Available as PDF.

Jan Struyf, Jesse Davis and David Page (2006). An Efficient Approximation to Lookahead in Relational Learners. In the Proceedings of the 17th European Conference on Machine Learning (ECML). Available as PDF.

Jesse Davis, Elizabeth Burnside, Ines Dutra, David Page, Raghu Ramakrishnan, Vitor Santos Costa and Jude Shavlik (2005). View Learning for Statistical Relational Learning: With an Application to Mammography. In the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). Available as postscript or PDF or Presentation.

Jesse Davis, Ines Dutra, David Page and Vitor Santos Costa (2005). Establishing Identity Equivalence in Multi-Relational Domains. In the Proceedings of the International Conference on Intelligence Analysis. Available as postscript, PDF or Word. Winner of the Best Technical Paper award.

Elizabeth S. Burnside, Jesse Davis, Vitor Santos Costa, Ines Dutra, Charles E. Kahn Jr., Jason P. Fine and David Page (2005). Knowledge Discovery from Structured Mammography Reports Using Inductive Logic Programming. In the Proceeding of the American Medical Informatics Association Annual Symposium (AMIA) . Will be available shortly.

Jesse Davis, Elizabeth Burnside, Ines Dutra, David Page, Vitor Santos Costa (2005). An Integrated Approach to Learning Bayesian Networks of Rules. In the Proceedings of the European Conference of Machine Learning (ECML). Available as postscript or PDF.

Book Chapters

Jesse Davis, Elizabeth Burnside, Ines Dutra, David Page, Raghu Ramakrishnan, Vitor Santos Costa and Jude Shavlik (2006). Learning a New View of a Database: With an Application to Mammography. To appear in L. Getoor and B. Taskar, editors, An Introduction to Statistical Relational Learning, MIT Press.

Refereed Workshops

Jesse Davis and Pedro Domingos (2008). Deep Transfer via Second-Order Markov Logic. To appear in the Proceedings of the AAAI-2008 Workshop on Transfer Learning for Complex Tasks. Chicago, IL: AAAI Press. Available as PDF

Jesse Davis, Elizabeth Burnside, David Page and Vitor Santos Costa (2006). View Learning Extended: Inventing New Tables for Statistical Relational Learning. In the Proceedings of Open Problems in Statistical Relational Learning Workshop (SRL2006). In conjunction with ICML 2006.

Jesse Davis, Vitor Santos Costa, Irene Ong, David Page and Ines Dutra (2004). Using Bayesian Classifiers to Combine Rules. Appears in the Proceedings of the 3rd Workshop on Multi-Relational Data Mining ( MRDM 2004). In conjunction with KDD 2004. Available as postscript or PDF.

Professional Service

Program Committee for the 21st International Joint Conference on Artificial Intelligence (IJCAI-09)
Program Committee for the 2008 IEEE International Conference on Data Mining (ICDM-08)
Program Committee for the 23rd Conference on Artificial Intelligence (AAAI-08)
Program Committee for the 25th International Conference on Machine Learning (ICML-08)
Program Committee for the 19th Brazilian Symposium on Artificial Intelligence (SBIA-08)
Reviewer for ACM Transactions on Knowledge Discovery from Data (2007,2008)
Reviewer for Journal of Artificial Intelligence Research (2007,2008)
Reviewer for Machine Learning Journal (2007,2008)
Reviewer for Annals of Mathematics and Artificial Intelligence (2008)
Reviewer for 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB-07)
Reviewer for the 20th International Joint Conference on Artificial Intelligence (IJCAI-07)
Reviewer for ACM Transactions on Computational Logic (2006)

Past Teaching Experience

Spring 2003: Discussion Leader for CS302: Introduction to Programming Sections 303 and 304.
Fall 2002: Discussion Leader for for CS302: Introduction to Programming Section 314.

Education

M.S. in Computer Science, University of Wisconsin, Madison, May 2005.

Courses taken:

Spring 2005
- CS784: Data Models and Languages with Raghu Ramakrishnan.
- Genetics 466: General Genetics.
Fall 2004
- CS520: Introduction to Theorectical Computer Science with Jin-Yi Cai.
- CS701: with Susan Horwitz.
Spring 2004
- MATH541: Modern Algebra 1 with Arnold Johnson
- MATH475: Combinatorics with James Propp
Fall 2003
- CS576: Introduction to Bioinformatics with Mark Craven.
- CS764: Topics in Database Systems with Jeffrey Naughton.
Spring 2003
- CS760: Machine Learning with Jude Shavlik.
- CS564: Database Management Systems: Design and Implementation with Jeff Naughton.
Fall 2002
- CS537: Introduction to Operating Systems with Marvin Solomon.
- CS731: Advanced Artificial Intelligence with Biomedical Applications with David Page.
B.A. in Computer Science, from Williams College, June 2002.

Computer Sciences Department
College of Letters and Science
University of Wisconsin - Madison

INFORMATION ~ PEOPLE ~ GRADS ~ UNDERGRADS ~ RESEARCH ~ RESOURCES

5355a Computer Sciences and Statistics ~ 1210 West Dayton Street, Madison, WI 53706
cs@cs.wisc.edu ~ voice: 608-262-1204 ~ fax: 608-262-9777

Jesse Davis

News

Research

Software

Publications

Journals

Refereed Conferences

Book Chapters

Refereed Workshops

Professional Service

Past Teaching Experience

Education

Spring 2005

Fall 2004

Spring 2004

Fall 2003

Spring 2003

Fall 2002