Faculty profile

Jude W. Shavlik

Jude W. Shavlik

Dept. of Computer Sciences
6357 Computer Science and Statistics
(608) 262-7784

Dept. of Biostatistics and Medical Informatics
6740 Medical Sciences Center
(608) 263-7625


Research Keywords

computer science, computational biology, machine learning


  • Department of Computer Sciences, Professor
  • Department of Biostatistics and Medical Informatics, Professor
  • Comprehensive Cancer Center, member
  • Genome Center, member

Current Projects

  • Machine Learning Research Group, researcher

Research Collaborators

  • Mark Craven, Biostats & Medical Informatics
  • David Page, Biostats & Medical Informatics
  • Xiaojin Zhu, Computer Sciences
  • Chuck Dyer, Computer Sciences

Representative Classes

  • Comp Sci 540: Introduction to Artificial Intelligence
  • Comp Sci 760: Machine Learning
  • Comp Sci 838: Learning and Modeling Biological Networks

Research Statement

My primary research interest is machine learning, including its application to biomedical tasks such as microarray ("gene chip") analysis and design, protein-structure determination, and information extraction from on-line biomedical text. Within machine learning I am particularly interested in the use of prior knowledge (i. e., going beyond the tradition of only using labeled examples), algorithms that can accept and refine advice, support-vector machines, inductive-logic programming, and reinforcement learning.

Selected Publications

  • F. DiMaio, J. Shavlik & G. Phillips (2006). A Probabilistic Approach to Protein Backbone Tracing in Electron Density Maps. Bioinformatics, Special Issue Based on the Papers Presented at the Fourteenth International Conference on Intelligent Systems for Molecular Biology (ISMB-06), Fortaleza, Brazil, 22, pp. e81-e89.
  • M. Goadrich, L. Oliphant & J. Shavlik (2006). Gleaner: Creating Ensembles of First-Order Clauses to Improve Recall-Precision Curves. Machine Learning, 64, pp. 231-262.
  • L. Torrey, J. Shavlik, T. Walker & R. Maclin (2006). Skill Acquisition via Transfer Learning and Advice Taking. Proceedings of the Seventeenth European Conference on Machine Learning (ECML'06), pp. 425-436, Berlin, Germany.
  • R. Maclin, J. Shavlik, L. Torrey, T. Walker & E. Wild (2005). Giving Advice about Preferred Actions to Reinforcement Learners Via Knowledge-Based Kernel Regression. Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI'05), pp. 819-824, Pittsburgh, PA.