Dr. Mary Elaine Califf
Associate Professor
School Information Technology
Office
Old Union - OU 202E
Office Phone
Email
- About
- Education
- Research
Current Courses
279.001Algorithms And Data Structures
279.002Algorithms And Data Structures
385.001Game Development with Unity
279.001Algorithms And Data Structures
279.002Algorithms And Data Structures
168.001Structured Problem Solving Using The Computer
Teaching Interests & Areas
Artificial Intelligence Algorithms and Data Structures Programming Programming Languages Compilers Database Writing
Research Interests & Areas
Natural Language Processing Machine Learning Computer Science Education
Ph D Computer Sciences
University of Texas at Austin
Austin, Texas
MS Computer Science
Baylor University
Waco, TX
MA English
Baylor University
Waco, TX
BA English
Baylor University
Waco, TX
Cited Research
Lavelli, A., Califf, M., Ciravegna, F., Freitag, D., Guiliano, C., Kushmerick, N., & Romano, L. A Critical Survey of the Methodology for IE Evaluation. Proceedings of the 4th International Conference on Language Resources and Evaluation (2004)
Conference Proceeding
Califf, M. Combining Rules and Naïve Bayes for Disease Classification. Second i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data - AMIA (2008)
Califf, M., Goodwin, M., & Brownell, J. Helping him see: Guiding a visually impaired student through the computer science curriculum. Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education (2008)
Califf, M., & Goodwin, M. Effective incorporation of ethics into courses that focus on programming. Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education (2005)
Ireson, N., Ciravegna, F., Califf, M., Freitag, D., Kushmerick, N., & Lavelli, A. Evaluating machine learning for information extraction. Proceedings of the 22nd International Conference on Machine Learning (ICML 2005) (2005)
Lavelli, A., Califf, M., Ciravegna, F., Freitag, D., Guiliano, C., Kushmerick, N., & Romano, L. IE evaluation: Criticisms and recommendations. Proceedings of the AAAI-04 Workshop on Adaptive Text Extraction and Mining (ATEM-2004) (2004)
Journal Article
Lavelli, A., Califf, M., Ciravegna, F., Freitag, D., Guiliano, C., Kushmerick, N., Romano, L., & Ireson, N. Evaluation of Machine Learning-based Information Extraction Algorithms: Criticisms and recommendations. Language Resources and Evaluation 42.4 (2009)
Goodwin, M., & Califf, M. An assessment of the impact of time management training on success in a time-intensive course. Journal on Excellence in College Teaching 17.2 (2007)
Califf, M., & Mooney, R. Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction. Journal of Machine Learning Research 4 (2003): 177-210.
Califf, M., & Thompson, C. Improving Learning by Choosing Examples Intelligently in Two Natural Language Tasks. Learning Language in Logic (J. Cussens and S. Džeroski Eds.) (2000): 279-299.
Califf, M., & Mooney, R. Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming. New Generation Computing 16 (1998): 263-281.
Presentations
Preliminary findings on the significance of learning styles over time. International Society for the Scholarship of Teaching and Learning Inaugural Meeting. (2004)
Grants & Contracts
Collaborative Research: ITWF: Building Communities: Recruiting and Retention of Underrepresented Groups in Computer Science. Illinois State University. (2004)
2019-02-25T14:52:54.206-06:00
2019