Dr. Mary Elaine Califf
Associate Professor of Computer Science
School Information Technology
Office
Williams Hall - WIH 17A
Office Phone
Email
- About
- Education
- Research
Current Courses
279.001Algorithms And Data Structures
327.001Concepts Of Programming Languages
327.003Concepts Of Programming Languages
400.003Independent Study
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
Mary Elaine Califf and Nick Dunne. 2022. Feedback in Context: Using a Code Review Tool for Program Grading. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2022). Association for Computing Machinery, New York, NY, USA, 92–97. https://doi.org/10.1145/3478431.3499402
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)