Clayton T Morrison
- Associate Professor, School of Information
- Associate Professor, Statistics-GIDP
- Associate Professor, Cognitive Science - GIDP
- Member of the Graduate Faculty
Contact
- Richard P. Harvill Building, Rm. 437A
- Tucson, AZ 85721
- claytonm@arizona.edu
Degrees
- M.S. Computer Science
- University of Massachusetts, Amherst, Massachusetts, United States
- Ph.D. Philosophy
- Binghamton University, Binghamton, New York, United States
- Situated Representation
- M.A. Philosophy
- Binghamton University, Binghamton, New York, United States
- B.A. Cognitive Science
- Occidental College, Los Angeles, California, United States
Work Experience
- University of Arizona, Tucson, Arizona (2014 - Ongoing)
- University of Arizona, Tucson, Arizona (2011 - 2014)
- SISTA, University of Arizona (2011 - 2013)
- Computer Science and SISTA, University of Arizona (2008 - 2011)
- USC Information Sciences Institute (2006 - 2008)
- USC Information Sciences Institute (2003 - 2006)
- Computer Science, University of Massachusetts (2001 - 2003)
- Computer Science, University of Massachusetts (1999 - 2001)
Interests
Research
Machine Learning, Causal Inference, Artificial Intelligence, Automated Planning, Knowledge Representation, Cognitive Science
Courses
2024-25 Courses
-
Neural Networks
INFO 557 (Spring 2025) -
Directed Research
INFO 692 (Fall 2024) -
Dissertation
INFO 920 (Fall 2024) -
Neural Networks
INFO 557 (Fall 2024) -
Neural Networks
ISTA 457 (Fall 2024)
2023-24 Courses
-
Dissertation
CSC 920 (Spring 2024) -
Dissertation
INFO 920 (Spring 2024) -
Independent Study
INFO 699 (Spring 2024) -
Dissertation
CSC 920 (Fall 2023) -
Intro to Machine Learning
INFO 521 (Fall 2023) -
Intro to Machine Learning
ISTA 421 (Fall 2023)
2022-23 Courses
-
Dissertation
CSC 920 (Spring 2023) -
Dissertation
INFO 920 (Spring 2023) -
Directed Research
INFO 692 (Fall 2022) -
Dissertation
CSC 920 (Fall 2022) -
Dissertation
INFO 920 (Fall 2022)
2021-22 Courses
-
Directed Research
INFO 692 (Summer I 2022) -
Artificial Intelligence
INFO 550 (Spring 2022) -
Artificial Intelligence
ISTA 450 (Spring 2022) -
Research
CSC 900 (Spring 2022) -
Research
STAT 900 (Spring 2022) -
Thesis
STAT 910 (Spring 2022) -
Directed Research
INFO 692 (Fall 2021) -
Independent Study
CSC 699 (Fall 2021) -
Intro to Machine Learning
INFO 521 (Fall 2021) -
Intro to Machine Learning
ISTA 421 (Fall 2021) -
Thesis
STAT 910 (Fall 2021)
2020-21 Courses
-
Independent Study
STAT 599 (Spring 2021) -
Research
CSC 900 (Spring 2021) -
Thesis
STAT 910 (Spring 2021) -
Directed Research
INFO 492 (Fall 2020) -
Directed Research
INFO 692 (Fall 2020) -
Dissertation
INFO 920 (Fall 2020) -
Independent Study
INFO 699 (Fall 2020) -
Independent Study
STAT 599 (Fall 2020) -
Thesis
STAT 910 (Fall 2020)
2019-20 Courses
-
Artificial Intelligence
INFO 550 (Spring 2020) -
Artificial Intelligence
ISTA 450 (Spring 2020) -
Capstone
INFO 698 (Spring 2020) -
Dissertation
INFO 920 (Spring 2020) -
Dissertation
INFO 920 (Fall 2019) -
Independent Study
CSC 599 (Fall 2019) -
Independent Study
INFO 699 (Fall 2019) -
Independent Study
MATH 599 (Fall 2019) -
Intro to Machine Learning
INFO 521 (Fall 2019) -
Intro to Machine Learning
ISTA 421 (Fall 2019) -
Research
INFO 900 (Fall 2019)
2018-19 Courses
-
Directed Research
INFO 492 (Spring 2019) -
Dissertation
INFO 920 (Spring 2019) -
Independent Study
STAT 599 (Spring 2019) -
Dissertation
INFO 920 (Fall 2018) -
Independent Study
INFO 699 (Fall 2018) -
Intro to Machine Learning
INFO 521 (Fall 2018) -
Intro to Machine Learning
ISTA 421 (Fall 2018)
2017-18 Courses
-
Capstone
INFO 698 (Summer I 2018) -
Directed Research
INFO 492 (Summer I 2018) -
Artificial Intelligence
INFO 550 (Spring 2018) -
Artificial Intelligence
ISTA 450 (Spring 2018) -
Directed Research
INFO 692 (Spring 2018) -
Dissertation
INFO 920 (Spring 2018) -
Independent Study
INFO 499 (Spring 2018) -
Directed Research
INFO 692 (Fall 2017) -
Dissertation
LIS 920 (Fall 2017) -
Intro to Machine Learning
INFO 521 (Fall 2017) -
Intro to Machine Learning
ISTA 421 (Fall 2017)
2016-17 Courses
-
Artificial Intelligence
INFO 550 (Spring 2017) -
Artificial Intelligence
ISTA 450 (Spring 2017) -
Research
STAT 900 (Spring 2017) -
Dissertation
STAT 920 (Fall 2016) -
Honors Independent Study
ISTA 399H (Fall 2016) -
Independent Study
ISTA 599 (Fall 2016) -
Independent Study
LIS 699 (Fall 2016) -
Intro to Machine Learning
INFO 521 (Fall 2016) -
Intro to Machine Learning
ISTA 421 (Fall 2016)
2015-16 Courses
-
Honors Thesis
ISTA 498H (Summer I 2016) -
Independent Study
ISTA 499 (Summer I 2016) -
Bayesian Modeling & Inference
INFO 510 (Spring 2016) -
Bayesian Modeling & Inference
ISTA 410 (Spring 2016) -
Directed Research
ISTA 392 (Spring 2016) -
Honors Thesis
ISTA 498H (Spring 2016) -
Internship
ISTA 493 (Spring 2016) -
Research
LIS 900 (Spring 2016)
Scholarly Contributions
Journals/Publications
- Noriega-Atala, E., Hein, P. D., Thumsi, S. S., Wong, Z., Wang, X., Hendryx, S., & Morrison, C. T. (2019). Inter-sentence Relation Extraction for Associating Biological Context with Events in Biomedical Texts. IEEE/ACM Transactions of Computational Biology and Bioinformatics (TCBB).
- Hamilton, C. W., Byrne, S., Barnard, K. J., Rodriguez, J. J., Morrison, C. T., Palafox, L. F., & Savage, R. (2018). A Bayesian Approach to Sub-Kilometer Crater Shape Analysis using Individual HiRISE Images. Transactions on Geoscience and Remote Sensing.
- Savage, R., Palafox, L. F., Morrison, C. T., Rodriguez, J. J., Barnard, K. J., Byrne, S., & Hamilton, C. W. (2018). A Bayesian Approach to Sub-Kilometer Crater Shape Analysis using Individual HiRISE Images. IEEE Transactions on Geoscience and Remote Sensing, PP(99), 1-11. doi:10.1109/TGRS.2018.2825608
- Valenzuela-Escárcega, M. A., Babur, Ö., Hahn-Powell, G., Bell, D., Hicks, T., Noriega-Atala, E., Wang, X., Surdeanu, M., Demir, E., & Morrison, C. T. (2018). Large-scale Automated Reading with Reach Discovers New Cancer Driving Mechanisms. Database: The Journal of Biological Databases and Curation.
- Camillo Villegas, J., Espeleta, J. E., Morrison, C. T., Breshears, D. D., & Huxman, T. E. (2014). Factoring in canopy cover heterogeneity on evapotranspiration partitioning: Beyond big-leaf surface homogeneity assumptions. Journal of Soil and Water Conservation, 69(3), 78A-83A.
Proceedings Publications
- Alexeeva, M., Sharp, R., Valenzuela, M. A., Kadowaki, J., Pyarelal, A., & Morrison, C. T. (2020, Spring). MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions. In 12th Edition of the Language Resources and Evaluation Conference (LREC).
- Hendryx, S. M., Leach, A. B., Hein, P. D., & Morrison, C. T. (2020, Fall). Meta-Learning Initializations for Image Segmentation. In Workshop on Meta-Learning (MetaLearn 2020; https://meta-learn.github.io/2020/) held in conjunction with the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), 2020.
- Noriega-Atala, E., Liang, Z., Bachman, J., Morrison, C. T., & Surdeanu, M. (2019, Summer). Understanding the Polarities of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods. In Workshop on Extracting Structured Knowledge from Scientific Publications (ESSP).
- Pyarelal, A., Sharp, R., Morrison, C. T., & Barnard, J. J. (2019, May). Interpreting Causal Expressions with Gradable Adjectives to Assembly Dynamics Models. In Modeling the World's Systems.
- Pyarelal, A., Valenzuela-Escárcega, M. A., Sharp, R., Hein, P. D., Stephens, J., Bhandari, P., Lim, H., Debray, S., & Morrison, C. T. (2019, May). AutoMATES: Automated Model Assembly from Text, Equations, and Software. In Modeling the World's Systems.More infoModels of complicated systems can be represented in different ways - inscientific papers, they are represented using natural language text as well asequations. But to be of real use, they must also be implemented as software,thus making code a third form of representing models. We introduce theAutoMATES project, which aims to build semantically-rich unifiedrepresentations of models from scientific code and publications to facilitatethe integration of computational models from different domains and allow formodeling large, complicated systems that span multiple domains and levels ofabstraction.
- Sharp, R., Pyarelal, A., Gyori, B. M., Alcock, K., Laparra, E., Valenzuela-Escárcega, M. A., Nagesh, A., Yadav, V., Bachman, J. A., Tang, Z., Lent, H., Luo, F., Paul, M., Bethard, S. J., Barnard, J. J., Morrison, C. T., & Surdeanu, M. (2019, Summer). Eidos, INDRA & Delphi: From Free Text to Executable Causal Models. In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL).
- Morrison, C. T., Jansen, P. A., Wainwright, E., Marmorstein, S., Marmorstein, S., Wainwright, E., Jansen, P. A., & Morrison, C. T. (2018, May). WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference. In Language Resources and Evaluation Conference (LREC).
- Noriega-Atala, E., Hein, P. D., Thumsi, S. S., Wong, Z., Wang, X., & Morrison, C. T. (2018, September). Inter-sentence Relation Extraction for Associating Biological Context with Events in Biomedical Texts. In The Sixth Workshop on Data Mining in Biomedical Informatics and Healthcare, held in conjunction with the IEEE International Conference on Data Mining (DMBIH@ICDM’18).
- Dawson, C. R., Huang, C., & Morrison, C. T. (2017, August). An infinite hidden Markov model with similarity-based transitions. In The Thirty-Fourth International Conference on Machine Learning (ICML 2017).More infoShort paper at competitive conference, double-blind review with acceptance rate of 25.46% (433 accepted out of 1701 submissions)
- Noriega-Atala, E., Valenzuela-Escárcega, M. A., Morrison, C. T., & Surdeanu, M. (2017, August). Focused Reading: Reinforcement Learning for What Documents to Read. In ICML 2017 Workshop on Interactive Machine Learning and Semantic Information Retrieval (IMLSIR@ICML 2017).
- Noriega-Atala, E., Valenzuela-Escárcega, M. A., Morrison, C. T., & Surdeanu, M. (2017, September). Learning what to read: Focused machine reading. In Conference on Empirical Methods in Natural Language Processing (EMNLP 2017).More infoShort paper at competitive conference, double-blind review with acceptance rate of 18% (107 accepted out of 582 submissions)
- Quick, D., & Morrison, C. T. (2017, October). Composition by Conversation. In The Forty-Third International Computer Music Conference (ICMC 2017).More infoFull paper at competitive conference, double-blind review.
- Valenzuela-Escárcega, M. A., Babur, Ö., Hahn-Powell, G., Bell, D., Hicks, T., Noriega-Atala, E., Wang, X., Surdeanu, M., Demir, E., & Morrison, C. T. (2017, September). Large-scale Automated Reading with Reach Discovers New Cancer Driving Mechanisms. In BioCreative VI Workshop (BioCreative6 2017).
- Brau, E., Dawson, C. R., Carillo, A., Sidi, D., & Morrison, C. T. (2016, February). Bayesian inference of recursive sequences of group activities from tracks. In The Thirteenth AAAI Conference on Artificial Intelligence (AAAI 2016).More infoFull paper at competitive conference, double-blind review with acceptance rate of 26% (549 accepted out of 2132 submissions)
- Gorji-Sefidmazgi, M., & Morrison, C. T. (2016, September). Spatiotemporal analysis of seasonal precipitation over US using co-clustering. In The 6th International Workshop on Climate Informatics (IC 2016).
- Shiaun Peng, K., & Morrison, C. T. (2016, July). Model predictive prior reinforcement learning for a heat pump thermostat. In The 11th International Workshop on Feedback Computing (FC 2016).
- Barnard, K. J., Barnard, K. J., Butler, E. A., Butler, E. A., Morrison, C. T., Morrison, C. T., Simek, K., Simek, K., Brau, E., Brau, E., Guan, J., & Guan, J. (2015, July). Moderated and Drifting Linear Dynamical Systems. In International Conference on Machine Learning.
- Dykhuis, N. J., Rossi, F., & Morrison, C. T. (2015, June). Contributions to Teams Formed in Dynamic Networks. In The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2015).
- Guan, J., Brau, E., Simek, K., Morrison, C. T., Butler, E. A., & Barnard, K. J. (2015, July). Moderated and Drifting Linear Dynamical Systems. In International Conference on Machine Learning (ICML 2015).More infoThis venue is a peer reviewed, competitive conference (acceptance rate: 26%) and the full paper is published as part of the conference proceedings
- Guan, J., Brau, E., Simek, K., Morrison, C. T., Butler, E. A., & Barnard, K. J. (2015, July). Moderated and Drifting Linear Dynamical Systems. In International Conference on Machine Learning.More infoThis venue is a peer reviewed, competitive conference (acceptance rate: 26%) and the full paper is published as part of the conference proceedings [ CSRanking endorsed, A* ]
- Hamilton, C. W., Palafox, L. F., & Morrison, C. T. (2015, June-July). Automated detection of geologic changes on Mars using Bayesian models. In International Union of Geodesy and Geophysics, General Assembly, 26.
Poster Presentations
- Hoogenboom, G., Morrison, C. T., Porter, C., Hein, P. D., & Pyarelal, A. (2020, Spring). Tools to Support Computational Crop Model Analysis and Comparison. The Second International Crop Modeling Symposium (iCROPM).
- Dudding, K. M., Carrington, J. M., & Morrison, C. T. (2019, Summer). Detecting Neonatal Pain Through the Connection of Neonate-Nurse Communication. Career, Connection, Community, Communicating Nursing Research. San Diego, CA.
- Hamilton, C. W., Palafox Novack, L. F., & Morrison, C. T. (2015, June-July). Automated detection of geologic changes on Mars using Bayesian models. The 26th General Assembly of the International Union of Geodesy and Geophysics (IUGG). Prague, the Czech Republic.