IDEA Lab Software 

Software releases are listed below by the type of software. Most of our lab paper publications and theses & dissertations release code when they appear.


  • Hagelslag is an object-based severe storm hazard forecasting system, developed and released open source by David Gagne as part of his PhD thesis.
  • Gagne II, D. J., A. McGovern, N. Snook, R. Sobash, J. Labriola, J. K. Williams, S. E. Haupt, and M. Xue, 2016:
    Hagelslag: Scalable object-based severe weather analysis and forecasting. Proceedings of the Sixth Symposium on
    Advances in Modeling and Analysis Using Python, New Orleans, LA, Amer. Meteor. Soc., 447.
  • Link to Hagelslag on github

Spatiotemporal Relational Random Forests and Spatiotemporal Relational Probability Trees

  • McGovern, Amy and Gagne II, David J. and Williams, John K. and Brown, Rodger A. and Basara, Jeffrey B. (2014) Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning. Machine Learning. Volume 95, Issue 1, Pages 27-50. Code, paper, software, and data.
  • Nathaniel Troutman. (2010). Enhanced Spatiotemporal Relational Probability Trees and Forests. Master’s Thesis, School of Computer Science, University of Oklahoma. Code, data, and the thesis.
  • McGovern, Amy; Supinie, Timothy; Gagne II, David John; Troutman, Nathaniel; Collier, Matthew; Brown, Rodger A.; Basara, Jeffrey; Williams, John. (2010) Understanding Severe Weather Processes through Spatiotemporal Relational Random Forests. To appear in the NASA Conference on Intelligent Data Understanding: CIDU 2010. Code and data for the paper.


  • McGovern, Amy and Trytten, Deborah. (2013). Making In-Class Competitions Desirable For Marginalized Groups. Proceedings of the 2013 Frontiers in Education Conference, pages 704-706. [pdf (261K)] Code and documentation.
  • McGovern, Amy and Tidwell, Zachery and Rushing, Derek (2011). Teaching Introductory Artificial Intelligence through Java-based Games. Proceedings of the symposium on Educational Advances in Artificial Intelligence. Code, paper, and model assignments.
  • McGovern, Amy, and Fager, Jason. (2007) Creating Significant Learning Experiences in Introductory Artificial Intelligence. Proceedings of SIGCSE 2007, technical symposium on computer science education, pages 39-43. [pdf (223K)]

Tornado motif mining

Multi-Modal Utility Trees

  • Dabney, William and McGovern, Amy (2010). Multi-Modal Utile Distinctions. University of Massachusetts Amherst Technical Report UM-CS-2010-065.Code and tech report

Ensembles of Bayesian Probability Networks

  • Christopher Utz. (2010). Learning Ensembles of Bayesian Network Structures Using Random Forest Techniques. Master’s Thesis, School of Computer Science, University of Oklahoma. Code, data, and the thesis.

Spatial kernels for drought

  • Collier, Matthew and McGovern, Amy. (2008). Kernels for the Investigation of Localized Spatiotemporal Transitions of Drought with Support Vector Machines. Proceedings of ICDM 2008, the 8th IEEE International Conference on Data Mining Workshops. Pisa, Italy. 15-19 December 2008, pages 359-368. [pdf (400K)Data page corresponding to the paper