IDEA Lab Publications and Presentations

Members - Faculty, students, and collaborators
Research - Details about our current research projects
Theses and Dissertations - Publications and code releases for student theses and disserations
Publications - Recent technical papers and presentations
Software - Recent software releases
 

2013

McGovern, Amy and Troutman, Nathaniel and Brown, Rodger A. and Williams, John K. and Abernethy, Jennifer. (2013) Enhanced Spatiotemporal Relational Probability Trees and Forests. Data Mining and Knowledge Discovery, Volume 26, Issue 2, Pages 398-433. [online first version, open access]

McGovern, Amy and Gagne II, David J. and Williams, John K. and Brown, Rodger A. and Basara, Jeffrey B. (to appear) Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning. Machine Learning. To appear.

Katona, Branden and Pirtle, Bradley and McGovern, Amy (2013) Using iPads to Teach Artificial Intelligence through Meteorology. Presented at the 22nd Symposium on Education at the annual American Meteorological Society meeting.. [Abstract and recorded presentation]

McGovern, Amy (2013) AMS AI Contest For 2014: Predicting Solar Radiation Using Ensemble Reforecasts. Presented at the 11th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the annual American Meteorological Society meeting. [Abstract and recorded presentation]

Dahl, Brittany and Katona, Branden and Pirtle, Bradley and McGovern, Amy and Brown, Rodger A. and Wicker, Louis J. (2013) Applications of Data Mining to Supercell Tornadogenesis. Presented at the 11th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the annual American Meteorological Society meeting. [Abstract and recorded presentation]

Gagne II, David John and McGovern, Amy and Xue, Ming (2013) Machine Learning Enhancement of Storm Scale Ensemble Probabilistic Precipitation Forecasts. Presented at the 11th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the annual American Meteorological Society meeting. [Abstract and recorded presentation]

2012

Hellman, Scott and McGovern, Amy and Xue, Ming. Learning Ensembles of Continuous Bayesian Networks: An Application to Rainfall Prediction. Proceedings of the Conference on Intelligent Data Understanding (CIDU-2012), electronically published. [pdf (1.4M)]

Gagne II, David John and McGovern, Amy and Xue, Ming. Machine Learning Enhancement of Storm Scale Ensemble Precipitation Forecasts. Proceedings of the Conference on Intelligent Data Understanding (CIDU-2012), electronically published. [pdf (2.3M)]

Pirtle, Bradley and Kimes, Ross and McGovern, Amy and Brown, Rodger A. Using the XSEDE Supercomputing and Visualization Resources to Improve Tornado Prediction Using Data Mining. Presented at the XSEDE 2012 Conference. [extended abstract pdf]

Gagne II, David John and McGovern, Amy and and Basara, Jeffrey and Brown, Rodger A. (2012) Tornadic Supercell Environments Analyzed Using Surface and Reanalysis Data: A Spatiotemporal Relational Data Mining Approach. Journal of Applied Meteorology and Climatology. Vol. 51, No. 12, pages 2203-2217.

Yan, Xiaolei and Sawalha, Lina and McGovern, Amy, and Barnes, Ronald D. (2012) Supporting Transparent Thread Assignment in Heterogeneous Multicore Processors Using Reinforcement Learning. Proceedings of the 3rd Workshop on SoCs, Heterogeneous Architectures and Workloads (SHAW-3). [pdf (304K)]

McGovern, Amy; Kimes, Ross; Pirtle, Bradley; Brown, Rodger A. (2012). Spatiotemporal Data Mining of High Resolution Simulations of Tornadoes. Presented at the Tenth Conference on Artificial Intelligence and its Applications to the Environmental Sciences. [Abstract and recorded presentation]

Gagne II, David John; McGovern, Amy; and Xue, Ming. (2012). Machine Learning Enhancement of Storm Scale Ensemble Precipitation Forecasts. Presented at the Tenth Conference on Artificial Intelligence and its Applications to the Environmental Sciences. [Abstract and recorded presentation]

2011

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. [pdf (924 K), link to our software release]

McGovern, Amy and Gagne II, David John and Troutman, Nathaniel and Brown, Rodger A. and Basara, Jeffrey and Williams, John. (2011) Using Spatiotemporal Relational Random Forests to Improve our Understanding of Severe Weather Processes. Statistical Analysis and Data Mining, special issue on the best of the 2010 NASA Conference on Intelligent Data Understanding. [pdf preprint (1.4M), link to official online version]

McGovern, Amy and Wagstaff, Kiri L. (2011) Machine Learning in Space: Extending our Reach. Editorial introduction to special issue on Machine Learning in Space in Machine Learning Journal. [Link to online first version on Springer's website]

McGovern, Amy; Rosendahl, Derek H; Brown, Rodger A; and Droegemeier, Kelvin K. (2011) Identifying Predictive Multi-Dimensional Time Series Motifs: An application to severe weather prediction. Data Mining and Knowledge Discovery. Volume 22, Issue 1, pages 232-258. [pdf (2.0M). Link to official springer version.]

Ahmed, Zafar and Yost, Patrick and McGovern, Amy and Weaver, Chris (2011). Steerable Clustering for Visual Analysis of Ecosystems. Proceedings of the International Workshop on Visual Analytics. [pdf (4M)]

Tidwell, Zachery and Hellman, Scott and McGovern, Amy. (2011). Expert Move Prediction for Computer Go using Spatial Probability Trees. University of Oklahoma technical report, OU-CS-2011-100. [pdf (624K]

Gagne II, David John; McGovern, Amy; Basara, Jeffrey B.; Brown, Rodger A. (2011). Tornadic supercell analysis from Oklahoma Mesonet and proximity sounding observations: a spatiotemporal relational data mining approach. Presented at the Ninth Conference on Artificial Intelligence and its Applications to the Environmental Sciences. [Abstract and recorded presentation]

Sliwinski, Timothy; Trueblood, Jonathan; Gagne II, David John; McGovern, Amy; Williams, John K.; Abernethy, Jennifer. (2011) Using spatiotemporal relational random forests (SRRFs) to predict convectively induced turbulence. Presented at the Ninth Conference on Artificial Intelligence and its Applications to the Environmental Sciences. [Abstract]

Trueblood, Jonathan; Sliwinski, Timothy; Gagne II, David John; McGovern, Amy; Williams, John K.; Abernethy, Jennifer. (2011) Spatiotemporal relational random forest (SRRF) prediction of convectively-induced turbulence: a severe encounter case study. Presented at the Ninth Conference on Artificial Intelligence and its Applications to the Environmental Sciences. [Abstract and recorded presentation]

2010

McGovern, Amy; Rosendahl, Derek H; Brown, Rodger A; and Droegemeier, Kelvin K. (2010) Identifying Predictive Multi-Dimensional Time Series Motifs: An application to severe weather prediction. Data Mining and Knowledge Discovery. Volume 22, Issue 1, pages 232-258. [pdf (2.0M). Link to official springer version.]

Utz, Christopher. (2010). Learning Ensembles of Bayesian Network Structures Using Random Forest Techniques. Master's Thesis, School of Computer Science, University of Oklahoma. [pdf 1.2M]

Troutman, Nathaniel. (2010). Enhanced Spatiotemporal Relational Probability Trees and Forests. Master's Thesis, School of Computer Science, University of Oklahoma. [pdf 1.9M]

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. Proceedings of the NASA Conference on Intelligent Data Understanding: CIDU 2010. pdf (500K)

Gagne II, David John; Supinie, Timothy A.; McGovern, Amy; Basara, Jeffrey B.; Brown, Rodger A. (2010). Analyzing the effects of low level boundaries on tornadogenesis through spatiotemporal relational data mining. Presented at the Eighth Conference on Artificial Intelligence and its Applications to the Environmental Sciences. Abstract and recorded presentation.

Abernethy, Jennifer; Supinie, Timothy A.; A. McGovern, and Williams, J. K. (2010) Capturing relationships between coherent structures and convectively-induced turbulence using Spatiotemporal Relational Random Forests. Presented at the Eighth Conference on Artificial Intelligence and its Applications to the Environmental Sciences. Abstract and recorded presentation.

2009

Supinie, Timothy and McGovern, Amy and Williams, John and Abernethy, Jennifer. Spatiotemporal Relational Random Forests. Proceedings of the 2009 IEEE International Conference on Data Mining (ICDM) workshop on Spatiotemporal Data Mining. [pdf (272 K)]

Bodenhamer, Matthew; Bleckley, Samuel; Fennelly, Daniel; Fagg, Andrew H. and McGovern, Amy. (2009) Spatio-temporal Multi-Dimensional Relational Framework Trees. Proceedings of the 2009 IEEE International Conference on Data Mining (ICDM) workshop on Spatiotemporal Data Mining. [pdf (305 K)]

Gagne II, David J.; McGovern, Amy; Brotzge, Jerry. (2009). Classification of Convective Areas Using Decision Trees. Journal of Atmospheric and Oceanic Technology. Vol 26, Issue 7, pages 1341-1353. [pdf (1.1 MB)]

Gagne II, David John; McGovern, Amy; Hiers, Nathan C.; Collier, Matthew; Brown, Rodger A. (2009 ). Expanding the Spatial Awareness of Spatiotemporal Relational Probability Trees to Improve the Analysis of Severe Thunderstorm Models. Preprints of the Seventh Conference on Artificial Intelligence and its Applications to the Environmental Sciences.

Spencer, Andy; McGovern, Amy; Elmore, Kimberly; Richman, Michael (2009). Hydrometeor Classi fication using Polarimetric Radar and Spatiotemporal Relational Probability Trees. Preprints of the Seventh Conference on Artificial Intelligence and its Applications to the Environmental Sciences

2008

Derek Rosendahl. (2008). Identifying Precursors to Strong Low-Level Rotation within Numerically Simulated Supercell Thunderstorms: A Data Mining Approach.Master's Thesis, School of Meteorology, University of Oklahoma. [pdf 7.4M]

McGovern, Amy and Jensen, David (2008) Optimistic Pruning for Multiple Instance Learning. Pattern Recognition Letters. Volume 29, Issue 9, pages 1252-1260. [pdf (224K, submitted version. The final version is online here.)]

McGovern, Amy and Hiers, Nathan and Collier, Matthew and Gagne II, David J. and Brown, Rodger A. (2008). Spatiotemporal Relational Probability Trees. Proceedings of the 2008 IEEE International Conference on Data Mining, Pages 935-940. Pisa, Italy. 15-19 December 2008. [pdf (326K)]

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)]

McGovern, Amy and Utz, Christopher M. and Walden, Susan E. and Trytten, Deborah A. and Shehab, Randa L. (2008) Learning the Structure of Retention Data using Bayesian Networks. To appear in the 2008 Frontiers in Education Conference.

Hiers, Nathan; McGovern, Amy; Rosendahl, Derek H.; Brown, Rodger A; Droegemeier, Kelvin K. (2008). Using Spatiotemporal Relational Data Mining to Identify the Key Parameters for Anticipating Rotation Initiation in Simulated Supercell Thunderstorms. Preprints of the Sixth Conference on Artificial Intelligence and its Applications to the Environmental Sciences, joint session with the 24th Conference on International Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology. [pdf 1.1M]

Gagne II, David John; McGovern, Amy and Brotzge, Jerry. (2008) Automated Classification of Convective Areas in Reflectivity using Decision Trees. Preprints of the Sixth Conference on Artificial Intelligence and its Applications to the Environmental Sciences, joint session with the 19th Conference on Probability and Statistics in the Atmospheric Sciences. [pdf 556K]

Gagne II, David John; McGovern, Amy and Brotzge, Jerry. (2008) Using Multiple Machine Learning Techniques to Improve the Classification of a Storm Set. Preprints of the Sixth Conference on Artificial Intelligence and its Applications to the Environmental Sciences. [pdf 40K]

2007

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)]

McGovern, Amy, and Rosendahl, Derek H., and Kruger, Adrianna, and Beaton, Meredith G., and Brown, Rodger A., and Droegemeier, Kelvin K. (2007) Understanding the formation of tornadoes through data mining. Preprints of the Fifth Conference on Artificial Intelligence and its Applications to Environmental Sciences at the American Meteorological Society annual conference. [pdf (1.9M)]

Dabney, William, and McGovern, Amy (2007) Utile Distinctions for Relational Reinforcement Learning. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-07), pages 738-743. [pdf (490K)]

2006

McGovern, Amy, Kruger, Adrianna, Rosendahl, Derek, and Droegemeier, Kelvin. (2006) Open problem: Dynamic Relational Models for Improved Hazardous Weather Prediction. Presented at the ICML Workshop on Open Problems in Statistical Relational Learning. [pdf 204K]

Dabney, William and McGovern, Amy. (2006) The Thing That We Tried That Worked: Utile Distinctions for Relational Reinforcement Learning. Presented at the ICML Workshop on Open Problems in Statistical Relational Learning. [pdf 529K]

2005 and earlier

McGovern, Amy, Friedland, Lisa, Hay, Michael, Gallagher, Brian, Fast, Andrew, Neville, Jennifer, and Jensen, David (2003) Exploiting Relational Structure to Understand Publication Patterns in High-Energy Physics, Knowledge Discovery Laboratory, University of Massachusetts Amherst. (2003). SIGKDD Explorations, December 2003, Volume 5, Issue 2, pages 165-172. Winning entry to the open task for KDD Cup. [pdf (1.6MB)]

McGovern, Amy , and Jensen, David (2003) Identifying Predictive Structures in Relational Data Using Multiple Instance Learning. To appear in the Proceedings of the 20th International Conference on Machine Learning, pages TBD. [postscript (252K) | gzipped postscript (160K) | pdf (112K)]

McGovern, Amy (2002) Autonomous Discovery of Temporal Abstractions from Interaction with an Environment. University of Massachusetts Amherst. [postscript (1984K) | gzipped postscript (774K) | pdf (664K)]

McGovern, Amy , Moss, Eliot, and Andrew G. Barto (2002) Building a Basic Block Instruction Scheduler using Reinforcement Learning and Rollouts, Machine Learning, Special Issue on Reinforcement Learning. Volume 49, Numbers 2/3, Pages 141-160. [postscript (200K) | gzipped postscript (60K) | pdf (160K)]

McGovern, Amy , and Barto, Andrew G. (2001) Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density. Proceedings of the 18th International Conference on Machine Learning, pages 361-368. [postscript (252K) | gzipped postscript (160K) | pdf (112K)]

McGovern, Amy , and Barto, Andrew G. (2001) Accelerating Reinforcement Learning through the Discovery of Useful Subgoals. Proceedings of the 6th International Symposium on Artificial Intelligence, Robotics and Automation in Space: i-SAIRAS 2001, electronically published. [postscript (184K) | gzipped postscript (45K) | pdf (95K)]

McGovern, Amy , and Moss, Eliot (1998) Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts, Proceedings of the 11th Neural Information Processing Systems Conference (NIPS '98), pages 903-909. [postscript (120K) | gzipped postscript (34K) | pdf (80K)]

Hofmann, Martin O., McGovern, Amy , Whitebread, Kenneth R. (1998) Mobile Agents Prevail in the Digital Battlefield. In the Proceedings of the 2nd International Conference on Autonomous Agents (Agents'98), pages 219-225. [postscript (696K) | gzipped postscript (200K) | pdf (80K)]

McGovern, Amy , Sutton, Richard S., Fagg, Andrew H. (1997) Roles of Macro-Actions in Accelerating Reinforcement Learning. 1997 Grace Hopper Celebration of Women in Computing, pages 13-18. [postscript (472K) | gzipped postscript(72K) | pdf(184K)]


Created by amcgovern [at] ou.edu.

Last modified June 19, 2013 1:59 PM