Publications and Products: Amy McGovern

Ph.D. Thesis

Refereed Publications (includes Journals, Conferences, Book Chapters, and Workshops)

  1. McGovern, Amy; Elmore, Kim; Gagne II, David John; Haupt, Sue Ellen; Karstens, Chris; Lagerquist, Ryan; Smith, Travis and J. K. Williams. Using Artificial Intelligence to Improve Real-time Decision Making for High-Impact weather. (To Appear) Bulletin of the American Meteorological Society.
  2. McGovern, Amy; Potvin, Corey and Brown, Rodger A. (to appear) Using Large-scale Machine Learning to Improve our Understanding of the Formation of Tornadoes. Invited chapter in Large-Scale Machine Learning in the Earth Sciences.
  3. Foss, Greg; McGovern, Amy; Potvin, Corey; Abram, Greg; Bowen, Anne; Hulkoti, Neena; Kaul, Arnav; Suey, Nick. (2016). Data Mining Tornadogenesis Precursors. Scientific Visualization and Data Analytics Showcase, The International Conference for High Performance Computing, Networking, Storage and Analysis (SC 16).
  4. Foss, Greg; McGovern, Amy; Potvin, Corey; Abram, G.; Bowen, A.; Hulkoti, N. and Kaul, A. (2016) Showcase: Data Mining Tornadogenesis Precursors. Eurographics Symposium on Parallel Graphics and Visualization. [pdf, video]
  5. Clark, Adam; MacKenzie, Andrew; McGovern, Amy; Lakshmanan, Valliappa and Brown, Rodger A. (2015) An Automated, Multi-parameter Dryline Identification Algorithm. Weather and Forecasting, Volume 30, Issue 6, pages 1781-1794. Link to full text on the WaF page.
  6. Morris, Robert; Bonet, Blai; Cavazza, Marc; desJardins, Marie; Felner, Ariel; Hawes, Nick; Knox, Brad; Konidaris, George; Lang, Jerome; Linares Lopez, Carlos; Magazzeni, Daniele; McGovern, Amy; Natarajan, Sriraam; Sturtevant, Nathan R.; Thielscher, Michael; Yeoh, William; Sardina, Sebastian and Wagstaff, Kiri (2015) A Summary of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AI Magazine, Volume 36, Issue 3.
  7. Lakshmanan, Valliappa; Gilleland, Eric; McGovern, Amy and Tingley, Martin (Editors.) (2015) Machine Learning and Data Mining Approaches to Climate Science. Proceedings of the 4th International Workshop on Climate Informatics. Springer. Link to book on Springer and Amazon.
  8. McGovern, Amy; Gagne II, David John; Basara, Jeffrey; Hamill, Thomas M. and Margolin, David. (2015) Solar Energy Prediction: An International Contest to Initiate Interdisciplinary Research on Compelling Meteorological Problems. Bulletin of the American Meteorological Society, Volume 96, pages 1388-1395. Link to the pdf on BAMS (local pdf here)
  9. McGovern, Amy; Balfour, Andrea; Beene, Marissa and Harrison, David. (2015) Storm Evader: Using an iPad To Teach Kids about Meteorology and Technology. Bulletin of the American Meteorological Society, Volume 96, Issue 3, pages 397-404. Final pdf
  10. Gagne II, David John; McGovern, Amy; Brotzge, Jerald; Coniglio, Michael; Correia, James and Xue, Ming. (2015) Day-Ahead Hourly Hail Prediction Integrating Machine Learning with Storm-Scale Numerical Weather Models. Proceedings of the 2015 Innovative Applications of Artificial Intelligence conference, pages 3954-3960. pdf (10 MB)
  11. Gagne II, David John; McGovern, Amy and Xue, Ming. (2014) Machine Learning Enhancement of Storm-Scale Ensemble Probabilistic Quantitative Precipitation Forecasts. Weather and Forecasting, 29, 1024–1043. doi: http://dx.doi.org/10.1175/WAF-D-13-00108.1 [pdf (4 MB)]
  12. McGovern, Amy; Gagne II, David J.; Williams, John K.; 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. [online first version, open access]
  13. McGovern, Amy; Rosendahl, Derek H. and Brown, Rodger A. (2014) Toward Understanding Tornado Formation Through Spatiotemporal Data Mining. Book chapter in Data Mining for Geoinformatics: Methods and Applications, edited by Cervone, Guid and Lin, Jessica and Waters, Nigel. 29 DOI 10.1007/978-1-4614-7669-6 2, Springer Science Business Media New York 2014. [link to a pre-print of the pdf. The officially formatted pdf is linked above.]
  14. McGovern, Amy; Troutman, Nathaniel; Brown, Rodger A.; 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]
  15. Gagne II, David; McGovern, Amy; Brotzge, Jerald and Xue, Ming. (2013) Severe Hail Prediction within a Spatiotemporal Relational Data Mining Framework. Presented at the 8th International Workshop on Spatial and Spatio-Temporal Data Mining (SSTDM), electronically published. [pdf (512 K)]
  16. 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)]
  17. Gagne II, David John; McGovern, Amy; 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. [link to AMS pdf]
  18. Hellman, Scott; McGovern, Amy and Xue, Ming. (2012) 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)]
  19. Gagne II, David John; McGovern, Amy and Xue, Ming. (2012) Machine Learning Enhancement of Storm Scale Ensemble Precipitation Forecasts. Proceedings of the Conference on Intelligent Data Understanding (CIDU-2012), electronically published. [pdf (2.3M)]
  20. Pirtle, Bradley; Kimes, Ross; McGovern, Amy and Brown, Rodger A. (2012) Using the XSEDE Supercomputing and Visualization Resources to Improve Tornado Prediction Using Data Mining. Presented at the XSEDE 2012 Conference. [extended abstract pdf]
  21. Yan, Xiaolei; Sawalha, Lina; 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)]
  22. McGovern, Amy; Gagne II, David John; Troutman, Nathaniel; Brown, Rodger A.; 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. Vol 4, Issue 4, pages 407-429. [pdf preprint (1.4M), link to official online version]
  23. 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. [preprint, Link to online first version on Springer's website]
  24. McGovern, Amy; Rosendahl, Derek; Brown, Rodger A. and Droegemeier, 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.]
  25. McGovern, Amy; 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 (923K)]
  26. Ahmed, Zafar; Yost, Patrick; McGovern, Amy and Weaver, Chris. (2011). Steerable Clustering for Visual Analysis of Ecosystems. Proceedings of the International Workshop on Visual Analytics. [pdf (4M)]
  27. McGovern, Amy; Supinie, Timothy; Gagne II, David John; Troutman, Nathaniel; Collier, Matthew; Brown, Rodger A.; Basara, Jeffrey and 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)
  28. Gagne II, David J.; McGovern, Amy and Brotzge, Jerald. (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)]
  29. Supinie, Timothy; McGovern, Amy; Williams, John and Abernethy, Jennifer. (2009) Spatiotemporal Relational Random Forests. Proceedings of the 2009 IEEE International Conference on Data Mining (ICDM) workshop on Spatiotemporal Data Mining, electronically published. [pdf (272 K)]
  30. 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, electronically published. [pdf (305 K)]
  31. 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.)]
  32. McGovern, Amy; Hiers, Nathan; Collier, Matthew; 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)]
  33. 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 of the [pdf (400K)]
  34. McGovern, Amy; Utz, Christopher M.; Walden, Susan E. and Trytten, Deborah A. (2008) Learning the Structure of Retention Data using Bayesian Networks. Proceedings of the 2008 Frontiers in Education Conference.
  35. 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)]
  36. 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)]
  37. McGovern, Amy , and Jensen, David (2003) Identifying Predictive Structures in Relational Data Using Multiple Instance Learning. Proceedings of the 20th International Conference on Machine Learning, pages 528-535. [postscript (252K) | gzipped postscript (160K) | pdf (112K)]
  38. 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)]
  39. McGovern, Amy; Moss, J. Eliot B. and Barto, Andrew G. (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. Official link to the pdf on Springer. [postscript (200K) | gzipped postscript (60K) | pdf (160K)]
  40. 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)]
  41. 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)]
  42. McGovern, Amy and Moss, J. Eliot B. (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)]
  43. Hofmann, Martin O.; McGovern, Amy and 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)]
  44. McGovern, Amy; Sutton, Richard S. and 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)]

Products

Unrefereed Publications and Presentations

  1. Karstens, C; LaDue, D.; Correia Jr., J.; Calhoun, K. M.; Smith, T.; Ling, C.; Meyer, T. C.; McGovern, A.; Lagerquist, R. A.; Kingfield, D. M.; Smith, B. T.; Leitman, E. M.; Cintineo, J. L.; Wolfe, J. P.; Gerard, A.; Rothfusz, L. P.. (2017) Prototyping a Next-Generation Severe Weather Warning System for FACETs. Presented at the Seventh Conference on Transition of Research to Operations at the 2017 American Meteorological Society meeting. [Abstract]
  2. Lagerquist, R. A.; McGovern, A.; Smith, T. (2017) Using Machine Learning to Predict Straight-line Convective Wind Hazards Throughout the Continental United States. Presented at the 15th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the 2017 American Meteorological Society meeting. [Abstract]
  3. Smith, T. M.; Ortega, K. L.; Calhoun, K. M.; Karstens, C.; Kingfield, D. M.; Lagerquist, R. A.; Ma- halik, M. C.; McGovern, A.; Meyer, T. C.; Obermeier, H.; Reinhart, A. E.; Smith, B. R. (2017) Initial Results from MYRORSS: A Multi-Radar/Multi-Sensor Climatology of the United States. Presented at the Special Symposium on Severe Local Storms: Observation needs to advance research, prediction and communication at the 2017 American Meteorological Society meeting. [Abstract]
  4. Gagne II, D. J.; Haupt, S. E.; McGovern, A.; Williams, J. K.; Linden, S. (2017) The Performance Impacts of Machine Learning Design Choices for Gridded Solar Irradiance Forecasting. Presented at the Eighth Conference on Weather, Climate, Water and the New Energy Economy at the 2017 American Meteorological Society meeting. [Abstract]
  5. Gagne II, D.J.; McGovern, A.; Sobash, R.A.; Haupt, S.E.; Williams, J.K. (2017) Evaluation of Real- Time Machine Learning Hail Forecasts from the NCAR Convection-Allowing Ensemble. Presented at the 28th Conference on Weather Analysis and Forecasting / 24th Conference on Numerical Weather Prediction at the 2017 American Meteorological Society meeting. [Abstract]
  6. Harrison, D.; McGovern, A.; Karstens, C.; Lagerquist, R. A. (2017) Best Track: Object-Based Path Identification and Analysis. Presented at the Seventh Symposium on Advances in Modeling and Analysis Using Python at the 2017 American Meteorological Society meeting. [Abstract]
  7. Nardi, J. M.; Gagne II, D. J.; McGovern, A.; Snook, N. (2017) Verification of Automated Hail Fore- casts from the 2016 Hazardous Weather Testbed Spring Experiment. Presented at the 28th Conference on Weather Analysis and Forecasting / 24th Conference on Numerical Weather Prediction at the 2017 American Meteorological Society meeting. [Abstract]
  8. Harrison, D.; Karstens, C.; McGovern, A. (2017) Verification and Analysis of Probabilistic Hazards Information Guidance. Presented at the Fifth Symposium on Building a Weather-Ready Nation: Enhancing Our Nations Readiness, Responsiveness, and Resilience to High Impact Weather Events at the 2017 American Meteorological Society meeting. [Abstract]
  9. Lagerquist, Ryan; McGovern, Amy; Smith, Travis; Richman, Michael. (2016) Machine Learning for Real-time Prediction of Damaging Straight-line Winds. Presented at the 2016 Severe Local Storms Conference. [Abstract] [poster]
  10. Lagerquist, Ryan; McGovern, Amy; Smith, Travis; Richman, Michael and Lakshmanan, Valliappa. (2016) Importance-Ranking of Climate Variables for Prediction of Damaging Straight-Line Winds.
  11. Gagne II, David John; McGovern, Amy; Snook, Nathan; Sobash, Ryan. A.; Labriola, Jonathan D.; Williams, John K.; Haupt, Sue. E. and Xue, Ming. (2016). Hagelslag: Scalable Object-Based Severe Weather Analysis and Forecasting. Presented at the Sixth Symposium on Advances in Modeling and Analysis Using Python. [Abstract and poster] [URL to open-source software release] [local pdf of poster]
  12. Balfour, Andrea; Davis, Taner and McGovern, Amy. (2016) Storm Lab: Teaching Kids that Air Masses Drive the Weather Using Serious Games. Presented at the 25th Symposium on Education at the Annual American Meteorological Society meeting. [Abstract and recorded presentation]
  13. Gagne II, David John; McGovern, Amy; Snook, Nathan; Sobash, Ryan A. and Xue, Ming. (2016) Severe Hail Forecasting Evaluation: Machine Learning and Severe Weather Proxy Variables. Presented at the 14th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the annual American Meteorological Society meeting. [Abstract and recorded presentation]
  14. Lagerquist, Ryan A.; McGovern, Amy; Lakshmanan, Valliappa and Smith, Travis M. (2016) Real-time Prediction of Damaging Straight-line Winds Produced by Thunderstorms. Presented at the 14th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the annual American Meteorological Society meeting. [Abstract and recorded presentation]
  15. McGovern, Amy; Karstens, Christopher. D.; Smith, Travis. M. and Calhoun, Kristin. M. (2016) Using Machine Learning for Nowcasting Severe Weather Hazards. Presented at the 14th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the annual American Meteorological Society meeting. [Abstract and recorded presentation]
  16. McGovern, Amy; Potvin, Corey K. and Brown, Rodger A. (2016) Combining Large-Scale Machine Learning Techniques with HPC to Better Understand Tornadoes. Presented at the 14th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences and the Second Symposium on High Performance Computing for Weather, Water, and Climate at the annual American Meteorological Society meeting. [Abstract and recorded presentation]
  17. McGovern, Amy and Potvin, Corey K and Brown, Rodger A. (2015) Using Machine Learning Techniques to Investigate Tornadogenesis. Presented at the National Weather Association's 40th Annual Meeting. [Abstract] [Poster]
  18. Gagne, David John; McGovern, Amy; Brotzge, Jerald; Coniglio, Michael C.; Correia Jr., James and Xue, Ming. (2015) Hail Size Prediction with Machine Learning Applied to Storm-Scale Ensembles: Spring 2014 Evaluation and Physical Understanding. Presented at the 13th Conference on Artificial Intelligence at the annual American Meteorological Society meeting. [Abstract and recorded presentation]
  19. Gagne, David John; Haupt, Sue E.; Linden, Seth; Williams, John K.; McGovern, Amy.; Wiener, G.; Lee, J. A. and McCandless, T. C. (2015) Scaling Machine Learning Models to Produce High Resolution Gridded Solar Power Forecasts. Presented at the 13th Conference on Artificial Intelligence at the annual American Meteorological Society meeting. [Abstract and recorded presentation]
  20. Harrison, David; Roux, Zachary. A.; McGovern, Amy and Blumberg, W. G. (2015) Promoting a Weather Ready Nation Through Serious Games. Presented at the 24th Symposium on Education at the annual American Meteorological Society meeting. [Abstract and recorded presentation]
  21. Katona, Branden T.; McGovern, Amy; Lakshmanan, V. and Clark, Adam J. (2014) Automated Identification of Cold Pools in a Convection-permitting model. Presented at the 12th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the annual American Meteorological Society meeting. [Abstract and recorded presentation]
  22. MacKenzie, Andrew J.; McGovern, Amy; Lakshmanan, V.; Clark, Adam J. and Brown, Rodger A. (2014) A 3-Dimensional Watershed Transform Technique for Storm Extraction on Gridded Data. Presented at the 12th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the annual American Meteorological Society meeting. [Abstract and recorded presentation]
  23. MacKenzie, Andrew J.; Lakshmanan, V.; McGovern, Amy; Brown, Rodger A. and Clark, Adam J. (2014) An Automated, Multi-parameter Dry line Detection Algorithm. Presented at the 12th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the annual American Meteorological Society meeting. [Abstract and recorded presentation]
  24. Dahl, Brittany; Potvin, Corey K.; Wicker, Louis J.; Brown, Rodger A. and McGovern, Amy. (2014) Dependence of Vortex Characteristics on Grid Resolution in Simulated Supercells. Presented at the 12th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the annual American Meteorological Society meeting. [Abstract and recorded presentation]
  25. Harrison, David; Balfour, Andrea; Beene, Marissa and McGovern, Amy. (2014) Teaching meteorology and technology through an iPad application. Presented at the 23nd Symposium on Education at the annual American Meteorological Society meeting. [Abstract and recorded presentation]
  26. Katona, Branden; 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]
  27. 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]
  28. Dahl, Brittany; Katona, Branden; Pirtle, Bradley; McGovern, Amy; 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]
  29. Gagne II, David John; 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]
  30. McGovern, Amy; Kimes, Ross; Pirtle, Bradley and 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]
  31. 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]
  32. Gagne II, David John; McGovern, Amy; Basara, Jeffrey B. and 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]
  33. Sliwinski, Timothy; Trueblood, Jonathan; Gagne II, David John; McGovern, Amy; Williams, John K. and 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, slides from the talk]
  34. Trueblood, Jonathan; Sliwinski, Timothy; Gagne II, David John; McGovern, Amy; Williams, John K. and 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, slides from the talk]
  35. Gagne II, David John; Supinie, Timothy A.; McGovern, Amy; Basara, Jeffrey B. and 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.
  36. Abernethy, Jennifer; Supinie, Timothy A.; McGovern, Amy 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.
  37. Gagne II, David John; McGovern, Amy; Hiers, Nathan C.; Collier, Matthew; Brown and 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.
  38. Spencer, Andy; McGovern, Amy; Elmore, Kimberly and Richman, Michael. (2009). Hydrometeor Classification using Polarimetric Radar and Spatiotemporal Relational Probability Trees. Preprints of the Seventh Conference on Artificial Intelligence and its Applications to the Environmental Sciences
  39. Hiers, Nathan; McGovern, Amy; Rosendahl, Derek H.; Brown, Rodger A and 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]
  40. Gagne II, David John; McGovern, Amy and Brotzge, Jerald. (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]
  41. Gagne II, David John; McGovern, Amy and Brotzge, Jerald. (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]
  42. McGovern, Amy; Rosendahl, Derek H.; Kruger, Adrianna; Beaton, Meredith G.; Brown, Rodger A. and Droegemeier, Kelvin K. (2007) Anticipating 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)]
  43. 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]
  44. 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]
  45. 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. Winning entry to the open task for KDD Cup. Presented at KDD 2003. [kdl_kddcup2003.pdf (1.1MB)]
  46. Blau, Hannah and McGovern, Amy. (2003) Categorizing Unsupervised Relational Learning Algorithms. For the Workshop on Learning Statistical Models from Relational Data at International Joint Conference on Artificial Intelligence
  47. McGovern, Amy and Barto, Andrew G. (2002) Autonomous Discovery of Temporal Abstractions from Interaction with an Environment.Poster presentation at the Symposium on Abstraction, Refomulation, and Approximation (SARA 2002) [pdf (568K)] Abstract appears in Lecture Notes in Computer Science, Volume 2371/2002, pages 338-339. [pdf (78K)]
  48. McGovern, Amy. (2001) Scheduling Java Byte Code in the Java Virtual Machine Using Reinforcement Learning, Presented at the 2001 Workshop on Reinforcement Learning.
  49. McGovern, Amy and Barto, Andrew G. (2001) Linear Discriminant Diverse Density for Automatic Discovery of Subgoals in Reinforcement Learning. Poster presentation at the Workshop on Hierarchy and Memory in Reinforcement Learning at the 18th International Conference on Machine Learning.
  50. McGovern, Amy. (2000) Birds of a Feather Session: Women Students in Computer Science Presented at the 2000 Grace Hopper Celebration of Women in Computing
  51. McGovern, Amy; Moss, J. Eliot B. and Barto, Andrew G. (1999) Basic-block Instruction Scheduling Using Reinforcement Learning and Rollouts. Proceedings of the 1999 IJCAI workshop on learning and optimization. [postscript (154K)| gzipped postscript (49K) | pdf (120K)]
  52. McGovern, Amy. (1998) acQuire-macros: An Algorithm for Automatically Learning Macro-actions, In the Neural Information Processing Systems Conference (NIPS '98) workshop on Abstraction and Hierarchy in Reinforcement Learning [postscript (1368K) | gzipped postscript (160K) | pdf (272K)]
  53. McGovern, Amy; Precup, Doina; Ravindran, B.; Singh, Satinder and Sutton, Richard S. (1998) Hierarchical Optimal Control of MDPs, Proceedings of the 10th Yale Workshop on Adaptive and Learning systems. [postscript (2824K) | gzipped postscript (600K) | pdf (494K)]
  54. McGovern, Amy and Sutton, Richard S. (1997) Towards a better Q(lambda). Presented at the Fall 1997 Reinforcement Learning Workshop.
  55. McGovern, Amy and Sleator, Daniel. (1996) Computer Game Playing in the Domain of Spades. Senior Thesis presentation, Carnegie Mellon University.

Technical reports

Chaired workshops and conferences


amy@cs.umass.edu
Last modified: March 22, 2017 1:57 PM