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2021

• Burke Amanda; Satterfield, Elizabeth; McGovern, Amy (2021) Approximating Observation Error Statistics using Two Machine Learning Models. Joint Center for Satellite Data Assimilation (JCSDA) Quarterly. Number 69, Spring 2021, pages 12-17.  [link to pdf[ 
• Zhi Li, Mengye Chen, Shang Gao, Xiangyu Luo, Jonathan J. Gourley, Pierre Kirstetter, Tiantian Yang, Randall Kolar, Amy McGovern, Yixin Wen, Bo Rao, Teshome Yami, Yang Hong. (2021) CREST-iMAP v1.0: A fully coupled hydrologic-hydraulic modeling framework dedicated to flood inundation mapping and prediction, Environmental Modelling and Software, Volume 141, 105051, https://doi.org/10.1016/j.envsoft.2021.105051  
• Flora, M. L., Potvin, C. K., Skinner, P. S., Handler, S., & McGovern, A. (2021). Using Machine Learning to Generate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System, Monthly Weather Review, Early online release at https://doi.org/10.1175/MWR-D-20-0194.1
• McGovern, Amy (2021). NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (Invited Presentation). Presented at the 20th Conference on Artificial Intelligence for Environmental Science at the 2021 American Meteorological Society Annual Meeting. [abstract and presentation]
• Earnest, B.; McGovern, A.; Jirak, I. (2021). Predicting Wildfires Using Machine Learning. Presented at the 20th Conference on Artificial Intelligence for Environmental Science at the 2021 American Meteorological Society Annual Meeting. [abstract and presentation]
• Harrison, D.; McGovern, A.; Karstens, C.; Jirak, I.; Marsh, P. (2021) A Climatology of HREF Forecasts in Severe Convective Environments. Presented at the 20th Conference on Artificial Intelligence for Environmental Science at the 2021 American Meteorological Society Annual Meeting. [abstract and presentation]
• Lagerquist, R.; McGovern, A.; Gagne II, D.J.; Homeyer, C. (2021) Using Significance Tests and Physical Constraints to Interpret a Neural Network for Tornado Prediction. Presented at the 20th Conference on Artificial Intelligence for Environmental Science at the 2021 American Meteorological Society Annual Meeting. [abstract and presentation]
• Woodward, A.; McGovern, A.; Allen, J. (2021) Frontal Identification using a Machine Learning Model. Presented at the 20th Conference on Artificial Intelligence for Environmental Science at the 2021 American Meteorological Society Annual Meeting. [abstract and presentation]
• Eckstein, G.; McGovern, A.; Reinhart, A.; Calhoun, K. (2021) Using Convolutional Neural Networks to Predict Convective Initiation from Radar and Satellite Data. Presented at the 20th Conference on Artificial Intelligence for Environmental Science at the 2021 American Meteorological Society Annual Meeting. [abstract and presentation]
• Burke, A.; Satterfield, E.; McGovern, A. (2021) Approximating Observation Error Statistics Using Machine Learning Models. Presented at the Ninth AMS Symposium on the Joint Center for Satellite Data Assimilation (JCSDA) at the 2021 American Meteorological Society Annual Meeting. [abstract and presentation]
• Burke, A.; Snook. N.; McGovern, A. (2021) Improving Machine Learning–Based Probabilistic Hail Forecasts through Statistical Weighting. Presented at the 20th Conference on Artificial Intelligence for Environmental Science at the 2021 American Meteorological Society Annual Meeting. [abstract and presentation]
• McGovern, Amy (2021). Transitioning AI Research from Academic to Operations. Presented at the 11th Conference on Transition of Research to Operations at the 2021 American Meteorological Society Annual Meeting. [abstract and presentation]
• McGovern, Amy (2021) Machine Learning Applications in High-Impact Weather Research. Invited presentation to Northern Illinois University seminar series for the Department of Geographic and Atmospheric Sciences.

2020

• Burke, Amanda; McGovern, Amy; Gagne II, David John; Snook, Nathan (2020) Temporally Weighting Machine Learning Models for High-Impact Severe Hail Prediction. AI for Earth Sciences Workshop at NeurIPS 2020. https://ai4earthscience.github.io/neurips-2020-workshop/ papers/ai4earth_neurips_2020_35.pdf  
• S. A. Boukabara, V. M. Krasnopolsky, S. G. Penny, J. Q. Stewart, A. Mc- Govern, D. Hall, J. E. Ten Hoeve, J. Hickey, H.-L. A. Huang, J. Williams, K. Ide, P. Tissot, S. E. Haupt, K. S. Casey, N. Oza, A. Geer, E. S. Maddy, and R. N. Hoffman. (2020) Outlook for exploiting artificial intelligence in the earth and environmental sciences. Bulletin of the American Meteorological Society. Volume 102, Number 5, E1016-E1032. https://doi.org/10.1175/BAMS-D-20-0031.1.
• McGovern, Amy (2021) Machine Learning Applications in High-Impact Weather Research. Invited presentation to Northern Illinois University seminar series for the Department of Geographic and Atmospheric Sciences.
• McGovern, Amy (2020) Building trustworthy AI for environmental science. Invited presentation to Oregon State Computer Science seminar series.
• McGovern, Amy (2020) Building trustworthy AI for environmental science. Invited presentation to data science working group for CLIVAR, the climate and ocean variability, predictability, and change.
• McGovern, Amy (2020) NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography. Keynote talk to the 2020 ASR/ARM Topical Workshop on Machine Learning and Statistical Methods for Observations, Modeling, and Observational Constraints on Modeling. 
• McGovern, Amy (2020) Building trustworthy AI for environmental science. OU School of Meteorology Convective Seminar. 
• McGovern, Amy (2020) Building trustworthy AI for environmental science. Invited talk for the NITRD Big Data and AI annual meeting. 
• McGovern, Amy. (2020) Machine Learning for High-Impact Weather. Invited talk for Science Discussion Group at the Storm Prediction Center. 
• McGovern, Amy. (2020) Building trustworthy AI for environmental science. Invited talk for the AIML@OU seminar. 
• McGovern, Amy. (2020) Building trustworthy AI for environmental science. Invited talk for the Georgia Tech Institute for Data Engineering and Science (Ideas) Machine Learning seminar. 
• McGovern, Amy. (2020) Trustworthy AI for High Impact Weather Prediction. Presented at the 2nd Workshop on Leveraging AI in Environmental Science. Recordings and slides are available here. 
• McGovern, Amy. (2020) Building trustworthy AI for environmental science. Invited talk for the Machine Learning seminar series. Recorded slides are talk are here. 
• Lagerquist, Ryan; McGovern, Amy; Gagne, David; Homeyer, Cameron. (2020). Significance-tested and physically constrained interpretation of a deep-learning model for tornadoes. Presented at the ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction. 
• McGovern, Amy; Bostrom, Ann; Ebert-Uphoff, Imme; He, Ruoying; Thorncroft, Chris; Tissot, Philippe; Boukabara, Sid; Demuth, Julie; Gagne II, David John; Hickey, Jason; Williams, John K. (2020) Weathering Environmental Change Through Advances in AI. EOS, Volume 101, https://doi.org/10.1029/2020EO147065. Published on 28 July 2020.
• Lagerquist, Ryan; McGovern, Amy; Homeyer, Cameron R; Gagne II, David John; Smith, Travis. (2020) Deep Learning on Three-dimensional Multiscale Data for Next-hour Tornado Prediction. Monthly Weather Review. Volume 48, Number 7, pages 2837–2861. [https://doi.org/10.1175/MWR-D-19-0372.1]
• Lagerquist, Ryan; Allen, John T; McGovern, Amy. (2020) Climatology and Variability of Warm and Cold Fronts over North America from 1979-2019. Journal of Climate. Volume 33, Number 15, pages 6531–6554. [https://doi.org/10.1175/JCLI-D-19-0680.1]
• McGovern, A., R. Lagerquist, and D. Gagne (2020) Using machine learning and model interpretation and visualization techniques to gain physical insights in atmospheric science. Proceedings of the International Conference on Learning Representations, [electronically published].
• Handler, Shawn; Reeves, Heather; McGovern, Amy; (2020) Development of a Probabilistic Subfreezing Road Temperature Nowcast and Forecast Using Machine Learning. Weather and Forecasting. [https://doi.org/10.1175/WAF-D-19-0159.1]
• Jergensen, G. E, McGovern, A., Lagerquist, R., and Smith, Travis (2020). Classifying convective storms using machine learning, Weather and Forecasting. Volume 35, Number 2, Pages 537-559. https://doi.org/10.1175/WAF-D-19-0170.1 
• Burke, A., N. Snook, D.J. Gagne II, S. McCorkle, and A. McGovern (2020) Calibration of Machine Learning–Based Probabilistic Hail Predictions for Operational Forecasting. Weather and Forecasting, 35, 149-168, https://doi.org/10.1175/WAF-D-19-0105.
• McGovern, Amy (2020). How Python Can Help Us to Create the Physical Data Scientists of the Future (Core Science Keynote). Presented at the Joint Session How Artificial Intelligence at Scale Will Link Weather and Climate Data to Society at the 10th Symposium on Advances in Modeling and Analysis Using Python and the 19th Conference on Artificial Intelligence for Environmental Science at the 2020 American Meteorological Society meeting. [abstract and presentation]
• Burke, Amanda; Snook, Nathan; McGovern, Amy (2020). Regional High-Impact Hail Forecasting Using Random Forests. Presented at the 19th Conference on Artificial Intelligence for Environmental Science at the 2020 American Meteorological Society meeting. [abstract and presentation]
• Flora, Montgomery; Potvin, Corey; Skinner, Patrick; McGovern, Amy (2020). Using Machine Learning to Improve Storm-Scale 1-h Probabilistic Forecasts of Severe Weather. Presented at the 19th Conference on Artificial Intelligence for Environmental Science at the 2020 American Meteorological Society meeting. [abstract and presentation]
• Allen, John; Lagerquist, Ryan; McGovern, Amy (2020). Climatology and Variability of Warm and Cold Fronts over North America. Presented at the 33rd Conference on Climate Variability and Change at the 2020 American Meteorological Society meeting. [abstract and presentation]
• Lagerquist, Ryan; Allen, John; McGovern, Amy (2020). Using Deep Learning to Create a Long-Term Climatology of Warm and Cold Fronts. Presented at the 19th Conference on Artificial Intelligence for Environmental Science at the 2020 American Meteorological Society meeting. [abstract and presentation]
• Lagerquist, Ryan; McGovern, Amy; Homeyer, Cameron; Gagne, David John; Smith, Travis (2020). Short-Term Tornado Prediction via Deep Learning on 3D Multiscale Data. Presented at the Severe Local Storms Symposium at the 2020 American Meteorological Society meeting. [abstract and presentation]
• Snook, Nathan; Burke, Amanda; McGovern, Amy; Gagne, David John. (2020). Results and Verification for Machine-Learning-Based HREFv2 and HRRRE Hail Forecasts from the Spring and Summer of 2019. Poster at the 10th Conference on Transition of Research to Operations at the 2020 American Meteorological Society meeting. [abstract]
• Lagerquist, Ryan; McGovern, Amy; Gagne, David John; Homeyer, Cameron; Smith, Travis (2020). Understanding What Deep Learning Has Learned about Tornadoes. Presented at the Joint Session on Physical Interpretability in Machine Learning at the 26th Conference on Probability and Statistics and the 19th Conference on Artificial Intelligence for Environmental Science at the 2020 American Meteorological Society meeting. [abstract and presentation]
• Avery, Katherine; McGovern, Amy; Bridge, Eli; Kelly, Jeffrey F. (2020). Locating Bird Roosts Using NEXRAD Radar Data and Image Segmentation. Presented at the 19th Conference on Artificial Intelligence for Environmental Science at the 2020 American Meteorological Society meeting. [abstract and presentation]
• McGovern, Amy (2020). Lessons Learned Using ML for Knowledge Discovery in the Atmospheric Sciences. Presented at the Joint Session on Physical Interpretability in Machine Learning at the 26th Conference on Probability and Statistics and the 19th Conference on Artificial Intelligence for Environmental Science at the 2020 American Meteorological Society meeting. [abstract and presentation]
• Harrison, David; McGovern, Amy; Karstens, Christopher (2020). Predicting Storm Prediction Center Watch Likelihood Using Machine Learning. Presented at the 19th Conference on Artificial Intelligence for Environmental Science at the 2020 American Meteorological Society meeting. [abstract and presentation]
• Smith, Travis; Calhoun, K. M.; Campbell, Patrick A.; Ortega, Kiel L.; Reinhart, Anthony; Fransisco, Dianna M.; Steeves, Rebecca B.; Klockow-McClain, Kim E.; Berry, Kodi; Williams, Skylar S.; McGovern, Amy; Lagerquist, Ryan A.; Meyer, Tiffany C.; Stumpf, Gregory J.; Gerard, Alan E.; (2020). Probabilistic Hazard Information for Severe Convective Storms in FACETS—Progress and Plans. Presented at the 10th Conference on Transition of Research to Operations at the 2020 American Meteorological Society meeting. [abstract and presentation]
  
• McGovern, Amy; Hickey, Jason; Hall, David; Ebert-Uphoff, Imme; Thorncroft, Christopher; Williams, John; Trapp, Robert J.; He, Ruoying; Bromberg, Carla (2020). AI2ES: Alpha-Institute—Artificial Intelligence for Environmental Sciences. Presented at the 19th Conference on Artificial Intelligence for Environmental Science at the 2020 American Meteorological Society meeting. [abstract and presentation]

2019

• McGovern, A., D.J. Gagne II, R. Lagerquist, K. Elmore, and G.E. Jergensen (2019) Making the black box more transparent: Understanding the physical implications of machine learning. Bulletin of the American Meteorological Society, Volume 100, Number 11, Pages 2175-2199. https://doi.org/10.1175/BAMS-D-18-0195.1
• McGovern, A., C. Karstens, T. Smith, and R. Lagerquist, (2019) Quasi-Operational Testing of Real-time Storm-longevity Prediction via Machine Learning. Weather and Forecasting, Volume 34, Number 5, Pages 1437-1451. [paper on WAF website]
• Lagerquist, R., A. McGovern, and D.J. Gagne II. (2019) Deep learning for spatially explicit prediction of synoptic-scale fronts. Weather and Forecasting, Volume 34, Number 4, Pages 1137-1160. [paper on WAF website]
• Moss, R.H.; Avery, S.; Baja, K.; Burkett, M.; Chischilly, A.M.; Dell, J.; Fleming, P.A.,; Geil, K.; Jacobs, K.; Jones, A.; Knowlton, K.; Koh, J.; Melillo, J.; Pandya, R.; Richmond, T.C.; Scarlett, L.; Snyder, J.; Stults, M.; Waple, A.; Whitehead, J.; Zarrilli, D.; Ayyub, B.; Fox, J.; Ganguly, A.; Joppa, L.; Julius, S.; Kirshen, P.; Kreutter, R.; McGovern, A.; Meyer, R.; Neumann, J.; Solecki, W.; Smith, J.; Tissot, P.; Yohe, G.; Zimmerman, R.,(2019) Evaluating Knowledge to Support Climate Action: A Framework for Sustained Assessment: Report of an Independent Advisory Committee on Applied Climate Assessment. Weather, Climate, and Society. 11:3, pages 465-487. [paper on WCAS website]
• McGovern, Amy (2019). Using Machine Learning to Improve Prediction and Understanding of Convective Hazards. Presented at the NOAA STAR 1st Workshop on Leveraging AI in the Exploitation of Satellite Observations and Numerical Weather Prediction. [slides]
• Jergensen, G. E.; McGovern, A.; Obermeier, H.; and Smith, T. (2019) Real-Time and Climatological Storm Classification Through Deep Learning. Presented at the 18th Conf on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the 2019 American Meteorological Society meeting. [abstract]
• Lagerquist, R.; McGovern, A.; Homeyer, C. R.; Potvin, C.K.; Sandmael, T.; Smith, T. M. (2018) Development and Interpretation of Deep-learning Models for Nowcasting Convective Hazards. Presented at the 18th Conf on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the 2019 American Meteorological Society meeting. [abstract]
• Snook, N.; Gagne, D. J.; Burke, A.; McGovern, A. (2019) An Overview of Hail Prediction using Random Forests during the 2018 Hazardous Weather Testbed Spring Forecast Experiment. Presented at the 18th Conf on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the 2019 American Meteorological Society meeting. [abstract]
• Burke, A.; Snook, N.; Gagne, D.J.; McGovern, A. (2019) Real-time Hail Prediction using Machine Learning Algorithms and HREFv2. Presented at the 18th Conf on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the 2019 American Meteorological Society meeting. [abstract]
  
• Flora, M. L.; Potvin, C.K.; Skinner, P.; McGovern, A. (2019) Using Machine Learning to improve storm-scale 1-h probabilistic low-level rotation forecasts. Presented at the 18th Conf on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the 2019 American Meteorological Society meeting. [abstract]
• Loken, E. D.; Clark, A. J.; McGovern, A. (2019) Post-Processing HREFv2 Heavy Rainfall Forecasts Using Machine Learning. Presented at the 18th Conf on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the 2019 American Meteorological Society meeting. [abstract]
• McGovern, A.; Gagne, D.J.; Lagerquist, R. A.; Jergensen, E.; Elmore, K. L. (2019) Making the black box more transparent: Understanding the Physical Implications of Machine Learning (Core Science Keynote). Presented at the 18th Conf on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the 2019 American Meteorological Society meeting. [abstract]

2018

• McGovern, A; Lagerquist, R; Elmore, K. Gagne II, D.J.; and Jergensen, E. (2018) Interpretation and Visualization of Weather-based Machine-learning Models. Presented at the Emerging Data Science and Machine Learning Opportunities in the Weather and Climate Sciences workshop at the 2018 AGU Fall meeting. [link to workshop]
• Sweeney, A. J.; Homeyer, C.; Lagerquist, R; McGovern, A.; and Sandmael, T. (2018). Seasonal evaluation of Storm Life Cycles in the CONUS from 12 years of Radar-Based Storm Tracks. Presented at the AGU Fall Meeting.[abstract]
• Lagerquist, R. A.; Homeyer, C. R.; McGovern, A. ; Potvin, C. K.; Sandmael, T.; and Smith, T. M. (2018) Deep Learning for Real-Time Storm-Based Tornado Prediction. Presented at the 2018 Severe Local Storms Conference [abstract]
• Chilson, Carmen; Avery, Katherine; McGovern, Amy; Bridge, Eli; Sheldon, Daniel and Kelly, Jeffrey (2018) Automated Detection of Bird Roosts using NEXRAD Radar Data and Convolutional Neural Networks. Remote Sensing in Ecology and Conservation. [link to online edition][pdf]
• McGovern, S.; Karstens, C.; Harrison, D.; Smith, T. (2018). Using Machine Learning to Predict Storm Longevity in Real Time. Presented at the 17th Conf on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the 2018 American Meteorological Society meeting. [abstract]
• Green, T.; McGovern, A.; Snook, N. (2018). Analyzing the Sensitivity of Hail Prediction to Model Grid Spacing. Presented at the 22nd Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS) at the 2018 American Meteorological Society meeting. [abstract]
• Harrison, D.; Karstens, C.; McGovern, A. (2018) Using Machine Learning Techniques to Predict Near-Term Severe Weather Trends. Presented at the 13th Symposium on Societal Applications: Policy, Research and Practice at the 2018 American Meteorological Society meeting. [abstract]
• Chilson, C.; Avery, K.; McGovern, A.; Bridge, E.; Sheldon, D.; Kelly, J. F. (2018) Automated Detection of Bird Roosts using NEXRAD Radar Data and Convolutional Neural Networks. Presented at the 17th Conf on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the 2018 American Meteorological Society meeting. [abstract]
• Gagne II, D. J.; Adams-Selin, R.; Thompson, G.; Gallo, B.; McGovern, A.; Romine, G.; Schwartz, C.; Snook, N.; Sobash, R.A.; (2018) Evaluation of Hail Size Forecasting Models during the 2016 Hazardous Weather Testbed Spring Experiment. Presented at the 25th Conference on Probability and Statistics at the 2018 American Meteorological Society meeting. [abstract]
• Lagerquist, R.; McGovern, A.; Richman, M. B.; Smith, T.; (2018) Using Machine Learning to Forecast Severe Thunderstorm Winds on a CONUS-Wide Grid. Presented at the 17th Conf on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the 2018 American Meteorological Society meeting. [abstract]
• Jergensen, E.; McGovern, A.; Karstens, C.; Obermeier, H.; Smith, T. (2018) Real-Time and Climatological Storm Classification Using Support Vector Machines. Presented at the 17th Conf on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the 2018 American Meteorological Society meeting. [abstract]
• McGovern, A.; Jergensen, E.; Karstens, C.; Obermeier, H.; Smith, T. (2018) Real-Time and Climatological Storm Classification Using Machine Learning. Presented at the 17th Conf on Artificial and Computational Intelligence and its Applications to the Environmental Sciences at the 2018 American Meteorological Society meeting. [abstract]

2017

• Lagerquist, Ryan; McGovern, Amy and Smith, Travis. (2017) Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind. Weather and Forecasting, 32:6, pages 2175-2193. [paper on WAF website] [pdf]
• Gagne II, David John; McGovern, Amy; Haupt, Sue Ellen; Sobash, Ryan; Williams, John K. and Xue, Ming. (2017) Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles. Weather and Forecasting, 32, 1819-1840. [paper on AMS website] [pdf]
• Trytten, Deborah and McGovern, Amy (2017) Moving from Managing Enrollment to Predicting Student Success. To appear in the Proceedings of the Frontiers in Education.
• Gagne II, David John; Haupt, Sue Ellen; McGovern, Amy; and Williams, John K. (2017) Evaluation of Statistical Learning Configurations for Gridded Solar Irradiance Forecasting. Solar Energy. Volume 150, pages 383-393. [link to pdf on publisher site]
• 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. (2017) Bulletin of the American Meteorological Society. Volume 98, Issue 10, pages 2073-2090. [paper on BAMS website] [pdf]
• McGovern, Amy and Potvin, Corey and Brown, Rodger A. (2017) 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. The book is avaialble here.
• 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]
• 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]
• 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]
• 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]
• 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]
• 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]
• 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]
• 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]

2016

• 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]
• 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)
  • This is a visualzation at Supercomputing 16 and the movie to go with the presentation can be found here.
• 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]
  • This short paper goes with a visualization which is linked here.
• Lagerquist, Ryan and McGovern, Amy and Smith, Travis and Richman, Michael and Lakshmanan, Valliappa. (2016) Importance-Ranking of Climate Variables for Prediction of Damaging Straight-Line Winds.
  • Oral presentation: The 13th International Meeting on Statistical Climatology. [slides]
    
  • Poster presentation: 2016 Severe Convection and Climate Workshop [poster] [abstract]
• Foss, Greg and McGovern, Amy and Potvin, Corey and Abram, G. and Bowen, A. and Hulkoti, N. and Kaul, A. (2016) Showcase: Data Mining Tornadogenesis Precursors. Eurographics Symposium on Parallel Graphics and Visualization. [pdf, video]
• Gagne II, David John and McGovern, A, and Snook, N. and Sobash, R. A. and Labriola, J. D. and Williams, J. K. and Haupt, S. E. and Xue, M. (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]
• Balfour, Andrea and Davis, T., and McGovern, A. (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]
• Gagne II, David John and McGovern, Amy and Snook, N. and Sobash, R. A. and Xue, M. (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]
• Lagerquist, Ryan A. and McGovern, Amy and Lakshmanan, V. and Smith, T. M. (2016) Real-time Prediction of Damaging Straight-line Winds Produced by Thunderstorms. To be 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]
• McGovern, Amy and Karstens, C. D. and Smith, T. M. and Calhoun, K. M. (2016) Using Machine Learning for Nowcasting Severe Weather Hazards. To be 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]
• McGovern, Amy and Potvin, C. K. and Brown, R. A. (2016) Combining Large-Scale Machine Learning Techniques with HPC to Better Understand Tornadoes. To be 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]

2015

• Clark, Adam and MacKenzie, Andrew and McGovern, Amy and 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.
• Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (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.
• McGovern, Amy and Gagne II, David John and Basara, Jeffrey and 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)
• Amy McGovern, Andrea Balfour, Marissa Beene, David Harrison (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.
• Gagne II, David John and McGovern, Amy and Brotzge, Jerald and Coniglio, Michael and 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)
• Gagne, David John and McGovern, A. and Brotzge, J. and Coniglio, M. C. and Correia Jr., J. and Xue, M. (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]
• Gagne, David John and Haupt, S.E. and Linden, S. and Williams, J. K. and McGovern, A. and Wiener, G. and 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]
• Harrison, David and Roux, Z. A. and 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]

2014

• Gagne, David John, Amy McGovern, Ming Xue, 2014: Machine Learning Enhancement of Storm-Scale Ensemble Probabilistic Quantitative Precipitation Forecasts. Wea. Forecasting, 29, 1024–1043. doi: http://dx.doi.org/10.1175/WAF-D-13-00108.1 [pdf (4 MB)]
• McGovern, Amy and 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.]
• 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. Vol 95, Issue 1, pages 27-50. [online version, open access]
• Gagne, David John; McGovern, Amy; Brotzge, Jerald; Coniglio, Michael C.; Correia Jr., James and Xue, Ming. (2014) Hail Size Prediction Using Machine Learning Techniques Applied to Storm Scale Ensembles. Presented at the 2014 Severe Local Storms Conference, American Meteorological Society. Link to handout/poster
• Dahl, B.; Potvin, C. K.; Potvin; Wicker, L. J.; McGovern, A; and Brown, R. A. (2014) Sensitivity of Vortex Production to Small Environmental Perturbations in High-Resolution Supercell Simulations. Presented at the 2014 Severe Local Storms Conference, American Meteorological Society. Link to handout/poster
• Dokken, D. P.; P. Belik; K. Scholz; M. M. Shvartsman; C. K. Potvin; B. A. Dahl and A. McGovern (2014) Possible implications of a vortex gas model and self-similarity for tornadogenesis and maintenance. University of Minnesota Math Physics Seminar Series, Minneapolis, MN.
• Dokken, D. P.; P. Belik; K. Scholz; M. M. Shvartsman; C. K. Potvin; B. A. Dahl and A. McGovern (2014) Applications of Statistical Mechanics of Vortex Gases to Tornadogenesis. Presented at the 2014 Severe Local Storms Conference, American Meteorological Society. Link to handout/poster
• Dokken, D. P.; P. Belik; K. Scholz; M. M. Shvartsman; C. K. Potvin; B. A. Dahl and A. McGovern (2014) Possible implications of a vortex gas model and self-similarity for tornadogenesis and maintenance. arXiv:1403.0197v5
• Katona, Branden T. and McGovern, Amy and 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]
• MacKenzie, Andrew J. and McGovern, Amy and Lakshmanan, V. and 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]
• MacKenzie, Andrew J. and Lakshmanan, V. and McGovern, Amy and 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]
• Dahl, Brittany and Potvin, Corey K. and Wicker, Louis J. and 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]
• Harrison, David and Balfour, Andrea and 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]

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]
  
• Gagne II, David and McGovern, Amy and 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)]
• 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)]
• 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 and earlier

• 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]
• 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)]
• McGovern, Amy; Moss, E; and Barto, A; (2000). Building a Basic Block Instruction Scheduler using Reinforcement Learning and Rollouts. Chapter in Neural Computing Surveys. Volume 3, pages 1-58 [pdf, online copy]
• 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)]