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Amy McGovern of the School of Computer Science


Associate Professor
LaboratoryInteraction,Discovery, Exploration, and Adaptation lab
Phone:  (405) 325-5427 | Office: DEH 251


PhD, University of Massachusetts Amherst
MS, University of Massachusetts Amherst
BS, Carnegie Mellon University (with honors)

Associate Professor, School of Computer Science, University of Oklahoma
Adjunct Associate Professor, School of Meteorology, University of Oklahoma
Assistant Professor, School of Computer Science, University of Oklahoma
Adjunct Assistant Professor, School of Meteorology, University of Oklahoma


Machine learning and data mining, spatiotemporal and relational data mining; Real-world applications with a special interest in high-impact weather. STEM education.

Dr. Amy McGovern is an associate professor in the School of Computer Science at the University of Oklahoma. Dr. McGovern is an NSF CAREER award winner and her research focuses on developing novel spatiotemporal data mining method for real-world applications, particularly focusing on severe weather. Dr. McGovern received her PhD in Computer Science from the University of Massachusetts Amherst in 2002 and was a senior postdoctoral research associate at the University of Massachusetts until joining the University of Oklahoma in January, 2005. She received her MS from the University of Massachusetts Amherst (1998) and her BS (honors) from Carnegie Mellon University (1996).

Co-Editor, AI Matters, 2016-present

Co-Chair, 14th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences, 2016

Member, American Meteorological Society (AMS) committee on Probability and Statistics, 2016 - 2018

NSF CAREER Award, 2008-2015

Teaching Scholars Initiative Award, Fall 2012

College of Engineering and Michael F. Price College of Business Alumni Teaching Award. Awarded to top teachers within the College of Engineering and Price School of Business. Spring 2008: Artifi- cial Intelligence, Spring 2009: Data Mining


Title: Developing Spatiotemporal Relational Models to Anticipate Tornado Formation

The goal of this research is to revolutionize the ability to anticipate tornadoes by developing advanced techniques for statistical pattern discovery in spatially and temporally varying relational data. These models are applied to complete fields of meteorological quantities obtained through data assimilation and simulation. Doppler radar data is limited and, while modern data assimilation techniques allow the unobserved quantities to be estimated, the resulting four-dimensional fields are too complicated for the extraction of meaningful, repeatable patterns by either humans or current data mining techniques. By studying a full field of variables, the models can identify critical interactions among high level features. The models are developed and verified in close collaboration with domain experts. The interdisciplinary nature of the research is used to improve retention and recruitment in computer science (CS). Introducing authentic projects into both early CS and meteorology classes will improve the number of technically trained students in both majors.

NSF, “CSR: Small: Learning-Assisted Parellization,” Co-PI, $500K, Aug. 2010-July 2013.

IBM through DARPA, “Learning to Guide Search in Large State Spaces,” PI, $95K, Jan. 2010-Dec. 2010.

NSF CAREER, “Developing Spatiotemporal Relational Models to Anticipate Tornado Formation,” PI, $500K, July 2008-June 2013.

Defense Advanced Research Projects Agency, subcontract to the University of California Berkeley, “Using Go as a Platform for Knowledge Guided Exploration,”, PI, $400K, Aug 2008-Sept 2009.

NOAA, Development and Implementation of Probabilistic Hail Forecast Products using Multi-moment Microphysics and Machine Learning Algorithms, $335,084, Co-PI NSF, NRT: Aeroecology as a Test-bed for Interdisciplinary STEM Training, $2,952,429, Co-PI NSF, CC*IIE Engineer: A Model for Ad- vanced Cyberinfrastructure Research and Education Facilitators, $400,000, Co-PI NSF, The Severe Hail Analysis, Representa- tion, and Prediction (SHARP) Project, $819,070, Role: Co-PI

 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.

Trytten, Deborah and McGovern, Amy (2017) Moving from Managing Enrollment to Predicting Student Success. To appear in the Proceedings of the Frontiers in Education.

McGovern,Amy;Elmore,Kim;GagneII,DavidJohn;Haupt,SueEllen;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.

 Foss, G; McGovern, A; Potvin, C.K.; Dahl, B.; Abram, G.; Bowen, A.; Hulkoti, N. and Kaul, A. (2017). Spot the Difference; Tornado Visualizations Extended Abstract in Practice and Experience in Advanced Research Computing Conference Series.

McGovern, Amy; 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.