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Dr. Amy McGovern

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Amy McGovern of the School of Computer Science

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)

Professor, School of Computer Science, University of Oklahoma
Adjunct Professor, School of Meteorology, University of Oklahoma
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/data mining/data science for the physical sciences; Real-world applications with a special interest in high-impact weather. STEM education.

Dr. Amy McGovern is a professor in the School of Computer Science at the University of Oklahoma and an adjunct professor in the School of Meteorology 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).


Professional activities:
Editor, Weather and Forecasting, 2019-present

Associate Editor, Monthly Weather Review, 2019-present

Co-Chair, Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences, 2019

Co-Editor, AI Matters, 2018 – present

Vice-Chair, American Meteorological Society (AMS) committee on Artificial Intelligence and its Applications to Environmental Science, 2018-present

Awards and Honors:
University of Oklahoma Vice President for Research Award for Interdisciplinary Scholarship, Spring 2019

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: Artificial Intelligence, Spring 2009: Data Mining

NSF CAREER Award, 2008-2015

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.

NOAA, “Improving Operational Hail Prediction through Machine Learning from HREF and CAPS Storm-Scale Ensemble FV3 and WRF ARW Forecasts including Ad- vanced Microphysics”, $395,772, co-PI, 2018-2020

NSF, “EAGER: Improving our Understanding of Supercell Storms through Data Science”, $168,517, PI, 2018-2019

NOAA, Development and Implementation of Probabilistic Hail Forecast Products using Multi-moment Microphysics and Machine Learning Algorithms, $335,084, Co-PI, 2016-1018

NSF, NRT: Aeroecology as a Test-bed for Interdisciplinary STEM Training, $2,952,429, Co-PI, 2015-2020

NSF, CC*IIE Engineer: A Model for Advanced Cyberinfrastructure Research and Education Facilitators, $400,000, Co-PI, 2014-2016

NSF, The Severe Hail Analysis, Representation, and Prediction (SHARP) Project, $819,070, Co-PI, 2013-2016

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.

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;Gagne II,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.