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2020 – today
- 2024
[j34]Amy McGovern
, Imme Ebert-Uphoff, Elizabeth A. Barnes, Ann Bostrom, Mariana G. Cains, Phillip Davis, Julie L. Demuth, Dimitrios I. Diochnos, Andrew H. Fagg, Philippe Tissot, John K. Williams, Christopher D. Wirz:
AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography. AI Mag. 45(1): 105-110 (2024)- [i10]Roch Guérin, Amy McGovern, Klara Nahrstedt:
Report on the NSF Workshop on Sustainable Computing for Sustainability (NSF WSCS 2024). CoRR abs/2407.06119 (2024) - [i9]Maria J. Molina, Amy McGovern, Jhayron S. Perez-Carrasquilla, Robin L. Tanamachi:
Using Generative Artificial Intelligence Creatively in the Classroom: Examples and Lessons Learned. CoRR abs/2409.05176 (2024) - 2023
- [i8]Randy J. Chase, Amy McGovern, Cameron Homeyer, Peter Marinescu, Corey K. Potvin:
Machine Learning Estimation of Maximum Vertical Velocity from Radar. CoRR abs/2310.09392 (2023) - 2022
- [i7]Randy J. Chase, David R. Harrison, Amanda Burke, Gary M. Lackmann, Amy McGovern:
A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning. CoRR abs/2204.07492 (2022) - [i6]Ignacio Lopez-Gomez, Amy McGovern, Shreya Agrawal, Jason Hickey:
Global Extreme Heat Forecasting Using Neural Weather Models. CoRR abs/2205.10972 (2022) - [i5]Randy J. Chase, David R. Harrison, Gary M. Lackmann, Amy McGovern:
A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning. CoRR abs/2211.00147 (2022) - [i4]Montgomery Flora, Corey K. Potvin, Amy McGovern, Shawn Handler:
Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement. CoRR abs/2211.08943 (2022) - [i3]Montgomery Flora, Corey K. Potvin, Amy McGovern, Shawn Handler:
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction. CoRR abs/2211.10378 (2022) - 2021
- [j33]Zhi Li, Mengye Chen, Shang Gao, Xiangyu Luo, Jonathan J. Gourley, Pierre Kirstetter, Tiantian Yang, Randall L. Kolar, Amy McGovern, Yixin Wen, Bo Rao, Teshome Yami, Yang Hong:
CREST-iMAP v1.0: A fully coupled hydrologic-hydraulic modeling framework dedicated to flood inundation mapping and prediction. Environ. Model. Softw. 141: 105051 (2021) - [i2]Amy McGovern, Imme Ebert-Uphoff, David John Gagne II, Ann Bostrom:
The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences. CoRR abs/2112.08453 (2021) - 2020
- [j32]Amy McGovern, Iolanda Leite, Anuj Karpatne:
Welcome to AI Matters 6(1). AI Matters 6(1): 3-4 (2020) - [j31]Amy McGovern:
NSF AI institute for research on trustworthy ai in weather, climate, and coastal oceanography. AI Matters 6(3): 14-16 (2020) - [i1]Montgomery Flora, Corey K. Potvin, Patrick S. Skinner, Shawn Handler, Amy McGovern:
Using Machine Learning to Calibrate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System. CoRR abs/2012.00679 (2020)
2010 – 2019
- 2019
- [j30]Amy McGovern, Iolanda Leite:
Welcome to AI matters 5(1). AI Matters 5(1): 3 (2019) - [j29]Amy McGovern, Iolanda Leite:
Welcome to AI matters 5(2). AI Matters 5(2): 3 (2019) - [j28]Amy McGovern, Iolanda Leite:
Welcome to AI matters 5(3). AI Matters 5(3): 4-5 (2019) - [j27]Sven Koenig, Sanmay Das, Rosemary D. Paradis, John P. Dickerson, Yolanda Gil, Katherine Guo, Benjamin Kuipers, Iolanda Leite, Hang Ma, Nicholas Mattei, Amy McGovern, Larry R. Medsker, Todd W. Neller, Marion Neumann, Plamen Petrov, Michael Rovatsos, David G. Stork:
ACM SIGAI activity report. AI Matters 5(3): 6-11 (2019) - [j26]Amy McGovern, Iolanda Leite, Anuj Karpatne:
Welcome to AI matters 5(4). AI Matters 5(4): 3-4 (2019) - 2018
- [j25]Eric Eaton, Amy McGovern:
Welcome to AI matters, volume 3, issue 4. AI Matters 3(4): 3 (2018) - [j24]Amy McGovern, Eric Eaton:
An interview with Ayanna Howard. AI Matters 3(4): 5-7 (2018) - [j23]Amy McGovern, Eric Eaton:
Welcome to AI matters 4(1). AI Matters 4(1): 3-4 (2018) - [j22]Amy McGovern:
Welcome to AI matters 4(2). AI Matters 4(2): 3-4 (2018) - [j21]Amy McGovern:
Welcome to AI matters 4(3). AI Matters 4(3): 3 (2018) - [j20]Sven Koenig, Sanmay Das, Rosemary D. Paradis, John P. Dickerson, Yolanda Gil, Katherine Guo, Benjamin Kuipers, Hang Ma, Nicholas Mattei, Amy McGovern, Larry R. Medsker, Todd W. Neller, Plamen Petrov, Michael Rovatsos, David G. Stork:
ACM SIGAI activity report. AI Matters 4(3): 7-11 (2018) - [j19]Amy McGovern, Iolanda Leite:
Welcome to AI matters 4(4). AI Matters 4(4): 3-4 (2018) - 2017
- [j18]Amy McGovern, Eric Eaton:
AI profiles: an interview with Jim Kurose. AI Matters 3(1): 14-16 (2017) - [j17]Eric Eaton, Amy McGovern:
Welcome to AI matters, volume 3, issue 2. AI Matters 3(2): 3 (2017) - [j16]Amy McGovern, Eric Eaton:
AI profiles: an interview with Peter Stone. AI Matters 3(2): 8-10 (2017) - [j15]Eric Eaton, Amy McGovern:
Welcome to AI matters, volume 3, issue 3. AI Matters 3(3): 3 (2017) - [j14]Sven Koenig, Sanmay Das, Rosemary D. Paradis, Eric Eaton, Yolanda Gil, Katherine Guo, Bojun Huang, Albert Jiang, Benjamin Kuipers, Nicholas Mattei, Amy McGovern, Larry R. Medsker, Todd W. Neller, Plamen Petrov, Michael Rovatsos, David G. Stork:
ACM SIGAI activity report. AI Matters 3(3): 7-11 (2017) - [j13]Amy McGovern, Eric Eaton:
AI profiles: an interview with Maja Matarić. AI Matters 3(3): 12-14 (2017) - [c23]Deborah Anne Trytten, Amy McGovern:
Moving from managing enrollment to predicting student success. FIE 2017: 1-9 - [c22]Greg Foss, Amy McGovern, Corey K. Potvin, Brittany A. Dahl, Greg Abram, Anne Bowen, Neena Hulkoti, Arnav Kaul, Nick Sehy:
Spot the Difference: Tornado Visualizations. PEARC 2017: 76:1-76:3 - 2016
- [j12]Eric Eaton, Amy McGovern:
Welcome to AI Matters, volume 2, issue 3. AI Matters 2(3): 3 (2016) - [j11]Amy McGovern, Eric Eaton:
AI profiles: an interview with Peter Norvig. AI Matters 2(4): 4-6 (2016) - [c21]Greg Foss, Amy McGovern, Corey K. Potvin, Greg Abram, Anne Bowen, Neena Hulkoti, Arnav Kaul:
Data Mining Tornadogenesis Precursors. EGPGV@EuroVis 2016: 99 - 2015
- [j10]Robert Morris, Blai Bonet, Marc Cavazza, Marie desJardins, Ariel Felner, Nick Hawes, Brad Knox, Sven Koenig, George Dimitri Konidaris, Jérôme Lang, Carlos Linares López, Daniele Magazzeni, Amy McGovern, Sriraam Natarajan, Nathan R. Sturtevant, Michael Thielscher, William Yeoh, Sebastian Sardiña, Kiri Wagstaff:
A Summary of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AI Mag. 36(3): 99-106 (2015) - [j9]Eric Eaton, Amy McGovern:
Welcome to AI Matters, volume 2, issue 2. AI Matters 2(2): 3 (2015) - [c20]David John Gagne II, Amy McGovern, Jerald Brotzge, Michael Coniglio, James Correia Jr., Ming Xue:
Day-Ahead Hail Prediction Integrating Machine Learning with Storm-Scale Numerical Weather Models. AAAI 2015: 3954-3960 - 2014
- [j8]Amy McGovern, David John Gagne II, John K. Williams, Rodger A. Brown, Jeffrey B. Basara:
Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning. Mach. Learn. 95(1): 27-50 (2014) - 2013
- [j7]Amy McGovern, Nathaniel Troutman, Rodger A. Brown, John K. Williams, Jennifer Abernethy:
Enhanced spatiotemporal relational probability trees and forests. Data Min. Knowl. Discov. 26(2): 398-433 (2013) - [c19]Amy McGovern, Deborah Anne Trytten:
Making in-class competitions desirable for marginalized groups. FIE 2013: 704-706 - [c18]David John Gagne II, Amy McGovern, Jerald Brotzge, Ming Xue:
Severe Hail Prediction within a Spatiotemporal Relational Data Mining Framework. ICDM Workshops 2013: 994-1001 - 2012
- [c17]David John Gagne II, Amy McGovern, Ming Xue:
Machine learning enhancement of Storm Scale Ensemble precipitation forecasts. CIDU 2012: 39-46 - [c16]Scott Hellman, Amy McGovern, Ming Xue:
Learning ensembles of Continuous Bayesian Networks: An application to rainfall prediction. CIDU 2012: 112-117 - [c15]Bradley Pirtle, Ross Kimes, Amy McGovern, Rodger A. Brown:
Using the XSEDE supercomputing and visualization resources to improve tornado prediction using data mining. XSEDE 2012: 35:1-35:2 - 2011
- [j6]Amy McGovern, Derek H. Rosendahl, Rodger A. Brown, Kelvin Droegemeier:
Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction. Data Min. Knowl. Discov. 22(1-2): 232-258 (2011) - [j5]Amy McGovern, Kiri L. Wagstaff:
Machine learning in space: extending our reach. Mach. Learn. 84(3): 335-340 (2011) - [j4]Amy McGovern, David John Gagne II, Nathaniel Troutman, Rodger A. Brown, Jeffrey B. Basara, John K. Williams:
Using spatiotemporal relational random forests to improve our understanding of severe weather processes. Stat. Anal. Data Min. 4(4): 407-429 (2011) - [c14]Amy McGovern, Zachery Tidwel, Derek Rushing:
Teaching Introductory Artificial Intelligence through Java-Based Games. EAAI 2011: 1729-1736 - [c13]Zafar Ahmed, Patrick Yost, Amy McGovern, Chris E. Weaver:
Steerable Clustering for Visual Analysis of Ecosystems. EuroVA@EuroVis 2011 - 2010
- [c12]Amy McGovern, Timothy A. Supinie, David John Gagne II, Nathaniel Troutman, Matthew W. Collier, Rodger A. Brown, Jeffrey B. Basara, John K. Williams:
Severe Weather Processes through Spatiotemporal Relational Random Forests. CIDU 2010: 213-227
2000 – 2009
- 2009
- [c11]Matthew Bodenhamer, Samuel Bleckley, Daniel Fennelly, Andrew H. Fagg, Amy McGovern:
Spatio-temporal Multi-dimensional Relational Framework Trees. ICDM Workshops 2009: 564-570 - [c10]Timothy A. Supinie, Amy McGovern, John K. Williams, Jennifer Abernethy:
Spatiotemporal Relational Random Forests. ICDM Workshops 2009: 630-635 - 2008
- [j3]Amy McGovern, David D. Jensen:
Optimistic pruning for multiple instance learning. Pattern Recognit. Lett. 29(9): 1252-1260 (2008) - [c9]Matthew W. Collier, Amy McGovern:
Kernels for the Investigation of Localized Spatiotemporal Transitions of Drought with Support Vector Machines. ICDM Workshops 2008: 359-368 - [c8]Amy McGovern, Nathan C. Hiers, Matthew W. Collier, David John Gagne II, Rodger A. Brown:
Spatiotemporal Relational Probability Trees: An Introduction. ICDM 2008: 935-940 - 2007
- [c7]William Dabney, Amy McGovern:
Utile Distinctions for Relational Reinforcement Learning. IJCAI 2007: 738-743 - [c6]Amy McGovern, Jason Fager:
Creating significant learning experiences in introductory artificial intelligence. SIGCSE 2007: 39-43 - 2003
- [j2]Amy McGovern, Lisa Friedland, Michael Hay, Brian Gallagher, Andrew S. Fast, Jennifer Neville, David D. Jensen:
Exploiting relational structure to understand publication patterns in high-energy physics. SIGKDD Explor. 5(2): 165-172 (2003) - [c5]Amy McGovern, David D. Jensen:
Identifying Predictive Structures in Relational Data Using Multiple Instance Learning. ICML 2003: 528-535 - 2002
- [j1]Amy McGovern, J. Eliot B. Moss, Andrew G. Barto:
Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts. Mach. Learn. 49(2-3): 141-160 (2002) - [c4]Amy McGovern:
Autonomous Discovery of Abstractions through Interaction with an Environment. SARA 2002: 338-339 - 2001
- [c3]Amy McGovern, Andrew G. Barto:
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density. ICML 2001: 361-368
1990 – 1999
- 1998
- [c2]Martin O. Hofmann, Amy McGovern, Kenneth R. Whitebread:
Mobile Agents on the Digital Battlefield. Agents 1998: 219-225 - [c1]Amy McGovern, J. Eliot B. Moss:
Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts. NIPS 1998: 903-909
Coauthor Index
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last updated on 2024-10-10 22:19 CEST by the dblp team
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