Shirley Moore received her PhD in Computer Sciences from Purdue University in 1990. She has over 20 years of research experience in parallel scientific computing, which is her current main research interest.

• Ph D in Computer Sciences, Purdue University (1990)
• MS in Mathematics, Wichita State University (1985)
• M.Ed in Math and Science Education, University of Illinois (1980)
• BA in Mathematics and Chemistry, Indiana University (1978)

• Network Anomaly Detection Using Federated Learning Military Communications Conference (MILCOMM 2022) (2022), William Marfo, Deepak K. Tosh, Shirley V. Moore.
• High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory IEEE Cluster 2022 (2022), Daniel Puleri, Sayan Roychowdhury, Peter Balogh, John Gounley, Erik Draeger, Jeff Ames, Adebayo Adebiyi, Simbarashe Chidyagwai, Benjamin Hernandez, Seyong Lee, Shirley V. Moore, Jeffrey Vetter, Amanda Randles.
• Condition monitoring and anomaly detection in cyber-physical systems 17th Annual System of Systems Engineering Conference (SOSE) (2022), William Marfo, Deepak K. Tosh, Shirley V. Moore.
• Propagation Pattern for Moment Representation of the Lattice Boltzmann Method IEEE Transactions on Parallel and Distributed Computing 33 3 (2022), John Gounley, Madhurima Vardhan, Erik Draeger, Pedro Valero-Lara, Shirley V. Moore, Amanda Randles.
• Condition monitoring and anomaly detection in cyber-physical systems 17th Annual System of Systems Engineering Conference (SOSE) (2022), W. Marfo, Deepak K. Tosh, Shirley V. Moore.
• Network Anomaly Detection Using Federated Learning IEEE Military Communications Conference (MILCOM) (2022), William Marfo, Deepak K. Tosh, Shirley V. Moore.
• The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science International Journal of High Performance Computing Applications 36 1 (2022), Eric Suchyta, Scott Klasky, Norbert Podhorszki, Matthew W. , Bolaji Adesoji, Choong-Seock Chang, Jong Choi, Philip D. , Julien Dominski, Stephane E. , Ian F. , Kai Germaschewski, Berk Geveci, Chris Harris, Kevin Huck, Qing L. , Jeremy Logan, Kshitij Mehta, Gabriele Merlo, Shirley V. Moore, Todd Munson, Manish Parashar, David P. , Mark Shepherd, Cameron Smith, Pradeep Subedi, Lipeng Wan, Ruonan Wang, Shuangxi Zhang.

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• COLLABORATIVE RESEARCH: FRAMEWORKS: SCALABLE PERFORMANCE AND ACCURACY ANALYSIS FOR DISTRIBUTED AND EXTREME-SCALE SYSTEMS (SPADE)
• Performance and Scalability of Distributed Deep Learning