Biography
Shaowen Wang is a Professor and Head of the Department of Geography and Geographic Information Science; Richard and Margaret Romano Professorial Scholar; and an Affiliate Professor of the Department of Computer Science, Department of Urban and Regional Planning, and School of Information Sciences at the University of Illinois at Urbana-Champaign (UIUC). He has served as Founding Director of the CyberGIS Center for Advanced Digital and Spatial Studies at UIUC since 2013. He served as Associate Director of the National Center for Supercomputing Applications (NCSA) for CyberGIS from 2010 to 2017 and Lead of NCSA’s Earth and Environment Theme from 2014 to 2017.
Research Interests
- geographic information science and systems (GIS)
- advanced cyberinfrastructure and cyberGIS
- complex environmental and geospatial problems
- computational and data sciences
- high-performance and distributed computing
- spatial analysis and modeling.
Research Description
His research has been actively supported by a number of U.S. government agencies (e.g., CDC, DOE, EPA, NASA, NSF, USDA, and USGS) and industry. He has served as the principal investigator of several multi-institution projects sponsored by the National Science Foundation (NSF) for establishing the interdisciplinary field of cyberGIS and advancing related scientific problem solving in various domains (e.g., agriculture, bioenergy, emergency management, geography and spatial sciences, geosciences, and public health).
Education
- PhD, University of Iowa
- MCS, Computer Science, University of Iowa
- MS, Geography, Peking University
- BS, Computer Engineering, Tianjin University, China
Awards and Honors
He was a visiting scholar at Lund University, sponsored by NSF in 2006, and a NCSA fellow in 2007. He received the NSF CAREER Award in 2009. He was named a Helen Corley Petit Scholar for 2011-2012 and Centennial Scholar for 2013-2016 by UIUC’s College of Liberal Arts and Sciences.
Courses Taught
- GEOG 480 - Principles of GIS
- GEOG 595 - Advanced Digital and Spatial Studies
- GEOG 479 - Advanced GIS
- GEOG 379 - Introduction to GIS
Additional Campus Affiliations
Computer Science Affiliate Professor
External Links
He served on the University Consortium for Geographic Information Science’s (UCGIS) board of directors from 2009 to 2012 and President of UCGIS from 2016 to 2017. He served on the advisory board of the NSF Extreme Science and Engineering Discovery Environment (XSEDE) program from 2016 to 2018. He has served as a member of the Board on Earth Sciences and Resources of the National Academies of Sciences, Engineering, and Medicine since 2015.
Recent Publications
Baig, F., Michels, A., Xiao, Z., Han, S. Y., Padmanabhan, A., Li, Z., & Wang, S. (2022). CyberGIS-Cloud: A unified middleware framework for cloud-based geospatial research and education. In PEARC 2022 Conference Series - Practice and Experience in Advanced Research Computing 2022 - Revolutionary: Computing, Connections, You [46] (PEARC 2022 Conference Series - Practice and Experience in Advanced Research Computing 2022 - Revolutionary: Computing, Connections, You). Association for Computing Machinery, Inc. https://doi.org/10.1145/3491418.3535148
Fu, P., Jaiswal, D., McGrath, J. M., Wang, S., Long, S. P., & Bernacchi, C. J. (2022). Drought imprints on crops can reduce yield loss: Nature's insights for food security. Food and Energy Security, 11(1), [e332]. https://doi.org/10.1002/fes3.332
Guo, C., Hu, H., Wang, S., Rodriguez, L. F., Ting, K. C., & Lin, T. (2022). Multiperiod stochastic programming for biomass supply chain design under spatiotemporal variability of feedstock supply. Renewable Energy, 186, 378-393. https://doi.org/10.1016/j.renene.2021.12.144
Jiang, Z., He, W., Kirby, M. S., Sainju, A. M., Wang, S., Stanislawski, L. V., Shavers, E. J., & Usery, E. L. (2022). Weakly Supervised Spatial Deep Learning for Earth Image Segmentation Based on Imperfect Polyline Labels. ACM Transactions on Intelligent Systems and Technology, 13(2), [25]. https://doi.org/10.1145/3480970
Kang, J. Y., Michels, A., Crooks, A., Aldstadt, J., & Wang, S. (2022). An Integrated Framework of Global Sensitivity Analysis and Calibration for Spatially Explicit Agent-based Models. Transactions in GIS, 26(1), 100-128. https://doi.org/10.1111/tgis.12837