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.
- 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.
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).
- 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.
- 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
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.
Choi, Y. D., Goodall, J. L., Sadler, J. M., Castronova, A. M., Bennett, A., Li, Z., Nijssen, B., Wang, S., Clark, M. P., Ames, D. P., Horsburgh, J. S., Yi, H., Bandaragoda, C., Seul, M., Hooper, R., & Tarboton, D. G. (2021). Toward open and reproducible environmental modeling by integrating online data repositories, computational environments, and model Application Programming Interfaces. Environmental Modelling and Software, 135, . https://doi.org/10.1016/j.envsoft.2020.104888
Davis, A. V., & Wang, S. (Accepted/In press). A Concurrent Entity Component System for Geographical Wildlife Epidemiological Modeling. Geographical Analysis. https://doi.org/10.1111/gean.12258
Jiang, H., Hu, H., Zhong, R., Xu, J., Xu, J., Huang, J., Wang, S., Ying, Y., & Lin, T. (2020). A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level. Global change biology, 26(3), 1754-1766. https://doi.org/10.1111/gcb.14885
Jiang, H., Hu, H., Wang, S., Ying, Y., & Lin, T. (2020). Understanding the impact of sub-seasonal meteorological variability on corn yield in the U.S. Corn Belt. Science of the Total Environment, 724, . https://doi.org/10.1016/j.scitotenv.2020.138235
Kang, J. Y., Aldstadt, J., Vandewalle, R., Yin, D., & Wang, S. (2020). A CyberGIS Approach to Spatiotemporally Explicit Uncertainty and Global Sensitivity Analysis for Agent-Based Modeling of Vector-Borne Disease Transmission. Annals of the American Association of Geographers, 110(6), 1855-1873. https://doi.org/10.1080/24694452.2020.1723400