Professor, Civil and Environmental Engineering Director, Link Lab
Bio
Ph.D., Civil Engineering, University of Texas at Austin, 2005M.S., Civil Engineering, University of Texas at Austin, 2003B.S., Civil Engineering, University of Virginia, 2001
Jon Goodall is a Professor in the Department of Civil and Environmental Engineering at the University of Virginia (UVA) and Director of the UVA Engineering Link Lab. He is a water resources engineer working to advance the use of data and computational sciences to improve the understanding, forecasting, and management of water systems. Much of his current work focuses on adapting techniques from cyber-physical systems for real-time flood mitigation in coastal urban communities experiencing sea level rise impacts. Professor Goodall leads the Hydroinformatics Research Group housed in the Link Lab, co-directs the UVA Public Interest Technology University Network (PIT-UN), and is a member of UVA's Environmental Resilience Institute and Center for Transportation Studies. He is a registered Professional Engineer (PE), a Fellow of the American Society of Civil Engineers, and an elected member of the Virginia Academy of Science, Engineering, and Medicine. He earned his Ph.D. and M.S. in Civil Engineering from the University of Texas at Austin and his B.S. in Civil Engineering from the University of Virginia.
Awards
Elected Member, Virginia Academy of Science, Engineering, and Medicine2021
Elected Fellow, American Society of Civil Engineering2018
Early Career Research Excellence Award, International Environmental Modelling & Software Society (iEMSs)2012
CAREER Award, National Science Foundation2009
Research Interests
Water Resources Engineering
Hydroinformatics
Stormwater
Flooding
Smart Cities
GIS
Selected Publications
pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms ABSRimer, S.P., Mullapudi, A., Troutman, S.C., Ewing, G., Bowes, B.D., Akin, A.A., Sadler, J., Kertesz, R., McDonnell, B., Montestruque, L. and Hathaway, J., 2023. Environmental Modelling & Software, p.105635.
Predicting combined tidal and pluvial flood inundation using a machine learning surrogate model ABSZahura, F.T. and Goodall, J.L., 2022. Journal of Hydrology: Regional Studies, 41, p.101087.
Flood resilience through crowdsourced rainfall data collection: Growing engagement faces non-uniform spatial adoption ABSChen, A.B., Goodall, J.L., Chen, T.D. and Zhang, Z., 2022. Journal of Hydrology, 609, p.127724.
Reinforcement learning-based real-time control of coastal urban stormwater systems to mitigate flooding and improve water quality. ABSBowes, B.D., Wang, C., Ercan, M.B., Culver, T.B., Beling, P.A. and Goodall, J.L., 2022. Environmental Science: Water Research & Technology, 8(10), pp.2065-2086.
Data-Driven Model Predictive Control For Real-Time Stormwater Management. ABSNing, J., Bowes, B.D., Goodall, J.L. and Behl, M., 2022, June. In 2022 American Control Conference (ACC) (pp. 1438-1443). IEEE.
