The University of Texas at Austin
P.O. Box X
Austin, Texas 78713-8924
Research Interests
Sustainable water resources management and decision support systems
Real-time data analytics, AI/ML, optimization
Natural hazard and risk assessment
Remote sensing applications in hydrology
Education
B.S. Civil and Environmental Engineering, University of California at Los Angeles, Los Angeles, California, 1995
M.S. Civil and Environmental Engineering, University of California at Berkeley, Berkeley, California, 1996
Ph.D. Civil and Environmental Engineering, University of California at Berkeley, Berkeley, California, 2000
Professional History
Senior Research Scientist, Research Scientist—Bureau of Economic Geology, 2011–Present
Principal Research Engineer, Senior Research Engineer, Research Engineer—Southwest Research Institute, 2003–2011
Environmental Engineer—SUNDA Environmental Technologies, LLC, 2000-2003
Environmental Engineer—Tetra Tech Inc., 1999–2000
Graduate Research Fellow—Los Alamos National Laboratory, 1998–1999
Selected Publications
Sun, A. Y., Jiang, P., Yang, Z. L., Xie, Y., & Chen, X., 2022. A graph neural network approach to basin-scale river network learning: The role of physics-based connectivity and data fusion. Hydrology and Earth System Sciences, in press.
Sun, A. Y., Yoon, H.Y., Shih C. Y., Zhong, Z., 2022, Applications of physics-informed scientific machine learning in subsurface science: A survey, in Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data, eds. A Karpatne, R Kannan, V Kumar, CRC Press, Boca Raton, FL, 2022. https://arxiv.org/abs/2104.04764
Sun, A. Y., Jiang, P., Mudunuru, M. K., & Chen, X. 2021, Explore Spatiotemporal Learning of Large Sample Hydrology Using Graph Neural Networks. Water Resources Research, e2021WR030394, https://doi.org/10.1029/2021WR030394
Sun, A. Y., Scanlon, B.R., Save H., and Rateb, A., 2021, Reconstruction of GRACE total water storage through automated machine learning, Water Resources Research, 57 (2), e2020WR028666, https://doi.org/10.1029/2020WR028666.
Sun, A. Y. and Tang, G., 2020, Downscaling satellite and reanalysis precipitation products using attention-based deep convolutional neural nets, Frontiers in Water, 2:536743, https://doi.org/10.3389/frwa.2020.536743.
Sun, A. Y., 2020, Optimal carbon storage reservoir management through deep reinforcement learning, Applied Energy, v. 278, 115660. [PDF]
Sun, A. Y. and Scanlon, B.R., 2019, How Can Big Data and Machine Learning Benefit Environment and Water Management: A Survey of Methods, Applications, and Future Directions, Environmental Research Letters, 14, 073001. [PDF]
Sun, A. Y., Scanlon, B.R., Zhang, Z., Walling, D., Bhanja, S., Mukherjee, A., Zhong, Z., 2019, Combining physically-based modeling and deep earning for fusing GRACE satellite data: Can we learn from mismatch? Water Resources Research, 55(2), 1179-1195 [PDF]
Sun, A.Y., Zhong, Z., Jeong, H., Yang, Q., 2019. Building complex event processing capability for intelligent environmental monitoring. Environmental Modelling & Software, 116: 1-6. [PDF]
Sun, A. Y., 2018, Discovering state-parameter mappings in subsurface models using generative adversarial networks, Geophysical Research Letters, 45(20): 11,137-11,146. [PDF]
Sun, A. Y., Xia, Y., Caldwell, T., and Hao, Z., 2018, Patterns of precipitation and soil moisture extremes in Texas, U.S.: A complex network analysis, Advances in Water Resources, v. 112, 203-213. [PDF]
Sun, A. Y., Jeong, H., Gonzalez, A., and Templeton, T., 2018, Metamodeling-based approach for risk assessment and cost estimation: application to geological carbon sequestration, Computers and Geosciences, v. 113, p. 70-80. [PDF]
Sun, A.Y., Chen, J., Donges, J., 2015, Global Terrestrial Water Storage Connectivity Revealed Using Complex Climate Network Analyses, Nonlinear Geophysical Processes, 22, 433-446, 2015.[PDF]
Sun, A. Y., Miranda, R. M., & Xu, X. 2014. Development of multi-metamodels to support surface water quality management and decision making. Environmental Earth Sciences, 1-12. [PDF]
Sun, A.Y., D. Wang, and X. Xu, 2014, Monthly streamflow forecasting using Gaussian Process regression, Journal of Hydrology, 511, 72–81 [PDF]
Sun, A.Y., 2013, Predicting groundwater level changes using GRACE data. Water Resources Research, 49, 1–13, doi:10.1002/
wrcr.20421.[PDF]
Sun, A. Y., 2013, Enabling collaborative decision-making in watershed management using cloud-computing services: Environmental Modelling & Software, v. 41, p. 93-97. [PDF]
Sun, A. Y., Green, R., Swenson, S., and Rodell, M., 2012, Toward calibration of regional groundwater models using GRACE data: Journal of Hydrology, v. 422-423, p. 1-9. [PDF]
Sun, A. Y., Green, R., Rodell, M., and Swenson, S., 2010, Inferring aquifer storage parameters using satellite and in situ measurements: estimation under uncertainty: Geophysical Research Letters, v. 37, L10401. [PDF]
Sun, A. Y., Morris, A., and Mohanty, S., 2009, Sequential updating of multimodal hydrogeologic parameter fields using localization and clustering techniques: Water Resources Research, v. 45, W07424.
