Vidya Samadi, Ph.D., M.ASCE

Assistant Professor of Water Resources Engineering/Affiliate Faculty with AI Research Institute for Science & Engineering
Visiting Academic Fellow, Division of Civil Engineering, Department of Engineering, The University of Cambridge, UK
Agricultural Sciences Department

Office:
Phone:

Email: samadi@clemson.edu
Personal Website: http://hydro-informatics-lab.com/

Educational Background

Research Assistant Professor of Water Resources Engineering
Department of Civil and Environmental Engineering,School of Engineering and Computing, University of South Carolina.

Postdoc in Water Resources Management
The Carolinas Integrated Sciences & Assessments (CISA; NOAA-funded research center), University of South Carolina.

Postdoc in Civil Engineering-Water Resources
Hydro-environmental Research Centre (HRC), Department of Civil Engineering, School of Engineering, Cardiff University, United Kingdom.

Ph.D. (D.Engr.)
Water Science and Engineering.

M.S. (M.Engr.)
Water Engineering, University of Tehran, Iran.

Courses Taught

-Computational Methods in Water Resources (Clemson University)
-GIS in Civil and Environmental Engineering (UofSC)
-GIS in Water Resources Engineering (UofSC)
-Research in Civil Engineering (UofSC)
-Doctoral Dissertation Research in Civil Engineering (UofSC & Clemson University)
-Doctoral Dissertation Research in Agricultural Sciences(Clemson University)
-Master’s Thesis Research in Computer Science (Clemson University)
-River Basin Management (Cardiff University & UofSC)
-Engineering Hydrology (UofSC)
-Advanced Hydrology (co-taught with Prof. Meadows of UofSC)
-Senior Design in Civil Engineering (co-taught with Prof. Meadows of UofSC)

Profile

Dr. Vidya Samadi is an Assistant Professor of Water Resources Engineering and an Affiliate Faculty at the AI Research Institute for Science & Engineering at Clemson University, as well as a Visiting Academic Fellow in the Department of Engineering (Division of Civil Engineering) at the University of Cambridge, UK. Her research advances hydroinformatics and cyber-physical systems, with a focus on developing analytics and artificial intelligence (AI) computing systems for water system modeling, including surface water informatics, precision irrigation software, and integrated water management.

A recognized leader in her field, Dr. Samadi was selected for the 2024 NSF CMMI Game Changer Academies for Advancing Research Innovation and received the 2024 Universities Council on Water Resources (UCOWR) Mid-Career Award for Applied Research. She has contributed her expertise as a panelist for the 2023 National Academies of Engineering & U.S. Government Accountability Office Expert Meeting on AI in Environmental Modeling, addressing storms, floods, and wildfires. Her honors include the American Society of Civil Engineers (ASCE) Technical Merit Award and Outstanding Reviewer Award. She has also served as Chair of the CUAHSI Informatics Committee and as a Board Member of the International Environmental Modelling & Software Society. In recognition of her leadership in AI-driven water research, she was awarded the National Science Foundation’s prestigious National Artificial Intelligence Research Resource (NAIRR) Pilot Award.

Research Interests

Dr. Samadi advances hydroinformatics and cyber-physical modeling systems, integrating water resources engineering, computer science, and AI to address critical challenges in water management and infrastructure resilience. Her research has been continuously funded by multiple National Science Foundation programs, including CBET, CMMI, GEO-Cyberinfrastructure, and OAC, as well as by agencies such as USGS, USDA, Savannah River National Laboratory, NOAA, and the U.S. Department of Transportation.

Recent Funded Projects (selected)
-Sole PI on the NSF CBET FloodEngine Project (NSF CBET #2429082)
-Lead PI on the NSF CyberTraining for Water Science (NSF OAC Award #2320979)
-Lead PI on the NSF SCC for Evacuation Planning Tool Development (NSF CMMI #2125283)
-Lead PI on the USDA-NIFA Irrigation Tool Development (NIFA #2023-67022-40555)
-Sole PI on the USGS Water Use Autonomous Agents Project (USGS #2024917)
-Lead PI on the Sustainable Agriculture Research and Education

Extension and Outreach

Dr. Samadi serves on the WMO-Global Energy and Water Exchanges (GEWEX) Hydrometeorology Panel (GHP). She also serves as a board member of the International Environmental Modelling and Software Society. At the state level, Vidya works collaboratively with state stakeholders and other officials across the state of South Carolina to address water research and outreach.

Publications

Recent Research Papers (please refer to my Google Scholar for a full list of publications)
* Denotes graduate students under my supervision.

1. Sadeghi Tabas S.*, Samadi V., Wilson C., Bhattacharya, B. Probabilistic Physics-guided Deep Neural Networks with Recurrence and Attention Mechanisms for Interpretable Daily Streamflow Simulation. Water Resources Research. In Press.
2. Zafarmomen, N*. and Samadi, V., 2025. Can Large Language Models Effectively Reason about Adverse Weather Conditions? Environmental Modelling & Software, p.106421.
3. Vidya Samadi, Hayley J. Fowler, Jessica Lamond, Thorsten Wagener, Manuela Brunner, Jonathan Gourley, Hamid Moradkhani, Ioana Popescu, Conrad Wasko, Daniel Wright, Huan Wu, Ke Zhang, Paola Andrea Arias Gomez, Qingyun Duan, Ali Nazemi, Petrus J van Oevelen, Andreas Prein, Joshua K. Roundy, Mostafa Saberian*, Lisa Umutoni*. 2025. The Needs, Challenges, and Priorities for Advancing Flood Research. Wiley WIREs Water.
4. Sadeghi Tabas S.*, Samadi V. 2024. Fill-and-Spill: A Novel Deep Reinforcement Learning for Water Infrastructure Management and Control. ASCE Journal of Water Resources Planning and Management, 150(7), p.04024022.
5. Guido, B. I.*, Popescu, I., Samadi, V., and Bhattacharya, B.2023. An integrated modeling approach to evaluate the impacts of nature-based solutions of flood mitigation across a small watershed in the southeast United States. The EGU Journal of Natural Hazards and Earth System Sciences. https://doi.org/10.5194/nhess-2022-281.
6. Windheuser L. *, Karanjit, R. *, Pally R. *, Samadi, S., and N.C. Hubig. 2023. An End-to-End Flood Gauge Height Prediction System Using Deep Neural Networks. The AGU Earth and Space Science, 10(1), p.e2022EA002385.
7. Phillips, R. C.*, Samadi, S., Hitchcock, B.D., Meadows, M., Wilson C. A. M.E. 2022. The devil is in the tail dependence: An assessment of multivariate copula-based frameworks and dependence concepts for coastal compound flood dynamics. The AGU Journal of Earth’s Future, p.e2022EF002705.
8. Sadeghi Tabas S.*, Samadi S. 2022. Variational Bayesian Dropout with a Gaussian Prior for Recurrent Neural Networks Application in Rainfall-Runoff Modeling. Environmental Research Letters.DOI:https://doi.org/10.1088/1748-9326/ac7247
9. Pally, R.*, Samadi S. 2022. Application of Image Processing and Convolutional Neural Networks for Flood Image Classification and Semantic Segmentation. Environmental Modelling & Software. 148, p.105285.
10. Samadi S., Pourreza-Bilondi M., Wilson C. A. M.E., Hitchcock, B.D. 2020. Bayesian Model Averaging with Fixed and Flexible Priors: Theory, Concepts, and Calibration Experiments for Rainfall-Runoff Modeling. The AGU Journal of Advances in Modeling Earth Systems. 12(7), p.e2019MS001924.

Software Patents
1. Pally, R. *, Kranjit, R. *, Sadeghi Tabas, S*. Samadi, S., 2022. Image processing and semantic segmentation for flood image analytics and inundation mapping. Software/Copyright Disclosure - Tech ID: 2022-049
2. Donratanapat, N.*, Kranjit, R. *, Sadeghi Tabas, S*. Samadi, S., 2022. Flood Analytics Information System (FAIS). Software/Copyright Disclosure - Tech ID: 2023-003.

Software/Package (selected)
1. Pally, R.*, and Samadi, S. 2022. A Python tool for Flood Image Classification and Semantic Segmentation (funded by NSF). Released strictly using the MIT license.
2. Donratanapat, N.*, Samadi S., and Vidal, J. 2019. “FAIS”: Flood Analytics Information System (funded by NSF). Released strictly using the MIT license.
3. Sadeghi Tabas, S.*, Samadi S. 2019. A Web GIS Project Screening Tool (PST, ArcGIS API for JavaScript) for Environmental Assessment(funded by SCDOT).

Links