Vidya Samadi, Ph.D., M.ASCE

Assistant Professor of Water Resources Engineering
Director of Clemson Hydrosystem and Hydroinformatics Research (HHR) Group
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. 2009

M.S. (M.Engr.)
Water Engineering, University of Tehran, Iran. 2002

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)
-Stochastic Hydrology and Hydroinformatics (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 in Water Resources Engineering at Clemson University. Her research focuses on advancing the field of hydroinformatics and cyber-physical systems, specifically developing analytics and artificial intelligence (AI) computing systems for water system modeling including surface water informatics, irrigation hydrologic modeling, and water system management. Dr. Samadi was accepted into the 2024 cohort of NSF-Civil, Mechanical, and Manufacturing Innovation (CMMI)’s Game Changer Academies for Advancing Research Innovation. She received the 2024 Universities Council on Water Resources Mid Career Award for Applied Research for her research work on hydroinformatics and water system modeling. Dr. Samadi was honored with the American Society of Civil Engineers (ASCE) Technical Merit Award and the ASCE Outstanding Reviewer Award. She has served as Chair of the Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI) Informatics Committee and a Board Member of the International Environmental Modelling & Software Society.

Research Interests

Dr. Samadi's research focuses on hydroinformatics and cyber-physical modeling systems, an interdisciplinary approach combining water resources engineering, computer science, and data analytics. The goal is to leverage advanced modeling and computing tools to address problems and challenges associated with water resource systems. Much of her current work is focused on machine learning applications in water resources and built environment domains. Vidya’s research has been continuously supported by various NSF programs (CBET, CMMI, CISE, OAC, GEO) as well as by other agencies such as USGS, USDA, Savannah River National Lab, NOAA, and the Department of Transportation (DOT).

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. 2024. Fill-and-Spill: A Novel Deep Reinforcement Learning for Water Infrastructure Management and Control. ASCE Journal of Water Resources Planning and Management. Journal of Water Resources Planning and Management, 150(7), p.04024022.
2. Samadi V., Stephens, K., Hughes, A., Murray-Tuite, Pamela.2024. Challenges and Opportunities When Bringing Machines onto the Team: Human-AI Teaming and Flood Evacuation Decisions. Environmental Modelling & Software, p.105976.
3. Humaira, N.*, Samadi, S., Hubig, N. 2023. An end-to-end deep learning-based pipeline for real-time flood event classification and scene object detection from multimedia images. IEEE Access. 10.1109/ACCESS.2023.3321312.
4. 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.
5. 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.
6. 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.
7. 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
8. 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.
9. 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