Scott Hamshaw, Ph.D., P.E.
Scott Hamshaw, Ph.D., P.E. is a Machine Learning Specialist in the Analysis & Prediction Branch of the USGS Water Resources Mission Area.
Scott Hamshaw received his Ph.D. in Civil & Environmental Engineering from the University of Vermont where he undertook mapping of watershed erosion and data-driven modeling of sediment transport in the Lake Champlain Basin. After completing his Ph.D., he was a postdoctoral associate and research assistant professor at the University of Vermont applying machine learning to water resource problems as well as teaching land surveying and mapping. Prior to his Ph.D., Scott worked as a consulting engineer in Burlington, VT practicing stormwater management, water system design, and site engineering. Scott joined the Analysis & Prediction Branch of the Water Resources Mission Area in 2021 and is based in Bristol, VT.
Scott’s interests are in the broad area of applied machine learning and water resource engineering with emphasis on using data-driven approaches to understand dynamics in streamflow and river water quality. Scott is active in national-scale research on applying deep learning methods to the prediction of water quantity and quality in rivers across the U.S.
Professional Experience
Machine Learning Specialist, U.S. Geological Survey, Water Mission Area - 2021 to present
Research Assistant Professor, Civil & Environmental Engineering, University of Vermont - 2019 to 2021
Post-Doctoral Associate, Vermont EPSCoR, University of Vermont - 2017 to 2019
Research Assistant, Civil & Environmental Engineering, University of Vermont - 2011 to 2017
Staff Engineer, Engineered Solutions, Inc., Burlington, VT - 2007 to 2010
Education and Certifications
Ph.D. Civil & Environmental Engineering, University of Vermont, 2018
P.E., State of Vermont, License No. 100919, 2015
M.S. Civil & Environmental Engineering, University of Vermont, 2014
B.A. Engineering, St. Michael's College, 2006
B.S. Civil Engineering, University of Vermont, 2006
Science and Products
Preliminary streamflow percentile predictions for ungaged areas of the Colorado River Basin, 1981-2020
Data-Driven Drought Prediction Project Model Outputs for Select Spatial Units within the Conterminous United States
Solute export patterns across the contiguous USA
Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations
Regional streamflow drought forecasting in the Colorado River Basin using Deep Neural Network models
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
surface-water-geospatial-data-assembly
Science and Products
Preliminary streamflow percentile predictions for ungaged areas of the Colorado River Basin, 1981-2020
Data-Driven Drought Prediction Project Model Outputs for Select Spatial Units within the Conterminous United States
Solute export patterns across the contiguous USA
Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations
Regional streamflow drought forecasting in the Colorado River Basin using Deep Neural Network models
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.