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usgs_staff_profile:

 meta:
   status_code: 200
   timestamp: '2023-09-30T16:43:22.341883'
   url: https://www.usgs.gov/staff-profiles/scott-hamshaw
 profile:
   abstracts: []
   affiliations: []
   education:
   - 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
   email: shamshaw@usgs.gov
   expertise_terms:
   - Machine Learning
   - Surface Water
   - Hydrologic Modeling
   - Sediment Transport
   - Land Surveying
   - Data Science
   honors: []
   intro_statements:
   - Scott Hamshaw, Ph.D., P.E. is a Machine Learning Specialist in the Analysis
     & Prediction Branch of the USGS Water Resources Mission Area.
   name: Scott Hamshaw, Ph.D., P.E.
   name_qualifier: null
   orcid: 0000-0002-0583-4237
   organization_link: https://www.usgs.gov/mission-areas/water-resources
   organization_name: Water Resources Mission Area
   personal_statement: "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\u2019s 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
   title: Machine Learning Specialist