Item talk:Q49376

ORCID:

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     name: Deep learning of estuary salinity dynamics is physically accurate at a
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     name: "Considering Uncertainty of Historical Ice Jam Flood Records in a Bayesian\
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     name: "Discussion of \u201Cfrequency of ice-jam flooding of peace-athabasca\
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     name: Geothermal energy characterization in the Appalachian Basin of New York
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   addressCountry: US
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   name: United States Geological Survey
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   alternateName: Engineering Systems and Environment
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   name: University of Virginia
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   alternateName: Civil and Environmental Engineering
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   name: Clarkson University
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   name: Cornell University
 familyName: Smith
 givenName: Jared
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 name: Jared D. Smith

USGS Staff Profile:

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   abstract: Machine Learning Specialist with the Water Resources Mission Area
   additionalType: short description
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   abstract: Jared D. Smith, Ph.D., is a Machine Learning Specialist in the USGS
     Water Resources Mission Area. He is based in Reston, VA.
   additionalType: staff profile page introductory statement
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   abstract: Jared has a background in environmental systems engineering, spatial
     data analysis, and statistics. His previous research has coupled physical and
     mathematical models with statistical analyses to inform planning and management
     decisions for environmental, earth-energy, and water resources systems. Jared
     joined the USGS in 2021 after completing a postdoc at The University of Virginia,
     where he developed green infrastructure portfolio optimizations that were designed
     to be robust to Bayesian-estimated parametric uncertainty of a watershed model.
     Jared completed his Ph.D. at Cornell University, where his research addressed
     Appalachian Basin geothermal resource assessment and subsequent uncertainty
     assessments for deep geothermal district heating projects at the Cornell and
     West Virginia University campuses. Previous work has also addressed ice jam
     flood frequency analysis under climate change, applied to the Peace-Athabasca
     Delta in Canada.
   additionalType: personal statement
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   abstract: "\u200BCluster Analysis and Prediction of Flood Flow Metrics for Minimally\
     \ Altered Catchments in the Conterminous United States, HydroML Symposium 2022"
   additionalType: staff profile page abstract
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   abstract: Discovering Flood Regions and Predicting Flood Flow Metrics to Inform
     Bridge Scour Studies in the Conterminous United States, National Hydraulic Engineering
     Conference, 2022
   additionalType: staff profile page abstract
 email: jsmith@usgs.gov
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   name: "\u200BPh.D. Environmental and Water Resources Systems Engineering, Cornell\
     \ University, 2019"
 - '@type': EducationalOccupationalCredential
   name: M.S. Environmental and Water Resources Systems Engineering, Cornell University,
     2016
 - '@type': EducationalOccupationalCredential
   name: B.S. Environmental Engineering, Clarkson University, 2013
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     name: Water Resources Mission Area
     url: https://www.usgs.gov/mission-areas/water-resources
   roleName: Machine Learning Specialist
   startDate: '2024-05-12T15:17:41.378526'
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 jobTitle: Machine Learning Specialist
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   additionalType: self-claimed expertise
   name: Model Diagnostics
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   additionalType: self-claimed expertise
   name: Machine Learning
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   additionalType: self-claimed expertise
   name: Hydrologic Statistics
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   additionalType: self-claimed expertise
   name: Optimization
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   additionalType: self-claimed expertise
   name: Water Resource Management
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   additionalType: self-claimed expertise
   name: Surface Water
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   additionalType: self-claimed expertise
   name: Geothermal Resource Assessment
 - '@type': Thing
   additionalType: self-claimed expertise
   name: Heat Flow
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     name: U.S. Geological Survey
   name: staff member
   startDate: '2024-05-12T15:17:41.376020'
 name: Jared Smith
 url: https://www.usgs.gov/staff-profiles/jared-smith