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ORCID:
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  "USGS Staff Profile": {
  '@id': https://orcid.org/0000-0003-3638-8572
    "_id": "https://www.usgs.gov/staff-profiles/alison-appling",
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    creator:
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      name: Deep learning of estuary salinity dynamics is physically accurate at a
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        fraction of hydrodynamic model computational cost
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    - '@id': https://doi.org/10.1038/s44221-024-00202-z
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      name: Deep learning for water quality
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          "abstract": "Alison Appling, Ph.D., (she/her) is a data scientist and ecologist who applies machine learning and other data-driven methods to predict and understand water resources dynamics.",
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          "abstract": "Current RolesProject Manager: Predictive Understanding of Multiscale Processes (PUMP)Task Lead: Advancing Machine Learning and Data Assimilation, within the PUMP ProjectAlison studies the movement of energy, carbon, and nutrients through rivers, lakes, and floodplains to better predict and understand variations in water quality over space and time.As a machine learning modeler and biogeochemist, she seeks modeling advances that bring together scientific knowledge and data-driven models. \u201cProcess-guided deep learning\u201d and \u201cdifferentiable hydrology\u201d are two approaches on which she collaborates.As a data scientist, she conducts analyses in ways that are reproducible, efficient, and transparent, and she has developed tools and workflows to support others in these goals.In her leadership roles, she facilitates fluid skill sharing within teams and communities of practice, challenges individuals to excel in their projects and careers, and coordinates across projects to realize the Water Mission Area\u2019s vision of broadly reusable, integrated tools for predicting water quantity and quality across the nation.Alison is based in State College, PA, and is a member of the Analysis and Prediction Branch in the Integrated Modeling and Prediction Division in the Water Mission Area. She is on the USGS career track called Equipment Development Grade Evaluation (EDGE).",
      name: Identifying Structural Priors in a Hybrid Differentiable Model for Stream
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        Water Temperature Modeling
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          "name": "Ph.D. Ecology, 2012. Duke University, Durham, NC. \nConnectivity Drives Function: Carbon and Nitrogen Dynamics in a Floodplain-Aquifer Ecosystem. Advisors: E. S. Bernhardt and R. B. Jackson"
      name: "Train, Inform, Borrow, or Combine? Approaches to Process\u2010Guided\
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        \ Deep Learning for Groundwater\u2010Influenced Stream Temperature Prediction"
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          "name": "B.S. Symbolic Systems, 2004. Stanford University, Stanford, CA. \nCoursework in computer science, decision analysis, logic, linguistics, and psychology."
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      name: Differentiable modelling to unify machine learning and physical models
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      name: "Near\u2010term forecasts of stream temperature using deep learning and\
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        \ data assimilation in support of management decisions"
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          "name": "Development Ecologist and Data Scientist, U.S. Geological Survey, 2019-Present"
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      name: 'Stream Temperature Prediction in a Shifting Environment: Explaining the
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        Influence of Deep Learning Architecture'
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          "name": "Postdoctoral Fellow, USGS Powell Center and University of Wisconsin-Madison. Mentors: E. H. Stanley, J. S. Read, E. G. Stets, and R. O. Hall, 2015-2016"
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      name: Machine learning for understanding inland water quantity, quality, and
        ecology
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Retrieved from "https://geokb.wikibase.cloud/wiki/Special:MobileDiff/1128111"