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Alison Appling, PhD

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. 

Current Roles

  • Project Manager: Predictive Understanding of Multiscale Processes (PUMP)
  • Task Lead: Advancing Machine Learning and Data Assimilation, within the PUMP Project

Alison 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. “Process-guided deep learning” and “differentiable hydrology” 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’s 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).

Professional Experience

  • Development Ecologist and Data Scientist, U.S. Geological Survey, 2019-Present 

  • Ecologist, U.S. Geological Survey, 2016-2019 

  • 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 

  • Postdoctoral Associate, University of New Hampshire. Mentor: W. H. McDowell, 2013-2015 

  • Postdoctoral Associate, Duke University. Mentor: J. B. Heffernan, 2012-2013 

  • Ph.D. Student and Teaching Assistant: Organismal Diversity, Aquatic Field Ecology, and General Microbiology, University Program in Ecology, Duke University, 2006-2012 

  • Research Technician, Stanford University & Carnegie Institution of Washington, 2004-2006 

  • Undergraduate Teaching Assistant: Programming Paradigms and Discrete Mathematics, Computer Science, Stanford University, 2001-2003 

Education and Certifications

  • Ph.D. Ecology, 2012. Duke University, Durham, NC. 

    Connectivity Drives Function: Carbon and Nitrogen Dynamics in a Floodplain-Aquifer Ecosystem. Advisors: E. S. Bernhardt and R. B. Jackson

  • B.S. Symbolic Systems, 2004. Stanford University, Stanford, CA. 

    Coursework in computer science, decision analysis, logic, linguistics, and psychology.

Science and Products