Alison Appling, PhD
Alison Appling, Ph.D., 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: Nutrient Prediction Innovation and Evaluation (NPIE)
- 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, Alison seeks modeling advances that bring together scientific knowledge and data-driven models, using approaches including process-guided deep learning and differentiable hydrology.
As a data scientist, Alison conducts analyses in ways that are reproducible, efficient, and transparent, and Alison has developed tools and workflows to support others in these goals.
In leadership roles, Alison plans projects and organizes teams to deliver relevant, timely, high-quality products; challenges individuals to excel in their projects and careers; and coordinates across projects to realize the Water Mission Area’s vision of fit-for-purpose, integrated tools for modeling water quantity and quality across the nation.
Alison is a member of the Analysis and Prediction Branch in the Integrated Modeling and Prediction Division in the Water Resources Mission Area. Alison 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.
Science and Products
Overcoming equifinality: Leveraging long time series for stream metabolism estimation Overcoming equifinality: Leveraging long time series for stream metabolism estimation
The metabolic regimes of flowing waters The metabolic regimes of flowing waters
sbtools: A package connecting R to cloud-based data for collaborative online research sbtools: A package connecting R to cloud-based data for collaborative online research
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.
Science and Products
Overcoming equifinality: Leveraging long time series for stream metabolism estimation Overcoming equifinality: Leveraging long time series for stream metabolism estimation
The metabolic regimes of flowing waters The metabolic regimes of flowing waters
sbtools: A package connecting R to cloud-based data for collaborative online research sbtools: A package connecting R to cloud-based data for collaborative online research
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.