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
Predicting water temperature in the Delaware River Basin
Data release: Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes
Data release: Process-based predictions of lake water temperature in the Midwest US
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
Metabolism estimates for 356 U.S. rivers (2007-2017)
Multi-task deep learning of daily streamflow and water temperature
Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory
Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature
Machine learning for understanding inland water quantity, quality, and ecology
Physics-guided machine learning from simulation data: An application in modeling lake and river systems
Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Physics-guided recurrent graph model for predicting flow and temperature in river networks
Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning
Graph-based reinforcement learning for active learning in real time: An application in modeling river networks
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
Heterogeneous stream-reservoir graph networks with data assimilation
Partial differential equation driven dynamic graph networks for predicting stream water temperature
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
Predicting water temperature in the Delaware River Basin
Data release: Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes
Data release: Process-based predictions of lake water temperature in the Midwest US
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
Metabolism estimates for 356 U.S. rivers (2007-2017)
Multi-task deep learning of daily streamflow and water temperature
Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory
Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature
Machine learning for understanding inland water quantity, quality, and ecology
Physics-guided machine learning from simulation data: An application in modeling lake and river systems
Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Physics-guided recurrent graph model for predicting flow and temperature in river networks
Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning
Graph-based reinforcement learning for active learning in real time: An application in modeling river networks
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
Heterogeneous stream-reservoir graph networks with data assimilation
Partial differential equation driven dynamic graph networks for predicting stream water temperature
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.