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Aaron Murphy

I am a Physical Scientist at UMESC involved with projects to automate the detection of wildlife in imagery using machine learning, to expand and refine databases for invasive carp monitoring, and to collect data using uncrewed aerial systems (UAS). I also have experience in ecological modeling, GIS, and conservation biology. 

My professional background includes work with invasive species at the Bureau of Reclamation and Colorado Parks and Wildlife.

Aaron Murphy conducts studies on:

  • Machine Learning
  • Database Development
  • Uncrewed Aerial Systems
  • Invasive Species

Professional Experience

  • 2023 – Present: Physical Scientist, USGS Upper Midwest Environmental Sciences Center (La Crosse, Wisconsin)

  • 2018 – 2023: Ecologist, Bureau of Reclamation, Technical Services Center, Ecological Research Lab (Denver, Colorado)

  • 2017 – 2018: Aquatic Nuisance Species Monitoring Lead, Colorado Parks and Wildlife (Denver, Colorado)

  • 2003 – 2006: Space Vehicles Engineer/Lieutenant, US Air Force Research Laboratory (Albuquerque, New Mexico)

Education and Certifications

  • M.S. – Conservation Technology, Florida Institute of Technology, 2016

  • B.S. – Conservation Biology & Ecology, Florida Institute of Technology, 2016

  • B.S. – Aeronautical & Astronautical Engineering, Purdue University, 2006

Science and Products

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An aerial image of a flock of waterfowl floating on the ocean.

Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery

The U.S. Geological Survey Upper Midwest Environmental Sciences Center is developing deep learning algorithms and tools for the automatic detection, enumeration, classification, and annotation of seabirds and other marine wildlife from digital aerial imagery — advancing cutting-edge research in collaboration with the Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (FWS...
By
Ecosystems Mission Area, Species Management Research Program, Upper Midwest Environmental Sciences Center
link

Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery

The U.S. Geological Survey Upper Midwest Environmental Sciences Center is developing deep learning algorithms and tools for the automatic detection, enumeration, classification, and annotation of seabirds and other marine wildlife from digital aerial imagery — advancing cutting-edge research in collaboration with the Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (FWS...
Learn More

Science and Products

link
An aerial image of a flock of waterfowl floating on the ocean.

Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery

The U.S. Geological Survey Upper Midwest Environmental Sciences Center is developing deep learning algorithms and tools for the automatic detection, enumeration, classification, and annotation of seabirds and other marine wildlife from digital aerial imagery — advancing cutting-edge research in collaboration with the Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (FWS...
By
Ecosystems Mission Area, Species Management Research Program, Upper Midwest Environmental Sciences Center
link

Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery

The U.S. Geological Survey Upper Midwest Environmental Sciences Center is developing deep learning algorithms and tools for the automatic detection, enumeration, classification, and annotation of seabirds and other marine wildlife from digital aerial imagery — advancing cutting-edge research in collaboration with the Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (FWS...
Learn More