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Summary:

-PhD Candidate in Marine Science with Dave Johnston in the Marine Robotics and Remote Sensing Center at Duke University
-Curriculum Vitae

Interests:

  • oceanographic remote sensing of physical-biological interactions
  • earth system science
  • machine learning approaches to remote sensing analysis
  • the intersection of ocean and planetary science
  • drones and autonomy for ocean science
  • being outside as much as possible

Name : Patrick Gray
Email : pgrayobx (at) gmail
Location : Durham, NC
LinkedIn : Profile
GitHub : Code

Work and Focus

I'm currently a PhD student with Dave Johnston in the Marine Robotics and Remote Sensing Lab at the Duke University Marine Laboratory and a Future Investigator in NASA Earth and Space Science and Technology (FINESST).

My doctoral research focuses on satellite and drone based remote sensing of oceans and coasts. My work aims to understand spatial and temporal variability of ocean biology and ecology. Working at the confluence of remote sensing, data science, and biological oceanography I lean heavily on machine learning and scientific computing tools for parsing large amounts of remotely sensed data and connecting satellite, drone, and in-situ oceanographic monitoring. I'm particularly interested in tools and research that will be relevant for Earth science and for exploring other bodies within our solar system.

Before joining the Duke Marine Lab, I worked at Harvard University as a research technician in the deep-sea focused Girguis Lab, served a year and a half as Chief Technology Officer at WayPaver Foundation, and spent time at Moon Express as a Software Engineer developing their ground data systems and engineering team tools. I graduated from the University of North Carolina as a Morehead-Cain Scholar with a degree in Computer Science where I started UNC Students for the Exploration and Development of Space and researched computer vision.

Open Source Coding Projects

Quick overview of open-source projects.

  • c

    Scientific Computing Template

    A stock scientific computing and deep learning environment for analysis of remote sensing data using docker conda, and most common tools for machine learning in remote sensing such as keras, scikit-learn, xarray, rasterio, etc.

  • b

    Deep Learning for Ecology

    Educational Resources on Neural Networks for Ecology and Remote Sensing.

  • f

    Open Geo Tutorial

    Tutorial of remote sensing and GIS methodologies using open source software in python.

  • m

    Automated Cetacean Photogrammetry

    Open source code and data for the manuscript "Drones and Convolutional Neural Networks Facilitate Automated and Accurate Cetacean Species Identification and Photogrammetry."

Publications

Summary of recent journal publications.