GregFurlich

  • Research Associate
  • SPACE DOMAIN AWARENESS

Dr. Greg Furlich is a Research Associate and the Space Domain Awareness Research Lead for the Center for National Security Initiatives (NSI) and faculty member of the University of Colorado Boulder. Received his PhD in Physics from the University of Utah in 2020 with a doctoral thesis focused on the ultraviolet remote sensing of ultra-high energy cosmic ray interactions within the atmosphere. Prior to NSI, worked as a research scientist at Lockheed Martin Space Systems with a focus on machine learning and algorithm development for a wide breadth of advanced programs and internal research and development (IRAD) projects. Was recognized at Lockheed Martin for innovative research and leading technical contributions. Research interests at NSI include sensors and sensing, data exploitation, and trusted autonomy for mission data processing in Space Domain Awareness and Missile Warning missions. Raised over $1.87 million awards in the past 3.5 years and was conferred a courtesy appointment with the Aerospace Engineering Sciences at Boulder.

Focus Area

Space Domain Awareness (SDA)

Education

PhD, Physics, University of Utah, 2020
MS, Physics, University of Utah, 2018
BS, Physics, Michigan Technological University, 2014

Professional Experience

2022 - Present, Research Associate, Center for National Security Initiatives, University of Colorado Boulder
2021 – 2022, Senior A/AI Research Engineer and Senior Research Scientist, Advanced Programs and Exploitations, Lockheed Martin Space Systems
2014 – 2020, Graduate Research Assistant, Telescope Array Cosmic Ray Observatory, Department of Physics and Astronomy, University of Utah

Awards

Recognized Technical Talent, Lockheed Martin, 2021
Departmental Scholar, Department of Physics, Michigan Technological University, 2013
Sigma Pi Sigma, Physics Honor Society, Inducted 2013
Michigan Space Grant Consortium, 2012

Research Interests

  • Sensors and Sensing:image collection, processing, and analysis over many spectral regimes (visible, infrared, ultraviolet, multispectral, hyperspectral); expeditionary low SWAP sensor systems for situational awareness
  • Mission Data Processing:signal, image, and video processing; feature extraction and exploitation; algorithm development for dim object detection and disparate data fusion for object tracking and state estimation; event detection, characterization, and typing; machine learning for automatic target recognition, stochastic processing modeling, anomaly detection, physics‐informed neural networks
  • Trusted Autonomy:autonomous decisions for proliferated distributed systems; battle management architectures; killchain or threat decomposition; course of action