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Dr. Greg Furlich is a Research Associate and the Space Domain Awareness research lead for the Center for National Security Initiatives (NSI) at the University of Å·ÃÀ¿Ú±¬ÊÓƵ Boulder. Dr. Furlich received his PhD in Physics from the University of Utah in 2020. His doctoral thesis focused on the ultraviolet remote sensing of ultra-high energy cosmic ray interactions within the atmosphere. Prior to NSI, Dr. Furlich worked as a research scientist at Lockheed Martin Space Systems with a focus on machine learning, data exploitation, and algorithm development for a wide breadth of advanced programs and internal research and development (IRAD) projects.

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 Å·ÃÀ¿Ú±¬ÊÓƵ 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

Signal, image, and video processing; remote sensing with a variety of sensor types (imaging, radar, lidar, SAR) and spectral regions (visible, infrared, ultraviolet, microwave, multispectral, hyperspectral); algorithm development for dim target detection; feature extraction and exploitation; disparate data fusion for target tracking and state estimation; event detection, classification, and typing; machine learning for image classification, image segmentation, automatic target recognition, synthetic image generation, anomaly detection; physics-informed neural networks.