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Design Develop and Deliver ML and ISR for the IC
Follow my publications at: https://scholar.google.com/citations?user=_A4fjOQAAAAJ&hl=en&oi=sra System Engineering: Transitioning ISR capabilities to operations * Created NGA’s Automating Automation research umbrella to scale the enterprise to 1000s of automated tasks. Identified research areas that dramatically increase the intelligence yield. * Led the integration of hardware/software Vision Based Navigation system to land humans on the moon * (Team) Built a closed loop tasking-collection-dissemination system and transitioned to operations. Created faster than real-time simulation using components to test workflows, components, and metrics. * Designed a human readable, machine-to-machine message structure to request ISR collection. The message structure was adopted operationally by multiple agencies. * Created an inter-agency coalition to fund multiple Wide Area Motion Imagery (WAMI) and Hyperspectral Imagery collections. Images are still used to test and develop machine learning systems. * Delivered an operational Airborne Wide Area Motion Imagery (WAMI) system to theater in 9 months * Managed 18 concurrent Army ISR airborne sensors to integrate with tactical and strategic users Improve machine learning effectiveness * Designed a machine learning (ML) National Imagery Interpretability Rating Scale (NIIRS) for EO and SAR. ML NIIRS enables enterprise automation, resilient autonomy, task specific collection, triage, and improve ML model creation. The ML NIIRS is expected to reduce manual processing by 80%. * Identified adversarial attack indicators and entry points across machine learning applications. Demonstrated low-cost counters that reduced attack identification times by half. * Designed on-the-fly training, during UAV transit to operational area, that addresses variable lighting across the UAV mission set. UAV pilots saw improved target
University of Otago
PhD, Information Science
January 1, 1994 – January 1, 1999
State University of New York College of Environmental Science and Forestry
Master of Science - MS, Photogrammetry and Remote Sensing
January 1, 1987 – January 1, 1991
State University of New York College of Environmental Science and Forestry
Bachelor of Science - BS, Environmental and Resource Engineering
January 1, 1985 – January 1, 1987
WiSC Enterprises, LLC
Program Manager
January 1, 2026 – Present
Draper
Research Scientist (IPA at NGA)
February 1, 2023 – March 1, 2026
Draper
Reston, Virginia, Lead for the Machine Intelligence Group
December 1, 2019 – March 1, 2026
Raytheon
Machine Learning Scientist
March 1, 2014 – December 1, 2019
Chantilly, Virginia
The McKenna Principals, Inc.
Chief Scientist
January 1, 2013 – January 1, 2014
Northern Virginia
Integrity Applications Incorporated
Principal Scientist
January 1, 2010 – January 1, 2013
Arlington, Virginia
SAIC
Senior Scientist
January 1, 2000 – March 1, 2010
University of Otago
Senior Lecturer
January 1, 1994 – January 1, 1999
Lockheed Engineering and Science Corporation / Hernandez Engineering
Scientist
January 1, 1990 – January 1, 1994
On-site
Cultural Fit Analysis
The candidate's career trajectory is heavily focused on scientific research, machine learning, and program management within defense and intelligence. While this demonstrates a strong capacity for complex technical work, the direct alignment with a Quality Assurance Engineer role is not immediately apparent. The projects and roles described emphasize scientific exploration, system design, and data exploitation rather than the systematic testing, quality assurance methodologies, and defect management typically associated with QA. The breadth of skills is high in scientific and engineering domains, but specific QA-centric skills are not highlighted, which may indicate a cultural fit challenge for a dedicated QA role.
Soft Skills & Operational Fit
The candidate's extensive experience in leading research and program management roles suggests strong leadership, problem-solving, and strategic thinking abilities. Their background in complex defense and intelligence projects indicates a capacity for handling high-pressure environments and collaborating within specialized teams. However, the resume does not explicitly detail soft skills relevant to a Quality Assurance Engineer role, such as meticulous attention to detail in testing, collaborative communication with development teams, or advocacy for quality processes.