Dave Cook
Founder and CEO of The Training Data Project
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Dave Cook is an experienced technology leader with over 25 years of expertise in advanced analytics, artificial intelligence (AI), and machine learning (ML). He currently supports AI/ML programs in the Defense Department and Intelligence Community and teaches at the University of Maryland in Geospatial Intelligence and AI.
In 2023, Dave founded the Training Data Project (TDP), a non-profit organization that promotes trust in AI data in the public sector through open-source tools, guidance, governance, and standards. He believes the data that fuels AI/ML must reflect values such as integrity and accountability to build trustworthy human-machine teaming for all.
Throughout his career, Dave has focused on solving complex data and analytic challenges in different fields, including law enforcement, intelligence, defense, health, and leading corporations worldwide. He believes scalable data infrastructure is crucial for gaining insights through new artificial intelligence and machine learning applications. An 18-time Marine Corps Marathon finisher, he is adamant that AI/ML is definitely more of a marathon than a sprint.
Dave has a Master of Science in Geospatial Intelligence from the University of Maryland, a Master of Science in Policy Analysis and Management Information Systems from Carnegie Mellon University- Heinz College, and a Bachelor of Arts in American History and Political Science from Northwestern University. He is an Advisory Board member for Quantic.edu and Capitol Technology University in Software Engineering, and is an Honorary Advisory Board Member for the Capitol Technology University AI Center of Excellence.
How Much Is Enough: Streaming Data, Strategic Sufficiency, and the Platform for the War We Can Win
This session introduces a real-time decision framework for strategic planning under constraints. We present the Cognitive Mission Alignment Model (CMAM) and its operational engine, the Cognitive Vector Analytics Platform (CVAP), which uses streaming data and symbolic modeling to continuously assess posture and generate decision-ready options.
CMAM represents posture as cognitive vectors—mathematical structures encoding capability, cost, latency, resilience, and risk. Structured and unstructured data, including telemetry, policy documents, and intelligence feeds, are transformed into a common vector format, enabling live comparisons and updates as conditions evolve.
CVAP identifies posture drift, evaluates alignment, and produces structured options for force design, investment, and prioritization. Agentic AI, as described in “The Era of Experience,” allows the system to learn continuously and recompute sufficiency as missions and constraints shift.
Through a Western Pacific scenario, attendees will see how posture is continuously recalculated as missions evolve and constraints shift. The system is designed to reflect what Charles Hitch called the requirement that “defense decisions cannot be divorced, in peace or war, from political, economic, and technological considerations.” This is not a dashboard; it is a decision engine for coherent, adaptive strategic governance.
This session is designed for the technical and the non-technical.
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