F1: ML 101 Machine Learning Fundamentals for Position, Navigation, and Timing

Time:
Monday, June 1, 8:30 a.m. - 10:00 a.m.
Location: Meeting Room 1-3

This tutorial delivers practical ML foundations for PNT applications in contested environments. Moving beyond algorithmic theory, the course addresses why most ML deployments fail: poor data strategy and misaligned development practices. We examine the complete development lifecycle with specific focus on encapsulating PNT domain expertise and integrating ML into the OODA loop as an operational force multiplier.

A central theme is data as the foundation of performance. We challenge reliance on purely simulated datasets, exploring strategic real-world collection, high-value dataset characteristics, and common development pitfalls with concrete avoidance strategies.

The curriculum employs a government-ready toolchain, including data collection systems, reference algorithms with proven performance in contested environments, standardized development frameworks, and vetted datasets. Through hands-on experience with tools designed for PNT-specific challenges, participants transform from ML solution consumers into informed architects capable of strategically evaluating, deploying, and enhancing these systems where navigation warfare meets operational necessity.

Renee Yazdi is the deputy lead engineer for Canyon, where she primarily supports Air Force Research Laboratory (AFRL) PNT projects, SWAC, SDUE, and Internal Research and Development (IR&D) efforts. With 30 years of experience, she has extensive expertise in the space domain, contributing to a diverse range of systems engineering and technology development initiatives. Her technical expertise includes remote sensing, communications, missile systems, and GPS/GNSS technologies. Ms. Yazdi holds a MS in EE Cal Poly, as well as a certificate in Artificial Intelligence and Machine Learning from Caltech.

Brian Zufelt is the deputy program manager for PNT programs at Canyon with over 18 years of experience in embedded systems, computer vision, and machine learning. He holds an MS in Computer Engineering from UNM and previously led ML platform development for GPS threat detection systems at COSMIAC, supporting AFRL, NASA, and DoD partners.