High Performance Embedded Computing Handbook (PDF)

David R. Martinez is Laboratory Fellow at MIT Lincoln Laboratory and MIT Lead Instructor. His emphasis is on artificial intelligence, high performance computing, and technical leadership. Prior to this appointment, he was Associate Division Head, and a member of the Laboratory's Steering Committee.

He is the MIT Lead Lecturer for the course titled: "AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment." The course is based on his former graduate engineering course taught at MIT that instructed students on techniques to formulating an AI strategic roadmap, starting from AI architecture principles, leading to concept prototyping, and deployment of end-to-end AI system capabilities, including hands-on experiential learning leveraging a single-board computer. The course also addressed responsible AI, and the leadership of multi-disciplinary teams.

Mr. Martinez has developed and led complex prototype systems, from their inception to their final deployments. The system demonstrations operated in real-time, leveraging adaptive signal processing and high performance embedded computing. These successful prototype demonstrations served as the pathfinder for industry to later commercialize.

He has been a keynote speaker at both national and international conferences. He was elected IEEE Fellow "for technical leadership in the development of high performance embedded computing for real-time defense systems." He holds three U.S. patents based on his work in signal processing for seismic applications. He received the special achievement award from ARCO Oil and Gas Research Center.

He established and chaired workshops on Robustness of AI Systems and Artificial Intelligence for Cyber Security. He was awarded the Eminent Engineer Award from the College of Engineering at NMSU, and was elected to the NMSU Klipsch Electrical and Computer Engineering Academy.

Mr. Martinez was awarded a BS degree from New

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