David R. Martinez

Associate Division Head in the Cyber Security and Information Sciences Division | MIT Lincoln Laboratory

Lecture Information:
  • November 29, 2018
  • 1:00 PM
  • ECS 241
david martinez photo

Speaker Bio

Mr. David Martinez is Associate Division Head in the Cyber Security and Information Sciences Division at MIT Lincoln Laboratory. Areas of expertise are leadership in cybersecurity, analytics, artificial intelligence, and high-performance computing. Mr. Martinez received his B.S. from New Mexico State University (NMSU), and his M.S. degree from MIT, and the E.E. degree in Electrical and Oceanographic Engineering jointly from MIT and the Woods Hole Oceanographic Institution. He completed an M.B.A. from SMU. He was elected IEEE Fellow “for technical leadership in the development of high performance embedded computing for real-time defense systems.” He was awarded the Eminent Engineer Award from the College of Engineering at NMSU. He was elected to the NMSU Klipsch Electrical and Computer Engineering Academy. He is a member of the Dean’s of Engineering Council at NMSU and the Advisory Board in the School of Computing and Information Sciences at the Florida International University. Mr. Martinez is a member of MIT/LL Steering Committee. He served on the Army Science Board. He co-authored the book titled: “High Performance Embedded Computing, A Systems Perspective,” CRC, 2008. He was born in El Paso, TX. He is fluent in Spanish and an avid golfer, saltwater fisherman, and outdoorsman.


This presentation addresses an AI canonical architecture suitable for a number of different classes of applications. Several examples will be shown focused on cybersecurity and potential vulnerabilities to adversarial attacks. One critical element of the end-to-end AI architecture is the need for robust AI. Significant advances have been made in AI algorithms and high performance computing. However, additional advancements in science and technology (S&T) are needed to validate the performance of AI systems. This performance assessment is very critical because AI systems are very brittle to adversarial modifications to the system. The AI canonical architecture starts with data conditioning, followed by classes of machine learning algorithms, human-machine teaming, modern computing, and robust AI. We will briefly address each of these areas. The presentation concludes with a summary of S&T challenges and recommendations.