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Mr. David Martinez

David R. Martinez is a Lincoln Laboratory Fellow at MIT Lincoln Laboratory. In this capacity, he is focusing on research and technical directions in the areas of artificial intelligence, high performance computing and digital transformation. He is also dedicated to AI teaching within MIT, MIT Lincoln Laboratory and to industry and government organizations.

He has been a keynote speaker at both national and international conferences. He co-authored-co-edited the book titled High Performance Embedded Computing Handbook: A Systems Perspective. He was elected IEEE Fellow for “technical leadership in the development of high performance embedded computing for real-time defense systems.” He holds three US patents based on his work in signal processing for seismic applications.

Mr. Martinez was awarded a bachelor’s degree from New Mexico State University, an MS degree from MIT and the EE degree jointly from MIT and the Woods Hole Oceanographic Institution in Electrical and Oceanographic Engineering. He completed an MBA from Southern Methodist University.

Courses taught by Mr. David Martinez

Artificial Intelligence Foundations

Artificial Intelligence Foundations

Self-paced

The course begins with a brief history of Artificial Intelligence (AI), including a survey of representative AI success stories and covers topics such as AI data requirements and conditioning, a selection of AI techniques including supervised learning, unsupervised learning and . . .

  • Free
  • Apr 06, 2022
  • Self-paced
  • Dr. Sarah Mcguire, Dr. Julie Mullen, Ms. Lauren Milechin, Dr. Vijay Gadepally, Dr. Charlie Dagli, Dr. Mykel Kochenderfer, Dr. Olga Simek, Dr. Rajmonda Caceres, Dr. Ryan Soklaski, Mr. David Martinez
  • Duration: Not Defined
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Artificial Intelligence Foundations

The course begins with a brief history of Artificial Intelligence (AI), including a survey of representative AI success stories and covers topics such as AI data requirements and conditioning, a selection of AI techniques including supervised learning, unsupervised learning and . . .

Duration: Not Defined Read More