Dr. Smith is a journeyman physicist with the good fortune to have participated in outstanding teams covering unusually wide range of technical subjects. His academic career began in experimental high-energy physics. His inability to deny the condensed-matter siren’s song led to a number of technical roles in the semiconductor industry, the last of which was designing nonlinear, quasi-optical, low-power plasmonic devices for RF/MMW/THz IoT leaf-node applications. He began work on a physics-based theory of current (un)supervised learning algorithms in 2016 in order to robustly apply “more than Moore” principles for next-generation hardware designs. Dr. Smith earned a B.S. degree in Physics/Mathematics from Texas A&I University in Kingsville, TX. He earned M.S. (Physics) and a Ph.D. (Applied Physics) degrees from Texas A&M University in College Station, TX.
AI/Machine Learning/Deep Learning
Performance Analysis and Optimization