Applications of Artificial Intelligence/Machine Learning in RF/Microwave Design and Signal Processing
Wednesday, 19 January 2022
Time: 09:45-11:30 AM Eastern Time (Q&A session: 11-11:30 AM)
Moderator/Organizer: Pushkar Kulkarni, Qualcomm
Advancements in algorithms and technology are driving use of artificial intelligence (AI)/machine learning (ML) in abundance in practical applications. At Radio Wireless Week (RWW) 2022 however, we will be focusing on how AI/ML techniques are becoming increasingly popular in RF/Microwave System Design and Signal Processing. Recognized experts from both academia and industry have been invited to present fresh ideas and new concepts to the curious minds. Mark your calendar, tell your friends, and join the YP session to learn about these emerging, exciting topics and opportunities that lie ahead.
A Signal Processing Perspective on Modern Machine Learning and Neural networks
Professor Mert Pilanci, Stanford University
Intelligent RF System Design using Artificial Intelligence
Rick Gentile, Mathworks
Machine Learning for Automotive RADAR Detection
Shawn Carpenter and Dr. Ushemadzoro Chipengo, Ansys
Machine Learning for Wireless Communications
Dr. Taesang Yoo, Qualcomm
Professor Mert Pilanci, Stanford University : “A signal processing perspective on modern machine learning and neural networks”
Mert Pilanci is an assistant professor of Electrical Engineering at Stanford University. He received his Ph.D. in Electrical Engineering and Computer Science from UC Berkeley in 2016. Prior to joining Stanford, he was an assistant professor of Electrical Engineering and Computer Science at the University of Michigan. In 2017, he was a Math+X postdoctoral fellow working with Emmanuel Candès at Stanford University. His research interests are in large scale machine learning, optimization, and information theory.
Rick Gentile, Mathworks : “Intelligent RF System Design using Artificial Intelligence”
Rick Gentile works at MathWorks where he is focused on tools that support radar, EW, and wireless communications applications. Prior to joining MathWorks, Rick was a Radar Systems Engineer at MITRE and MIT Lincoln Laboratory, where he worked on the development of many large radar systems. His focus was on signal processing and system integration. Rick also was a DSP Applications Engineer at Analog Devices where he led embedded processor and system level architecture definitions for high performance signal processing systems. He received a B.S. in Electrical and Computer Engineering from the University of Massachusetts, Amherst and an M.S. in Electrical and Computer Engineering from Northeastern University, where his focus areas of study included Microwave Engineering, Communications and Signal Processing.
Shawn Carpenter, Ansys : “Machine Learning for Automotive RADAR Detection”
Shawn Carpenter received his BEE degree in electrical engineering from the University of Minnesota Institute of Technology in 1988, and an MSEE in Electrical Engineering from Syracuse University in 1991 concurrently with the General Electric Thomas Edison Advanced Course in Engineering program. He has served as a Senior Microwave Engineer in Module Design and Array Technology for the GE Aerospace Electronics Laboratory (Syracuse, NY), VP Sales and Marketing for Sonnet Software, Inc., and Director, Sales & Marketing for Delcross Technologies.
Shawn joined Ansys Inc. during the 2015 acquisition of Delcross Technologies, and is currently a Program Director for 5G and Space applications. His current interests include phased array modeling techniques for MIMO and adaptive beamforming, installed antenna-host interactions, mm-wave radar sensor modeling and physical channel modeling for electrically large environments.