Dr. Uttam K. Majumder received a Ph.D. in electrical engineering from Purdue University, an M.S. in electrical engineering from the Air Force Institute of Technology, and a B.S. in computer science from the City College of New York, CUNY. Dr. Majumder is senior electronics engineer at U.S. Air Force Research Laboratory (AFRL). His research interests include artificial intelligence / machine learning (AI/ML), synthetic aperture radar (SAR) algorithm development for surveillance applications, radar waveforms design, and high-performance computing for SAR based automatic target recognition (ATR). His dissertation research was focused on “Nearly Orthogonal, Doppler Tolerant Waveforms and Signal Processing for Multi-Mode Radar Applications” and he submitted a patent for this innovation. In addition to publishing more than 40 articles with IEEE, SPIE, and other professional societies, Dr. Majumder recently (July 2020) published a book on “Deep Learning for Radar and Communications Automatic Target Recognition.” Dr. Majumder is a senior member and distinguished lecturer of IEEE.
Podcasts / Webinars
This webinar presents modern deep learning (DL) techniques for radio frequency (RF) imagery and signals (i.e., Synthetic Aperture Radar/SAR data, communication signals) classification. First, Dr. Majumder provides a short overview of machine learning (ML) /DL theory and an understanding of SAR imagery and RF signals. Then he demonstrates detailed algorithmic implementation and performance of DL algorithms on classifying SAR data and RF signals. Dr. Majumder presents recent research results, technical challenges, and directions of DL-based object classification for RF sensing. Finally, he covers adversarial attacks and mitigation techniques involving DL-based RF object recognition.