BEGIN:VCALENDAR CALSCALE:GREGORIAN VERSION:2.0 METHOD:PUBLISH PRODID:-//Drupal iCal API//EN X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU DTSTART:20070311T020000 TZNAME:EDT TZOFFSETTO:-0400 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU DTSTART:20071104T020000 TZNAME:EST TZOFFSETTO:-0500 END:STANDARD END:VTIMEZONE BEGIN:VEVENT SEQUENCE:1 X-APPLE-TRAVEL-ADVISORY-BEHAVIOR:AUTOMATIC 234231 20260406T102546Z DTSTART;TZID=America/New_York:20260417T150000 DTEND;TZID=America/New_York:2 0260417T155000 URL;TYPE=URI:https://www.wpi.edu/news/calendar/events/ece-g raduate-seminar-lecture-series-speaker-scott-kuzdeba-bae-systems-fast-labs tm ECE Graduate Seminar Lecture Series, Speaker: Scott Kuzdeba, BAE Systems FA ST Labs™ Title:\nRadio Frequency Machine Learning – Building Latent Representation s from Signals\nAbstract:\nRadio frequency (RF) machine learning (ML) cont inues to expand its use in communication applications. This has largely fo llowed the same trend as other domains, with initial deployment of supervi sed machine learning techniques taking over the role of traditional method s, followed by expansion into novel uses and generative AI. This talk walk s through the emergence of deep learning approaches to the RF domain, help ing to shed some light on methods that are now well established in the fie ld. The tutorial will largely focus on the physical layer (wireless signal s), looking at how the various types of RF ML can be applied to communicat ions waveforms. Emphasis will be given on what it means to learn in this c ontext as well as representation learning, which is framed as the core com ponent to supervised, unsupervised, and generative AI. Throughout, applica tion and domain perspectives will be shared.\n\n\nImage\n \n\n\n\n\nSpeak er:\nScott Kuzdeba\nChief Scientist and Director of the AI-enabled RF \& C yber Group, BAE Systems FAST Labs™\nBio:\nDr. Scott Kuzdeba is a Chief S cientist and Director of the AI-enabled RF \& Cyber group within FAST Labs ™, the research \& development branch of BAE Systems, Inc. He is heavily involved in the technical oversight and development of Artificial Intelli gence (AI) and Machine Learning (ML) efforts, with heavy emphasis in the r adio frequency (RF) domain. His expertise includes determining how to comb ine domain phenomenology with machine learning, with particular focus on d eveloping machine learning solutions to RF applications, including communi cations systems, radars, EW, and SIGINT. Dr. Kuzdeba recently published a book on this subject – Radio Frequency Machine Learning: A Deep Learning Perspective (Artech House, 2025). He has vast experience working R\&D pro grams for the government S\&T community and helping to bridge the gap to p rograms of record.Dr. Kuzdeba holds a PhD in computational neuroscience fr om Boston University, where he studied how human intelligence and communic ation systems are formed.\n\nHost: Professor Alex Wyglinski\n END:VEVENT END:VCALENDAR