BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Denver BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0700 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:MDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0600 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:MST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260413T174238Z UID:D09F139A-BE77-438B-BDD6-9C617868BBA8 DTSTART;TZID=America/Denver:20260506T103000 DTEND;TZID=America/Denver:20260506T120000 DESCRIPTION:Abstract: The unprecedented scale\, heterogeneity\, and perform ance requirements of 6G networks fundamentally challenge traditional optim ization-centric approaches to resource management\, motivating a paradigm shift toward artificial intelligence (AI)–driven methodologies. This lec ture examines how deep unsupervised learning and generative AI techniques can be leveraged to solve both convex and non-convex network resource allo cation problems under complex\, nonlinear constraints. Particular emphasis is placed on deep unsupervised learning frameworks\, deep implicit layers \, and differentiable projection methods that enforce strict constraint sa tisfaction in applications such as beamforming\, phase-shift optimization\ , and power allocation. The emerging role of generative AI models\, includ ing large language models (LLMs)\, is further discussed in enabling adapti ve and environment-aware resource allocation strategies that reduce depend ence on frequent model redesign and retraining. The lecture concludes by i dentifying key research challenges and outlining a roadmap toward scalable \, robust\, and AI-native 6G wireless networks.\n\nSpeaker Bio: Hina Tabas sum (Senior Member\, IEEE) received the Ph.D. degree from the King Abdulla h University of Science and Technology. She is currently an Associate Prof essor with the Lassonde School of Engineering\, York University\, Canada\, where she joined as an Assistant Professor in 2018. She is also appointed as a Visiting Faculty with the University of Toronto in 2024\, and the Yo rk Research Chair of 5G/6G-enabled mobility and sensing applications in 20 23\, for five years. She is listed in Stanford’s list of the World’s T op Two-Percent Researchers from 2021 to 2025. She has been selected as the IEEE ComSoc Distinguished Lecturer for the term 2025–2026. She has co-a uthored over 120 refereed articles in well-reputed IEEE journals\, magazin es\, and conferences. Her current research interests include multiband 6G wireless communications and sensing networks\, connected and autonomous sy stems\, and AI-enabled network mobility and resource management solutions. She has earned numerous distinctions\, including the N2Women Star in Netw orking and Communications (2025)\, Early Career Lassonde Innovation Award (2023)\, N2Women Rising Star in Networking and Communications (2022)\, mul tiple Exemplary Editor awards from IEEE journals\, and appointment to the NSERC Discovery Grant Evaluation Group (2025–2028). She served as an Ass ociate Editor for IEEE Communications Letters from 2019 to 2023\, IEEE Ope n Journal of the Communications Society from 2019 to 2023\, and IEEE Trans actions on Green Communications and Networking from 2020 to 2023. She is a lso currently serving as an Area Editor for IEEE Open Journal of the Commu nications Society and an Associate Editor for IEEE Transactions on Communi cations\, IEEE Transactions on Mobile Computing\, IEEE Transactions on Wir eless Communications\, and IEEE Communications Surveys and Tutorials.\n\nB ldg: ICT 424C\, University of Calgary\, Calgary\, Alberta\, Canada LOCATION:Bldg: ICT 424C\, University of Calgary\, Calgary\, Alberta\, Canad a ORGANIZER:maziar.shafieidarabi@ucalgary.ca SEQUENCE:13 SUMMARY:AI-Native Resource Management for 6G: From Deep Unsupervised Learni ng to Generative Intelligence URL;VALUE=URI:https://events.vtools.ieee.org/m/554690 X-ALT-DESC:Description: <br /><p style="line-height: 1\; text-align: justif y\;"><span style="font-family: 'times new roman'\, times\, serif\; font-si ze: 14pt\;"><strong data-olk-copy-source="MessageBody">Abstract: </strong> The unprecedented scale\, heterogeneity\, and performance requirements of 6G networks fundamentally challenge traditional optimization-centric appro aches to resource management\, motivating a paradigm shift toward artifici al intelligence (AI)&ndash\;driven methodologies. This lecture examines ho w deep unsupervised learning and generative AI techniques can be leveraged to solve both convex and non-convex network resource allocation problems under complex\, nonlinear constraints. Particular emphasis is placed on de ep unsupervised learning frameworks\, deep implicit layers\, and different iable projection methods that enforce strict constraint satisfaction in ap plications such as beamforming\, phase-shift optimization\, and power allo cation. The emerging role of generative AI models\, including large langua ge models (LLMs)\, is further discussed in enabling adaptive and environme nt-aware resource allocation strategies that reduce dependence on frequent model redesign and retraining. The lecture concludes by identifying key r esearch challenges and outlining a roadmap toward scalable\, robust\, and AI-native 6G wireless networks.</span></p>\n<p style="line-height: 1\; tex t-align: justify\;"><span style="font-size: 14pt\;"><span style="font-fami ly: 'times new roman'\, times\, serif\;"><strong><span lang="EN-AU">Speake r Bio</span></strong><span lang="EN-AU">: </span><span lang="EN-AU">Hina T abassum (Senior Member\, IEEE) received the Ph.D. degree from the King Abd ullah University of Science and Technology. She is currently an Associate Professor with the Lassonde School of Engineering\, York University\, Cana da\, where she joined as an Assistant Professor in 2018. She is also appoi nted as a Visiting Faculty with the University of Toronto in 2024\, and th e York Research Chair of 5G/6G-enabled mobility and sensing applications i n 2023\, for five years. She is listed in Stanford&rsquo\;s list of the Wo rld&rsquo\;s Top Two-Percent Researchers from 2021 to 2025. She has been s elected as the IEEE ComSoc Distinguished Lecturer for the term 2025&ndash\ ;2026. She has co-authored over 120 refereed articles in well-reputed IEEE journals\, magazines\, and conferences. </span></span><span lang="EN-AU" style="font-family: 'times new roman'\, times\, serif\;">Her current resea rch interests include multiband 6G wireless communications and sensing net works\, connected and autonomous systems\, and AI-enabled network mobility and resource management solutions. She has earned numerous distinctions\, including the N2Women Star in Networking and Communications (2025)\, Earl y Career Lassonde Innovation Award (2023)\, N2Women Rising Star in Network ing and Communications (2022)\, multiple Exemplary Editor awards from IEEE journals\, and appointment to the NSERC Discovery Grant Evaluation Group (2025&ndash\;2028). </span><span lang="EN-AU" style="font-family: 'times n ew roman'\, times\, serif\;">She served as an Associate Editor for IEEE Co mmunications Letters from 2019 to 2023\, IEEE Open Journal of the Communic ations Society from 2019 to 2023\, and IEEE Transactions on Green Communic ations and Networking from 2020 to 2023. She is also currently serving as an Area Editor for IEEE Open Journal of the Communications Society and an Associate Editor for IEEE Transactions on Communications\, IEEE Transactio ns on Mobile Computing\, IEEE Transactions on Wireless Communications\, an d IEEE Communications Surveys and Tutorials.</span></span></p> END:VEVENT END:VCALENDAR