BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260310T180906Z UID:22B2CAB4-45C1-4AAE-8783-FE90560349F1 DTSTART;TZID=America/Chicago:20260508T103000 DTEND;TZID=America/Chicago:20260508T120000 DESCRIPTION:Abstract: Large Language Models (LLMs) are emerging as a key en abler for reshaping wireless networks through their powerful reasoning and generalization capabilities. This talk begins with an overview of LLM fun damentals\, followed by a discussion of their emerging applications in wir eless systems\, highlighting both the opportunities they create and the pr actical challenges they pose. Prompt engineering is introduced as a lightw eight and effective alternative to fine-tuning\, enabling accurate\, conte xt-aware\, and resource-efficient decision-making. Two representative use cases will be presented. First\, network resource allocation will be addre ssed through a unified multi-agent framework in which iterative prompting and structured feedback are used to solve constrained non-convex optimizat ion problems\, achieving scalable\, feasible\, and near-optimal performanc e. Second\, intelligent decision-making for autonomous vehicular systems w ill be discussed through joint optimization of vehicle-to-infrastructure ( V2I) communications and autonomous driving policies. Across these applicat ions\, LLM-driven frameworks demonstrate reduced time complexity and enhan ced adaptability compared to conventional approaches. The talk concludes b y outlining how such LLM-driven optimization frameworks can evolve into un ified\, foundation-model-based engines for end-to-end wireless network int elligence.\n\nBiography: HINA TABASSUM (Senior Member\, IEEE) received the Ph.D. degree from the King Abdullah University of Science and Technology. She is currently an Associate Professor with the Lassonde School of Engin eering\, York University\, Canada\, where she joined as an Assistant Profe ssor in 2018. She is also appointed as a Visiting Faculty with the Univers ity of Toronto in 2024\, and the York Research Chair of 5G/6G-enabled mobi lity and sensing applications in 2023\, for five years. She is listed in t he Stanford’s list of the World’s Top Two-Percent Researchers from 202 1 to 2025. She has been selected as the IEEE ComSoc Distinguished Lecturer for the term 2025–2026. She has co-authored over 120 refereed articles in well-reputed IEEE journals\, magazines\, and conferences. Her current r esearch interests include multiband 6G wireless communications and sensing networks\, connected and autonomous systems\, and AI-enabled network mobi lity and resource management solutions. She has earned numerous distinctio ns\, including the N2Women Star in Networking and Communications (2025)\, Early Career Lassonde Innovation Award (2023)\, N2Women Rising Star in Net working and Communications (2022)\, multiple Exemplary Editor awards from IEEE journals\, and appointment to the NSERC Discovery Grant Evaluation Gr oup (2025–2028). She served as an Associate Editor for IEEE COMMUNICATIO NS LETTERS from 2019 to 2023\, IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOC IETY from 2019 to 2023\, and IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING from 2020 to 2023. She is also currently serving as an Area Ed itor for IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY and an Associate Editor for IEEE TRANSACTIONS ON COMMUNICATIONS\, IEEE TRANSACTIONS ON MOBI LE COMPUTING\, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS\, and IEEE COM MUNICATIONS SURVEYS AND TUTORIALS.\n\nSpeaker(s): HINA TABASSUM\, \n\nRoom : EITC E1 270\, Winnipeg\, Manitoba\, Canada LOCATION:Room: EITC E1 270\, Winnipeg\, Manitoba\, Canada ORGANIZER:amirkaba@myumanitoba.ca SEQUENCE:15 SUMMARY:Unlocking the Power of Large Language Models in Wireless Networks: From Prompt Engineering to Intelligent Optimization URL;VALUE=URI:https://events.vtools.ieee.org/m/546222 X-ALT-DESC:Description: <br /><p>Abstract: Large Language Models (LLMs) are emerging as a key enabler for reshaping wireless networks through their p owerful reasoning and generalization capabilities. This talk begins with a n overview of LLM fundamentals\, followed by a discussion of their emergin g applications in wireless systems\, highlighting both the opportunities t hey create and the practical challenges they pose. Prompt engineering is i ntroduced as a lightweight and effective alternative to fine-tuning\, enab ling accurate\, context-aware\, and resource-efficient decision-making. Tw o representative use cases will be presented. First\, network resource all ocation will be addressed through a unified multi-agent framework in which iterative prompting and structured feedback are used to solve constrained non-convex optimization problems\, achieving scalable\, feasible\, and ne ar-optimal performance. Second\, intelligent decision-making for autonomou s vehicular systems will be discussed through joint optimization of vehicl e-to-infrastructure (V2I) communications and autonomous driving policies. Across these applications\, LLM-driven frameworks demonstrate reduced time complexity and enhanced adaptability compared to conventional approaches. The talk concludes by outlining how such LLM-driven optimization framewor ks can evolve into unified\, foundation-model-based engines for end-to-end wireless network intelligence.</p>\n<p><strong>Biography:</strong> 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 the Stanford&rsquo\;s list of th e World&rsquo\;s Top Two-Percent Researchers from 2021 to 2025. She has be en selected as the IEEE ComSoc Distinguished Lecturer for the term 2025&nd ash\;2026. She has co-authored over 120 refereed articles in well-reputed IEEE journals\, magazines\, and conferences. Her current research interest s include multiband 6G wireless communications and sensing networks\, conn ected and autonomous systems\, and AI-enabled network mobility and resourc e management solutions. She has earned numerous distinctions\, including t he N2Women Star in Networking and Communications (2025)\, Early Career Las sonde Innovation Award (2023)\, N2Women Rising Star in Networking and Comm unications (2022)\, multiple Exemplary Editor awards from IEEE journals\, and appointment to the NSERC Discovery Grant Evaluation Group (2025&ndash\ ;2028). She served as an Associate Editor for IEEE COMMUNICATIONS LETTERS from 2019 to 2023\, IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY from 2 019 to 2023\, and IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING from 2020 to 2023. She is also currently serving as an Area Editor for IE EE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY and an Associate Editor for IEEE TRANSACTIONS ON COMMUNICATIONS\, IEEE TRANSACTIONS ON MOBILE COMPUTIN G\, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS\, and IEEE COMMUNICATIONS SURVEYS AND TUTORIALS.</p> END:VEVENT END:VCALENDAR