BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250307T001425Z UID:43F124AE-DD0D-4A88-8298-818831F9FE9C DTSTART;TZID=America/New_York:20240905T180000 DTEND;TZID=America/New_York:20240905T190000 DESCRIPTION:Special Presentation by Dr. Hao Zhou and Chengming Hu (McGill U .\, Canada)\n\nHosted by the Future Networks Artificial Intelligence & Mac hine Learning (AIML) Working Group\n\nDate/Time: Thursday\, September 5th\ , 2024 @ 6 PM EDT\n\nTopic:\n\nLarge Language Models (LLMs) for NextG Wire less Networks:\n\nFundamentals and Case Studies in Network Optimization an d Prediction\n\nAbstract:\n\nLarge language models (LLMs) have received co nsiderable attention recently due to their outstanding comprehension and r easoning capabilities\, leading to great progress in many fields. The adva ncement of LLM techniques also offers promising opportunities to automate many tasks in the communication networks field. After pre-training and fin e-tuning\, LLMs can perform diverse downstream tasks based on human instru ctions\, paving the way to artificial general intelligence (AGI)-enabled 6 G. This talk will first present a comprehensive overview of LLM fundamenta ls and applications to wireless networks\, discussing wireless-specific LL M training\, fine-tuning\, and practical deployment. Then\, it will presen t two case studies on specific network optimization and prediction problem s\, showing detailed prompt and algorithm designs along with simulation re sults.\n\nSpeakers:\n\nDr. Hao Zhou is currently a Postdoctoral Researcher at the School of Computer Science\, McGill University. He completed my Ph D degree at University of Ottawa\, Canada\, from 2019 to 2023. His researc h focuses on the intersection between machine learning\, optimization\, an d networked systems\, especially for 5G/6G wireless networks and power sys tems. Dr. Zhou is dedicated to developing novel machine learning algorithm s to address a series of optimization problems in networked systems\, incl uding resource allocation\, computational task offloading\, energy efficie ncy enhancement\, energy management and trading\, network security\, etc. He has published more than 30 peer-reviewed papers\, including reputable j ournals in IEEE Communication and Power Energy Societies\, e.g.\, IEEE Wir eless Communications\, IEEE Trans. Smart Grid\, and IEEE Communications Su rvey & Tutorials. He has received the Best Paper Award at the 2023 IEEE IC C conference\, and the 2023 IEEE ComSoc CSIM TC Best Journal Paper Award f or his contributions to transfer learning-enabled wireless network slicing . Dr. Zhou’s PhD Thesis entitled “ML-Based Optimization of Large-Scale Systems: Case Study in Smart Microgrids and 5G RAN” won the 2023 Facult y of Engineering’s Best Doctoral Thesis Award at University of Ottawa.\n \nChengming Hu is currently a Ph.D. candidate at McGill University\, Canad a. He received M.Sc. in Quality Systems Engineering with Concordia Institu te for Information Systems Engineering (CIISE)\, Concordia University\, Ca nada\, in 2019. His research interests focus on investigating computationa l intelligence techniques to enhance the effectiveness and security of IoT systems\, including ensemble learning\, knowledge distillation\, language model\, and feature learning\, etc. He is actively working on various rea l-world applications\, including power systems\, communication systems\, a nd transportation systems. His work has been published in top-tier\, peer- reviewed conferences and journals\, including ICLR\, IEEE TSG\, and IEEE P ESGM\, etc.\n\nCo-sponsored by: Artificial Intelligence & Machine Learning (AIML) Working Group\n\nVirtual: https://events.vtools.ieee.org/m/431215 LOCATION:Virtual: https://events.vtools.ieee.org/m/431215 ORGANIZER:c.polk@comsoc.org SEQUENCE:11 SUMMARY:Large Language Models (LLMs) for NextG Wireless Networks URL;VALUE=URI:https://events.vtools.ieee.org/m/431215 X-ALT-DESC:Description: <br /><p><img src="https://events.vtools.ieee.org/v tools_ui/media/display/8b67856c-a168-4bbf-9038-8322f34e1a57"></p>\n<p clas s="MsoNormal" style="margin-top: 12.0pt\;">Special Presentation by<strong> Dr. Hao Zhou </strong>and<strong> Chengming Hu (McGill U.\, Canada)</stro ng></p>\n<p><strong><span style="font-size: 16.0pt\; font-family: Copperpl ate\;"><span style="font-size: 12.0pt\; font-family: 'Calibri'\,sans-serif \; mso-ascii-theme-font: minor-latin\; mso-fareast-font-family: PMingLiU\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme-font: minor-latin \; mso-bidi-font-family: Arial\; mso-bidi-theme-font: minor-bidi\; mso-ans i-language: EN-US\; mso-fareast-language: ZH-TW\; mso-bidi-language: AR-SA \;">Hosted by the Future Networks Artificial Intelligence &amp\; Machine L earning (AIML) Working Group</span></span></strong></p>\n<p><strong><span style="font-size: 16.0pt\; font-family: Copperplate\;"><span style="font-s ize: 12.0pt\; font-family: 'Calibri'\,sans-serif\; mso-ascii-theme-font: m inor-latin\; mso-fareast-font-family: PMingLiU\; mso-fareast-theme-font: m inor-fareast\; mso-hansi-theme-font: minor-latin\; mso-bidi-font-family: A rial\; mso-bidi-theme-font: minor-bidi\; mso-ansi-language: EN-US\; mso-fa reast-language: ZH-TW\; mso-bidi-language: AR-SA\;"><span style="font-fami ly: Copperplate\;">Date/Time: </span></span><span style="font-size: 12.0pt \; font-family: 'Calibri'\,sans-serif\; mso-ascii-theme-font: minor-latin\ ; mso-fareast-font-family: PMingLiU\; mso-fareast-theme-font: minor-fareas t\; mso-hansi-theme-font: minor-latin\; mso-bidi-font-family: Arial\; mso- bidi-theme-font: minor-bidi\; mso-ansi-language: EN-US\; mso-fareast-langu age: ZH-TW\; mso-bidi-language: AR-SA\;">Thursday\, September 5th\, 2024 @ 6 PM EDT</span></span></strong></p>\n<p class="MsoNormal" style="margin-t op: .25in\;"><strong><u><span style="font-size: 16.0pt\; font-family: Copp erplate\;">Topic</span></u></strong><strong><span style="font-size: 16.0pt \; font-family: Copperplate\;">:</span></strong></p>\n<p class="MsoNormal" ><strong><span style="font-size: 16.0pt\;">Large Language Models (LLMs) fo r NextG Wireless Networks:</span></strong></p>\n<p class="MsoNormal"><stro ng><span style="font-size: 14.0pt\;">Fundamentals and Case Studies in Netw ork Optimization and Prediction</span></strong></p>\n<p class="MsoNormal" style="margin-top: .25in\;"><strong><u><span style="font-size: 16.0pt\; fo nt-family: Copperplate\;">Abstract</span></u></strong><strong><span style= "font-size: 16.0pt\; font-family: Copperplate\;">:</span></strong></p>\n<p ><strong><span style="font-size: 16.0pt\; font-family: Copperplate\;"><spa n style="font-size: 12.0pt\; font-family: 'Calibri'\,sans-serif\; mso-asci i-theme-font: minor-latin\; mso-fareast-font-family: PMingLiU\; mso-fareas t-theme-font: minor-fareast\; mso-hansi-theme-font: minor-latin\; mso-bidi -font-family: Arial\; mso-bidi-theme-font: minor-bidi\; mso-ansi-language: EN-US\; mso-fareast-language: ZH-TW\; mso-bidi-language: AR-SA\;">Large l anguage models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities\, leading to g reat progress in many fields. The advancement of LLM techniques also offer s promising opportunities to automate many tasks in the communication netw orks field. After pre-training and fine-tuning\, LLMs can perform diverse downstream tasks based on human instructions\, paving the way to artificia l general intelligence (AGI)-enabled 6G. This talk will first present a co mprehensive overview of LLM fundamentals and applications to wireless netw orks\, discussing wireless-specific LLM training\, fine-tuning\, and pract ical deployment. Then\, it will present two case studies on specific netwo rk optimization and prediction problems\, showing detailed prompt and algo rithm designs along with simulation results.</span></span></strong></p>\n< p><strong><span style="font-size: 16.0pt\; font-family: Copperplate\;"><u> Speakers</u>:</span></strong></p>\n<table style="border-collapse: collapse \; width: 100%\;" border="1"><colgroup><col style="width: 24.3015%\;"><col style="width: 75.7635%\;"></colgroup>\n<tbody>\n<tr>\n<td><img src="https ://events.vtools.ieee.org/vtools_ui/media/display/76d6a906-da37-4149-ad3f- 3578831ad433"></td>\n<td>\n<p class="MsoNormal" style="margin-top: 6.0pt\; "><span style="font-family: verdana\, geneva\, sans-serif\; font-size: 10p t\;">Dr. Hao Zhou is currently a Postdoctoral Researcher at the School of Computer Science\, McGill University. He completed my PhD degree at Univer sity of Ottawa\, Canada\, from 2019 to 2023. His research focuses on the i ntersection between machine learning\, optimization\, and networked system s\, especially for 5G/6G wireless networks and power systems. Dr. Zhou is dedicated to developing novel machine learning algorithms to address a ser ies of optimization problems in networked systems\, including resource all ocation\, computational task offloading\, energy efficiency enhancement\, energy management and trading\, network security\, etc. He has published m ore than 30 peer-reviewed papers\, including reputable journals in IEEE Co mmunication and Power Energy Societies\, e.g.\, IEEE Wireless Communicatio ns\, IEEE Trans. Smart Grid\, and IEEE Communications Survey &amp\; Tutori als. He has received the Best Paper Award at the 2023 IEEE ICC conference\ , and the 2023 IEEE ComSoc CSIM TC Best Journal Paper Award for his contri butions to transfer learning-enabled wireless network slicing. Dr. Zhou&rs quo\;s PhD Thesis entitled &ldquo\;ML-Based Optimization of Large-Scale Sy stems: Case Study in Smart Microgrids and 5G RAN&rdquo\; won the 2023 Facu lty of Engineering&rsquo\;s Best Doctoral Thesis Award at University of Ot tawa.</span></p>\n</td>\n</tr>\n<tr>\n<td><img src="https://events.vtools. ieee.org/vtools_ui/media/display/9fd2c9fb-5698-4b97-a05e-4ff681b08986" wid th="215" height="296"></td>\n<td><span style="font-size: 10pt\; font-famil y: verdana\, geneva\, sans-serif\;">Chengming Hu is currently a Ph.D. cand idate at McGill University\, Canada. He received M.Sc. in Quality Systems Engineering with Concordia Institute for Information Systems Engineering ( CIISE)\, Concordia University\, Canada\, in 2019. His research interests f ocus on investigating computational intelligence techniques to enhance the effectiveness and security of IoT systems\, including ensemble learning\, knowledge distillation\, language model\, and feature learning\, etc. He is actively working on various real-world applications\, including power s ystems\, communication systems\, and transportation systems. His work has been published in top-tier\, peer-reviewed conferences and journals\, incl uding ICLR\, IEEE TSG\, and IEEE PESGM\, etc.</span></td>\n</tr>\n</tbody> \n</table> END:VEVENT END:VCALENDAR