BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260125T034750Z UID:0462C5C3-2207-4820-9931-29AB9F0BD294 DTSTART;TZID=Etc/UTC:20260115T120000 DTEND;TZID=Etc/UTC:20260115T130000 DESCRIPTION:[ALLSTaR: Automated LLM-Driven Scheduler Generation and Testing for Intent-Based RAN]\n\nSpecial Presentation by Dr. Dilip Krishnaswamy ( C-DOT\, India)\n\nHosted by the Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group\n\nDate/Time: Thursday\, 15 January 2025 @ 12:00 UTC (12 PM GMT)\n\nTopic:\n\nNEOL: Multiscale Adaptation of L arge Language Models for Network Energy Optimization\n\nAbstract:\n\nFocus ing on energy conservation and sustainable system development in wireless mobile networks\, in this talk we explore energy-saving approaches in the telecommunications sector through a proactive methodology that predicts ne twork usage patterns. Edge cloud resources can be leveraged for AI model l earning based on observed network key performance indicators. Dynamic netw ork edge processing is utilized to enable dynamic network resource managem ent with digital twin model processing to provide the dynamic input contex t window to an AI inferencing engine. Furthermore\, the study finds that L arge Language Models (LLMs) that have the ability to process data across d ifferent timescales could be leveraged to create a future predicted networ k resource utilization context window. Our results indicate that such a pr edicted context window combined with dual threshold monitoring of network utilization can be used to enable dynamic network resource optimization.\n \nSpeaker:\n\n[]\nDilip Krishnaswamy has led the architecture\, design\, a nd development of engineering platforms & products at Intel Corp\, Qualcom m Research\, IBM Research\, and Jio Platforms. He received his PhD degree in Electrical Engineering from the University of Illinois at Urbana-Champa ign. He is an inventor on 80+ granted patents and has authored 80+ researc h publications. He is presently serving as Executive Vice-President at C-D OT (Centre for Development of Telematics)\, Bangalore\, India\, where he i s leading Advanced-5G Engineering and 6G Innovation efforts.\n\nBrochure ( PDF): [Webinar-AIML-2026-01-15-Krishnaswamy-LLM-Energy-Optimization-Brochu re.pdf](https://drive.google.com/file/d/1bzse7ocgUEqir1XCyBsM8o5lyPtKe_26/ view)\n\nCo-sponsored by: Future Networks Artificial Intelligence & Machin e Learning (AIML) Working Group\n\nVirtual: https://events.vtools.ieee.org /m/519915 LOCATION:Virtual: https://events.vtools.ieee.org/m/519915 ORGANIZER:baw@ieee.org SEQUENCE:63 SUMMARY:NEOL: Multiscale Adaptation of Large Language Models for Network En ergy Optimization URL;VALUE=URI:https://events.vtools.ieee.org/m/519915 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="margin-top: .25in \;"><img src="https://events.vtools.ieee.org/vtools_ui/media/display/c654b b65-a808-4a7c-9903-d22e0aa35286" alt="ALLSTaR: Automated LLM-Driven Schedu ler Generation and Testing for Intent-Based RAN" width="750" height="197"> </p>\n<p class="MsoNormal" style="margin-top: 12.0pt\;">Special Presentati on by<strong> Dr. Dilip Krishnaswamy (C-DOT\, India)</strong></p>\n<p clas s="MsoNormal" style="margin-top: 12.0pt\;">Hosted by the Future Networks<s trong> Artificial Intelligence &amp\; Machine Learning (AIML) Working Grou p</strong></p>\n<p class="MsoNormal" style="margin-top: 12.0pt\;"><strong> <span style="font-size: 14.0pt\; font-family: Copperplate\; mso-fareast-fo nt-family: PMingLiU\; mso-fareast-theme-font: minor-fareast\; mso-bidi-fon t-family: Arial\; mso-bidi-theme-font: minor-bidi\; mso-ansi-language: EN- US\; mso-fareast-language: ZH-TW\; mso-bidi-language: AR-SA\;">Date/Time</ span></strong><span style="font-size: 12.0pt\; font-family: 'Calibri'\,san s-serif\; mso-ascii-theme-font: minor-latin\; mso-fareast-font-family: PMi ngLiU\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme-font: mino r-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\;">: <strong>Thursday\, 15 January 2025</strong><strong> @ 12:00 U TC (12 PM GMT)</strong></span></p>\n<p class="MsoNormal" style="margin-top : .25in\;"><strong><u><span style="font-size: 16.0pt\; font-family: Copper plate\;">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: 16pt\;">NEOL: Multiscale Adaptation of Larg e Language Models for Network Energy Optimization&nbsp\;</span></strong></ p>\n<p class="MsoNormal" style="margin-top: .25in\;"><strong><u><span styl e="font-size: 16.0pt\; font-family: Copperplate\;">Abstract</span></u></st rong><strong><span style="font-size: 16.0pt\; font-family: Copperplate\;"> :</span></strong></p>\n<p>Focusing on energy conservation and sustainable system development in wireless mobile networks\, in this talk we explore e nergy-saving approaches in the telecommunications sector through a proacti ve methodology that predicts network usage patterns. Edge cloud resources can be leveraged for AI model learning based on observed network key perfo rmance indicators. Dynamic network edge processing is utilized to enable d ynamic network resource management with digital twin model processing to p rovide the dynamic input context window to an AI inferencing engine. Furth ermore\, the study finds that Large Language Models (LLMs) that have the a bility to process data across different timescales could be leveraged to c reate a future predicted network resource utilization context window. Our results indicate that such a predicted context window combined with dual t hreshold monitoring of network utilization can be used to enable dynamic n etwork resource optimization.</p>\n<p><strong><span style="font-size: 16.0 pt\; font-family: Copperplate\;"><u>Speaker</u>:</span></strong></p>\n<tab le style="border-collapse: collapse\; width: 100%\;" border="1"><colgroup> <col style="width: 14.779271%\;"><col style="width: 85.12476%\;"></colgrou p>\n<tbody>\n<tr>\n<td><img src="https://events.vtools.ieee.org/vtools_ui/ media/display/fc67ba57-fab4-4ea9-b894-53a496322a06" alt="" width="127" hei ght="141"></td>\n<td>\n<p class="MsoNormal" style="margin-top: 6.0pt\;"><s trong>Dilip Krishnaswamy</strong> has led the architecture\, design\, and development of engineering platforms &amp\; products at Intel Corp\, Qualc omm Research\, IBM Research\, and Jio Platforms. He received his PhD degre e in Electrical Engineering from the University of Illinois at Urbana-Cham paign. He is an inventor on 80+ granted patents and has authored 80+ resea rch publications. He is presently serving as Executive Vice-President at C -DOT (Centre for Development of Telematics)\, Bangalore\, India\, where he is leading Advanced-5G Engineering and 6G Innovation efforts.</p>\n</td>\ n</tr>\n</tbody>\n</table>\n<p>&nbsp\;</p>\n<p><strong>Brochure (PDF)</str ong>: <a href="https://drive.google.com/file/d/1bzse7ocgUEqir1XCyBsM8o5lyP tKe_26/view">Webinar-AIML-2026-01-15-Krishnaswamy-LLM-Energy-Optimization- Brochure.pdf</a></p> END:VEVENT END:VCALENDAR