BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Singapore BEGIN:DAYLIGHT DTSTART:20380119T111407 TZOFFSETFROM:+0800 TZOFFSETTO:+0800 RRULE:FREQ=YEARLY;BYDAY=3TU;BYMONTH=1 TZNAME:+08 END:DAYLIGHT BEGIN:STANDARD DTSTART:19820101T000000 TZOFFSETFROM:+0730 TZOFFSETTO:+0800 RRULE:FREQ=YEARLY;BYDAY=1FR;BYMONTH=1 TZNAME:+08 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260930T065511Z UID:16A14EAD-91B9-4377-A22E-C5A00E09E6D6 DTSTART;TZID=Asia/Singapore:20261015T093000 DTEND;TZID=Asia/Singapore:20261015T113000 DESCRIPTION:Title: Beyond Shannon and Moore: Rebuilding the Theoretical Fou ndations of Communications and Computing\n\nDate: 15th Oct 2026 (Thursday) \n\nTime: 0930 – 1130 am\n\nVenue: EEE Executive Seminar Rm (S2.2-B2-53) \n\nAbstract:\nFor almost a century\, communications and computing have be en built upon a remarkably small set of intellectual foundations: Nyquist sampling\, Shannon information theory\, the von Neumann architecture\, Moo re's law\, and classical linear signal processing. These principles enable d the digital revolution\, but many of the assumptions behind them are inc reasingly challenged by the requirements of AI\, 6G\, massive sensing syst ems\, extreme mobility\, new electromagnetic architectures\, and the physi cal limits of computation. This talk asks a fundamental question: do we ne ed new theories\, rather than simply better implementations of existing on es? I will discuss several directions in which classical abstractions need to be revisited\, including electromagnetic information theory\, nonlinea r signal processing\, super-resolution\, semantic communications\, multi-a gent learning\, information theory for non-equilibrium systems\, non-coher ent communications\, integrated sensing and communication\, and the thermo dynamics of computation and communication. These directions reflect a broa der transition from optimizing only transmitters and receivers toward opti mizing intelligent environments\, exploiting memory and data\, incorporati ng physical phenomena directly into communication models\, and learning ra ther than compensating for the imperfections of the physical world. The ce ntral message is that the next generation of communications and computing may require something deeper than another waveform\, architecture\, or neu ral network: it may require us to rethink some of the theories on which th e field has been built.\n\nSpeaker: Prof. Merouane Debbah\, Senior Directo r\, Digital Future Institute\, Khalifa University\, Abu Dhabi\n\nBio: Mero uane Debbah is a researcher\, educator and technology entrepreneur. Over h is career\, he has founded several public and industrial research centers\ , start-ups and is now Professor at Khalifa University of Science and Tech nology in Abu Dhabi and founding Senior Director of KU Digital Future Inst itute. He is a frequent keynote speaker at international events in the fie ld of telecommunication and AI. His research has been lying at the interfa ce of fundamental mathematics\, algorithms\, statistics\, information and communication sciences with a special focus on random matrix theory and le arning algorithms. In the Communication field\, he has been at the heart o f the development of small cells (4G)\, Massive MIMO (5G) and Large Intell igent Surfaces (6G) technologies. In the AI field\, he is known for his wo rk on Large Language Models\, distributed AI systems for networks and sema ntic communications. He received multiple prestigious distinctions\, prize s and best paper awards (more than 50 IEEE best paper awards) for his cont ributions to both fields and according to research.com is ranked as the be st scientist in France in the field of Electronics and Electrical Engineer ing. He is an IEEE Fellow\, a WWRF Fellow\, a Eurasip Fellow\, an AAIA Fel low\, an Institut Louis Bachelier Fellow\, an AIIA Fellow and a Membre ém érite SEE. More recently\, his work has focused on the convergence of gen erative AI and telecommunications. He has contributed to the development o f domain-specific foundation models for networks\, including TelecomGPT\, a family of large language models designed to understand and reason about telecommunications problems. His research also explores agentic AI and mul ti-agent systems for autonomous networks\, RF foundation models\, Large Pe rceptive Models\, telecom world models\, semantic communications\, and the use of generative AI for sensing\, network operation\, optimization\, and reasoning over the physical world. He is actually chair of the IEEE Large Generative AI Models in Telecom (GenAINet) Emerging Technology Initiative and a member of the Marconi Prize Selection Advisory Committee.\n\nEEE Ex ecutive Seminar Rm (S2.2-B2-53) \, Singapore\, Singapore\, Singapore LOCATION:EEE Executive Seminar Rm (S2.2-B2-53) \, Singapore\, Singapore\, S ingapore ORGANIZER:chau.yuen@ntu.edu.sg SEQUENCE:3 SUMMARY:Beyond Shannon and Moore: Rebuilding the Theoretical Foundations of Communications and Computing URL;VALUE=URI:https://events.vtools.ieee.org/m/580967 X-ALT-DESC:Description: <br /><p class="MsoNormal"><strong>Title: Beyond Sh annon and Moore: Rebuilding the Theoretical Foundations of Communications and Computing</strong></p>\n<p class="MsoNormal">&nbsp\;</p>\n<p class="Ms oNormal"><strong>Date: 15th Oct 2026 (Thursday)</strong></p>\n<p class="Ms oNormal"><strong>Time: 0930 &ndash\; 1130 am</strong></p>\n<p class="MsoNo rmal"><strong>Venue: EEE Executive Seminar Rm (S2.2-B2-53) </strong></p>\n <p class="MsoNormal">&nbsp\;</p>\n<p class="MsoNormal"><strong>Abstract:</ strong><br>For almost a century\, communications and computing have been b uilt upon a remarkably small set of intellectual foundations: Nyquist samp ling\, Shannon information theory\, the von Neumann architecture\, Moore's law\, and classical linear signal processing. These principles enabled th e digital revolution\, but many of the assumptions behind them are increas ingly challenged by the requirements of AI\, 6G\, massive sensing systems\ , extreme mobility\, new electromagnetic architectures\, and the physical limits of computation. &nbsp\;This talk asks a fundamental question: do we need new theories\, rather than simply better implementations of existing ones? I will discuss several directions in which classical abstractions n eed to be revisited\, including electromagnetic information theory\, nonli near signal processing\, super-resolution\, semantic communications\, mult i-agent learning\, information theory for non-equilibrium systems\, non-co herent communications\, integrated sensing and communication\, and the the rmodynamics of computation and communication. These directions reflect a b roader transition from optimizing only transmitters and receivers toward o ptimizing intelligent environments\, exploiting memory and data\, incorpor ating physical phenomena directly into communication models\, and learning rather than compensating for the imperfections of the physical world. &nb sp\;The central message is that the next generation of communications and computing may require something deeper than another waveform\, architectur e\, or neural network: it may require us to rethink some of the theories o n which the field has been built.</p>\n<p class="MsoNormal">&nbsp\;</p>\n< p class="MsoNormal"><strong>Speaker</strong>: Prof. Merouane Debbah\, Seni or Director\, Digital Future Institute\, Khalifa University\, Abu Dhabi</p >\n<p class="MsoNormal">&nbsp\;</p>\n<p class="MsoNormal"><strong>Bio:</st rong> Merouane Debbah is a researcher\, educator and technology entreprene ur. Over his career\, he has founded several public and industrial researc h centers\, start-ups and is now Professor at&nbsp\; Khalifa University of Science and Technology in Abu Dhabi and founding Senior Director of KU Di gital Future Institute. He is a frequent keynote speaker at international events in the field of telecommunication and AI. His research has been lyi ng at the interface of fundamental mathematics\, algorithms\, statistics\, information and communication sciences with a special focus on random mat rix theory and learning algorithms. In the Communication field\, he has be en at the heart of the development of small cells (4G)\, Massive MIMO (5G) and Large Intelligent Surfaces (6G) technologies. In the AI field\, he is known for his work on Large Language Models\, distributed AI systems for networks and semantic communications. He received multiple prestigious dis tinctions\, prizes and best paper awards (more than 50 IEEE best paper awa rds) for his contributions to both fields and according to research.com is ranked as the best scientist in France in the field of Electronics and El ectrical Engineering. He is an IEEE Fellow\, a WWRF Fellow\, a Eurasip Fel low\, an AAIA Fellow\, an Institut Louis Bachelier Fellow\, an AIIA Fellow &nbsp\; and a Membre &eacute\;m&eacute\;rite SEE. More recently\, his work has focused on the convergence of generative AI and telecommunications. H e has contributed to the development of domain-specific foundation models for networks\, including TelecomGPT\, a family of large language models de signed to understand and reason about telecommunications problems. His res earch also explores agentic AI and multi-agent systems for autonomous netw orks\, RF foundation models\, Large Perceptive Models\, telecom world mode ls\, semantic communications\, and the use of generative AI for sensing\, network operation\, optimization\, and reasoning over the physical world. He is actually chair of&nbsp\; the IEEE Large Generative AI Models in Tele com (GenAINet) Emerging Technology Initiative and&nbsp\; a member of the M arconi Prize Selection Advisory Committee.</p> END:VEVENT END:VCALENDAR