Please join us for a time to have a roundtable discussion on technical talks the past year and plans for next year. Virtual: https://events.vtools.ieee.org/m/457673

BitCoin was proposed in 2008 and (based on a white paper), launched in 2009. It was worthless initially, but is now priced at $100,000 per coin. It can be incremented into 1/100,000,000 parts, each called a “Satoshi”. The white paper proposes that security is ultimately achieved by the hosting agency having a more powerful and faster computer than anyone else. However, other security measures are a cryptographic system that would be difficult to overcome, and personnel who check each transaction for legitimacy. In spite of the seemingly impossible task of “hacking” the system, accounts have been stolen or lost several times. The purpose of this talk is not to advocate use of this system of commerce, but to recognize that at some point the US Government will be involved. This should eventually lead to a secure system for commerce. Some interesting features of the technology include the analogy of mining for gold, and the increasing difficulty of “finding” coins. Similar "coins" will also be discussed. Speaker(s): Doug Virtual: https://events.vtools.ieee.org/m/454896

“If we make them smaller, we can make them faster” has been the approach to building faster computers over the last 70-years starting with the first transistors and continuing as we built increasingly more complex integrated circuits. This was known as “Moore’s Law”. Recently, our technology is no longer achieving higher densities as we scale to smaller features sizes first with NAND Flash and now with SRAM and DRAM. New techniques known as Beyond Moore are required to continue to build faster and more complex computers. David Bondurant reviews the emergence of 3D memory and 3D packaging as today’s approach to building the fastest Supercomputers and AI Processors. Virtual: https://events.vtools.ieee.org/m/455111

In the dynamic landscape of startups, leveraging technology effectively can be a game-changer. This session delves into essential lessons learned in Information Technology (IT) that are crucial for the success and sustainability of startups. Whether you're launching a tech-driven product or service, managing data, or optimizing operations, understanding these insights can steer your startup towards growth and resilience. Co-sponsored by: Baton Rouge User Groups (BRUG) Speaker(s): Michael Viron Virtual: https://events.vtools.ieee.org/m/456302

Monthly meeting of the IEEE Baltimore Section's executive committee. The meeting is open to all Section members. This meeting will be by videoconference only. Virtual: https://events.vtools.ieee.org/m/456276

Agenda: Join Webex meeting: https://ieeemeetings.webex.com/ieeemeetings/j.php?MTID=m4942e0a63ee4cef11cb2fe744872c2b1 Meeting number: 2535 155 9659 Meeting password: GMtQQj3N7T7 To join by phone: +1-415-655-0002 United States Room: Longfellow Room, Westover Library, 1644 North McKinley Road, Arlington, Virginia, United States, 22205, Virtual: https://events.vtools.ieee.org/m/457225

Meetings of the Administrative Committee are held virtually. Members are welcome to attend. Reserve your place by registering online or calling the office by the Monday before. Agenda: AdCom Meeting: 7:00 P.M. - 9:00 P.M Virtual: https://events.vtools.ieee.org/m/440527

January 2025 EXCOM meeting only. Not a general meeting. Slatts Pub, 4858 Cooper Road, Blue Ash, Ohio, United States, 45242

January 2025 EXCOM meeting only. Not a general meeting. Slatts Pub, 4858 Cooper Road, Blue Ash, Ohio, United States, 45242

Special Presentation by Dr. Mérouane Debbah (Khalifa U., UAE) Hosted by the Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Date/Time: Thursday, January 16th, 2025 @ 12:00 UTC Topic: Generative Diffusion Models for Network Optimization Abstract: Network optimization is a fundamental challenge in Internet-of-Things (IoT) networks, often characterized by complex features that make it difficult to solve these problems. Recently, generative diffusion models (GDMs) have emerged as a promising new approach to network optimization, with the potential to directly address these optimization problems. However, the application of GDMs in this field is still in its early stages, and there is a noticeable lack of theoretical research and empirical findings. In this study, we first explore the intrinsic characteristics of generative models. Next, we provide a concise theoretical proof and intuitive demonstration of the advantages of generative models over discriminative models in network optimization. Based on this exploration, we implement GDMs as optimizers aimed at learning high-quality solution distributions for given inputs, sampling from these distributions during inference to approximate or achieve optimal solutions. Specifically, we utilize denoising diffusion probabilistic models (DDPMs) and employ a classifier-free guidance mechanism to manage conditional guidance based on input parameters. We conduct extensive experiments across three challenging network optimization problems. By investigating various model configurations and the principles of GDMs as optimizers, we demonstrate the ability to overcome prediction errors and validate the convergence of generated solutions to optimal solutions. Speaker: Dr. Mérouane Debbah is a Professor at the Khalifa University of Science and Technology in Abu Dhabi and founding Director of the KU 6G Research Center. He is a frequent keynote speaker at international events in the field of telecommunication and AI. His research has been lying at the interface of fundamental mathematics, algorithms, statistics, information and communication sciences with a special focus on random matrix theory and learning algorithms. In the Communication field, he has been 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 distinctions, prizes and best paper awards (more than 40 IEEE best paper awards) for his contributions to both fields and according to research.com he is ranked as the best scientist in France in the field of Electronics and Electrical Engineering. He is an IEEE Fellow, a WWRF Fellow, a Eurasip Fellow, an AAIA Fellow, an Institut Louis Bachelier Fellow, an AIIA Fellow, and a Membre émérite SEE. He is chair of the IEEE Large Generative AI Models in Telecom (GenAINet) Emerging Technology Initiative and a member of the Marconi Prize Selection Advisory Committee. Co-sponsored by: Artificial Intelligence & Machine Learning (AIML) Working Group Virtual: https://events.vtools.ieee.org/m/453702