Prof.  Mérouane Debbah

Biography:  Mérouane Debbah is a researcher, educator and technology entrepreneur. Over his career, he has founded several public and industrial research centers, start-ups and is now Chief Researcher at the Technology Innovation Institute in Abu Dhabi. 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 35 best paper awards) 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 Electrical Engineering 

Mérouane Debbah is a former student in Algeria of Lycée Cheikh Bouamama (ex-Descartes, Algiers). After his classes préparatoires in Lycée Henri IV (Paris), he entered the École normale supérieure Paris-Saclay in 1996 and obtained his PhD degree in 2002. His Phd thesis focused on a mathematical framework called free probability theory for the design of wireless networks. He started his career at Motorola Labs in Saclay in 1999. He joined the Telecommunication Research Center of Vienna in 2002 as a senior researcher (ftw.). 

From 2003 to 2007, he was an assistant professor at Eurecom in Sophia-Antipolis. His work focused mainly on the mathematical foundations of communication networks with the development of random matrix theory methods and game theory methods for signal processing and wireless communications.

In 2007, he was appointed full professor at CentraleSupélec (campus of Gif-sur-Yvette) at the age of 31. At the same time, he founded and was director of the Alcatel-Lucent chair on Flexible Radio. This was the first industrial chair in telecommunication in France with close ties between CentraleSupélec and Bell Labs. The chair was at the heart of the development of the small cells and Massive MIMO technologies. The chair focused also on training top scientists and formed more than 45 Phd and Post-doc researchers, many of which have become leaders in the wireless communication society. By 2017, the telecommunication department of CentraleSupélec was ranked number one in France and number 2 in Europe During that period, the European Commission awarded him an ERC (European Research Council) grant on random complex networks and an ERC POC (Proof of Concept) on Wireless Edge Caching.

In 2014, he joined Huawei and founded the Huawei Mathematical and Algorithmic Sciences Lab in Boulogne-Billancourt, with a special focus on mathematical sciences applied to wireless, optical and networking communications. At the end of 2019, the lab established had more than 200 researchers and was considered as one of the very top places in the world for industrial R&D in the field of communication networks. The initial focus of the lab on 5G and polar codes was a massive win for the company, which had built up a significant patents position in the domain.

In 2019, in order to encourage more fundamental research and push the actual fundamental limits of the ICT industry, he founded the Lagrange Mathematics and Computing Research center in Paris.  The research center focused on the promotion of fundamental research on the foundations of Mathematics of Computing and Data Science, as well as to expand the horizons of the field by exploring other scientific disciplines through a computational and mathematics lens. The center, which hosted several Medal Fields, was built on a unique innovative structure model for industry, based on open long term research grants that support pioneering projects for top scientists.

In 2021, he joined the new Technology Innovation Institute in Abu Dhabi which aims to bring together top tier talent from across the globe to research and develop disruptive technological innovations for the benefit of science, the economy and the environment. He founded the AI and Digital Science Research Center with a focus on Telecommunications, AI, and Cyber-Security. In 2023, the center had more than 80 people and was pioneer in the development of Large Language Models with the Developpement of NOOR (largest language Model in Arabic with 10 billion parameters) released in April 2022 and Falcon LLM (upon its release, top ranked Open Source 40 Billion parameters Large Language Model ) released in March 2023. These two models have positioned the UAE as a global leader in the AI field.

In 2023, he was appointed full professor at Khalifa University in Abu Dhabi.

Tilte: Large Language Models for wireless: The Next Revolution 

Abstract: The evolution of generative artificial intelligence (GenAI) constitutes a turning point in reshaping the future of technology in different aspects. Wireless networks in particular, with the blooming of self-evolving networks, represent a rich field for exploiting GenAI and reaping several benefits that can fundamentally change the way how wireless networks are designed and operated nowadays. To be specific, large language models (LLMs), a subfield of GenAI, are envisioned to open up a new era of autonomous wireless networks, in which a multimodal large model trained over various Telecom data, can be fine-tuned to perform several downstream tasks, eliminating the need for dedicated AI models for each task and paving the way for the realization of artificial general intelligence (AGI)- empowered wireless networks. In this talk, we aim to unfold the opportunities that can be reaped from integrating LLMs into the Telecom domain. In particular, we aim to put a forward-looking vision on a new realm of possibilities and applications of LLMs in future wireless networks, defining directions for designing, training, testing, and deploying Telecom LLMs, and reveal insights on the associated theoretical and practical challenges