Prof. Samson Lasaulce
Khalifa University, UAE

Bio: Samson Lasaulce is a CNRS Director of Research with CRAN at Nancy. Over 2023-2025, he has been a Chief Research Scientist in AI with Khalifa University (KU), Abu Dhabi where he was also the holder of the TII 6G Chair on Native AI. He has been the holder of the RTE Chair on the Digital Transformation of Electricity Networks at CentraleSupélec. He has also been a Professor with the Department of Physics at Ecole Polytechnique. Before joining CNRS he has been working for five years in private R&D companies (Motorola Labs and Orange Labs). Dr. Lasaulce is the recipient of several awards such as the SEE Blondel Medal. Dr. Lasaulce has been serving as an Associate Editor for several journals such as the IEEE Transactions on Signal Processing and Springer Nature Discover Artificial Intelligence. His current research interests lie in distributed networks with a focus on game theory, information theory, learning, distributed optimization, network control for communication networks, energy networks, and social networks. He is a co-author of the book "Game Theory and Learning for Wireless Networks: Fundamentals and Applications".

Speech Title: "Towards LLMs on Device: The Case of LLM Model Compression"

Abstract: In this task we will briefly review possible techniques to deploy large language models on small devices such as mobile phones. We will put more emphasis on the most recent quantization and adaptation techniques.

 

Prof. Guillermo De Ita Luna
Autonomous University of Puebla, Mexico

Bio: 34 years as a Full Professor and researcher in the Computer Science department at the Autonomous University of Puebla. (BUAP), México. Currently, a member of the Mexican System of Researchers at level 3 (the highest level). Guillermo De Ita has made research stances in Texas A&M, Chicago University, Lille – Inria France, as well as several Universities in Mexico. He was the principal of the Computer Science Dept (BUAP) from 1999 to 2003. He designed the engineering program in computer science and participated as a founding member of the master's and doctoral programs in Computer Science at the Computer Science
Department from BUAP. He has supervised 59 thesis projects; 32 in bachelor ‘s level and 23 in posgrade level. He has published 140 research articles in journals and conference proceedings that underwent rigorous double-blind peer review, along with 30 book chapters. Additionally, he contributed as an author to the publication of 5 books.

 

Prof. Antonios Saravanos
New York University (NYU), USA

Bio: Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in the Distributed Data Intensive Systems Lab (DiSL), examining various aspects of Internet-scale big data powered artificial intelligence (AI) systems, algorithms and analytics, including performance, scalability, reliability, privacy and trust. Prof. Liu is an elected IEEE Fellow, a recipient of IEEE Computer Society Technical Achievement Award (2012), and a recipient of the best paper award from numerous top venues, including IEEE ICDCS, WWW, ACM/IEEE CCGrid, IEEE Cloud, IEEE ICWS. In addition to serving as program chairs of top venues, such as WWW, VLDB, ICDCS, ICDE, IEEE Cloud Computing, and associate editors or guest editors of over a dozen journals, Prof. Liu served as the editor in chief of several ACM or IEEE journals, including IEEE Transactions on Service Computing (2013-2016), ACM Transactions on Internet Computing (since 2019). Currently, Professor Liu is serving as the Editor in Chief of IEEE Transactions on Big Data (since Jan 2025). Prof. Liu is a frequent keynote speaker in top-tier venues in Big Data, AI and ML systems and applications, Cloud Computing, Privacy, Security and Trust. Her current research is primarily supported by USA National Science Foundation under CISE programs, CISCO and IBM.

 

Prof. Farid Nait-Abdesselam
Université Paris Cité, France

Bio: Farid Nait-Abdesselam is a Full Professor of Computer Science at Université Paris Cité. He received the State Engineering degree from the University of Science and Technology Houari Boumediene, Algeria, in 1993, an M.S. degree from Université René Descartes (now Université Paris Cité), France, in 1994, and a Ph.D. degree from Université de Versailles Saint-Quentin-en-Yvelines (now Paris-Saclay University), France, in 2000, all in Computer Science.
His research lies at the intersection of cybersecurity, networking, and distributed systems, with a focus on secure communication architectures, network resilience and optimization, intrusion detection, and adaptive defense mechanisms in complex, constrained, and heterogeneous environments. He has authored over 180 peer-reviewed publications, edited two scientific books, and contributed multiple book chapters on advanced topics including network security, malware forensics, and blockchain technologies. His work integrates theoretical modeling with experimental validation and real-world deployment across mobile, vehicular, drone-based, and large-scale networked systems.

 

Assoc. Prof. Ismail Bennis
University of Haute Alsace, France

Bio: Ismail Bennis earned in 2009 a bachelor's degree in mathematics and computer science from the Université Mohammed V in Rabat, Morocco. 2011, he received a master's degree in Computer Networks and Telecommunications from the same university. He completed his PhD in 2015 under joint supervision between the Université Mohamed V in Rabat, Morocco and the Université de Reims Champagne-Ardenne in France. From 2015 to 2017, he worked as a temporary professor for research and teaching (A.T.E.R) at the University of Reims. Between 2017 and 2020, he worked as an associate professor at La Rochelle University. His research interests include routing protocols with quality of service over wireless sensor networks, IoT and outlier detection. Since September 2020, he has worked as an associate professor at the University of Haute Alsace.

Speech Title: "Towards Smarter LoRaWAN Networks: Insights from Multi-Gateway Deployments, AI-Driven Optimization and V2X Applications"

Abstract: LoRaWAN has established itself as a leading LPWAN technology for large-scale IoT deployments. However, achieving reliable and scalable communications remains challenging in dense networks, urban environments, and emerging mobility scenarios. This talk presents a synthesis of our research contributions on LoRaWAN performance analysis and optimization. We discuss the impact of gateway deployment, traffic patterns, and protocol parameters on network performance, and present solutions developed to improve reliability, scalability, and resource allocation. The talk also introduces reproducible simulation tools designed to facilitate LoRaWAN experimentation and optimization. Finally, we explore new research directions, including AI-driven network optimization and the use of LoRaWAN in vehicular and intelligent transportation systems. The presentation concludes by highlighting the opportunities and open challenges that will shape the next generation of LPWAN-based communications.

 

Dr. Douglas Schmidt
William & Mary, USA

Bio: Dr. Douglas C. Schmidt is the Dean of the School of Computing, Data Sciences & Physics at William & Mary, where he leads initiatives at the intersection of artificial intelligence, software engineering, and institutional transformation. An internationally recognized researcher and educator, his work explores how generative AI reshapes software development, testing, and human-computer collaboration, with a particular focus on intent-driven and human-centered AI systems.
Before joining William & Mary, Schmidt held senior leadership and faculty roles at Vanderbilt University and Carnegie Mellon University’s Software Engineering Institute. He has also collaborated extensively with industry and government partners developing and testing large-scale, software-reliant systems. He is a frequent speaker, author, and advisor on the opportunities and risks of deploying AI in real-world organizations, education, and critical infrastructure. In his keynote, Schmidt examines how AI is not just automating tasks, but redefining expertise, agency, and the future of knowledge-driven institution.

 

Dr. Carlos Faouzi Bader
DataCom Lab of Huawei Fourier research center at Boulogne-Billancourt-Paris (France)

Bio: Carlos Faouzi Bader (IEEE SM’17) received the Ph.D. degree (Hons.) in telecommunications from the Universidad Politécnica de Madrid in Spain, in 2002. He joined the Centre Technològic de Telecomunicacion de Catalunya (CTTC), Barcelona-Spain, as Senior Research Associate from 2006 to 2013. Between June 2013-2019, he has been appointed as Associate Professor at CentraleSupélec (France). He has been involved in several European projects from the 5th–7th EC research. He has as honorary adjunct Professor with the University of Technology Sydney, in Australia. From he was pointed as the Head of the Signals and Communications Department, Institute of Electronics and Telecommunications of Rennes (IETR), France. His research has been lying at in the Communication field, he has been at the heart of the development of small cells (4G) and (5G) and Large Intelligent Surfaces, terahertz communication (6G) technologies. In the AI field, he is known for his work on Large Language Models for telecommunication systems and networks. For the period 2020-2021, he has been the Director of Research of the Institut Supérieur d’Électronique de Paris (ISEP) in Paris, France. From Dec 2021 to end 2024, he has been the Director of the Digital Telecom Unit at the Artificial Intelligence and Digital Science research Centre (AIDRC) at the Technology Innovation Institute (TII) in Abu Dhabi (UAE). Since Nov 2025, he is as Expert and team Leader of the Network for Intelligence at the DataCom Lab of Huawei Fourier research center at Boulogne-Billancourt-Paris (France). He has published more than 55 journals and 146 papers in peer-reviewed international conferences, more than 13 book chapters, and 5 edited books.