Prof. Samson Lasaulc
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
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: Dr. Antonios Saravanos is Clinical (Full)
Professor of Information Systems at New York University
(NYU). He holds two doctorates, the first from Columbia
University (New York, USA) and the second from Bocconi
University (Milan, Italy), as well as graduate degrees from
the University of Oxford and the University of Cambridge in
the United Kingdom. A Senior Member of the Association for
Computing Machinery, Dr. Saravanos’s research examines the
drivers and barriers of technology adoption, trust, and
sustained engagement, with particular emphasis on
intelligent machines and “good tech”, examining the
influence of prosocial motivation and warm glow on user
intentions and continued use. He also collaborates on
applied AI projects that study learning dynamics in modern
models and develop predictive methods, including work in
medical imaging. In addition to his research, Dr. Saravanos
has played a significant role in curriculum development,
academic leadership, and faculty governance at NYU. He led
the creation of NYU’s first undergraduate "big data" degree,
the Bachelor of Science in Applied Data Analytics and
Visualization, served as its program coordinator from 2016
to 2022, and has been recognized for his efforts with the
NYU School of Professional Studies Outstanding Service Award
(2016) and the Teaching Excellence Award (2019).
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 institutions.