Keynote Speakers

Mathias Fink

Mathias Fink

Professor, ESPCI Paris

Mathias Fink is the George Charpak Professor at the Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris) where he founded in 1990 the Laboratory “Ondes et Acoustique” that became in 2009 the Langevin Institute. He is member of the French Academy of Science and of the National Academy of Technologies of France. In 2008, he was elected at the College de France on the Chair of Technological Innovation. He has received several scientific awards as the Silver Medal of CNRS, the CNRS Medal of innovation, the High Prize Yves Rocard and the Louis Neel Prize of the French Physical Society, the Helmholtz-Rayleigh Award of the Acoustical Society of America, the Rayleigh Award of the IEEE Ultrasonics Society and the Edwin H. Land Medal of the Optical Society of America. Mathias Fink’s area of research is concerned with the propagation of waves in complex media. His current research interests include wave control in complex media, time-reversal in physics, ultrasound medical imaging, multi-wave imaging, wireless communications, metamaterials, time-varying medium, super-resolution. With his colleagues, he pioneered different inventions in medical imaging (ultrafast ultrasonic imaging, matrix Imaging, transient elastography, shear wave elastography) and in the field of telecommunications (Time-reversal processing and Reconfigurable intelligent surfaces). 7 start-up companies with more than 500 employees have been created from his research (Echosens, Sensitive Object, Supersonic Imagine, Time Reversal Communications, CardiaWave, Austral DX and GreenerWave).

Keynote Title: Towards ultrawideband Intelligent Surfaces: From Acoustics to Electromagnetism

Abstract: RIS is proposed as a new paradigm for 6G. Today, RIS technology is essentially narrowband and uses tunable metasurfaces made of several hundred pixels with a local reflection coefficient arbitrary controlled with a varying phase. Such an approach is sufficient when dealing with a signal bandwidth of the order of the coherence bandwidth where the channel can be considered flat. However, when dealing with ultrawideband signals, a unique phase control for each pixel is not always enough and ones have to extend the RIS concept. The same problem occurs also in the field of multi-user sound communications in reverberating environment with acoustic reconfigurable intelligent surfaces, Here, the reconfigurable surface needs to cover a wide range of frequencies spanning several decades. We will address this problem for 2 different scenarios. One approach is designed for multiple carrier frequencies communication and used a RIS made of several hundred electronically-controlled adjustable Helmholtz resonators. The second approach is designed for transmitting broadband continuous spectra and it used feedback metasurfaces where each elementary cell works as a reconfigurable spatio-temporal filter mimicking a double time-reversal process. The extension of these concepts in the microwave range will be discussed.




Marios Kountouris

Marios Kountouris

Professor, EURECOM, France

Marios Kountouris is a Professor at the Communication Systems Department, EURECOM, France. Prior to his current appointment, he has held positions at CentraleSupélec, France, Huawei Paris Research Center, France, the University of Texas at Austin, USA, and Yonsei University, S. Korea. He received a diploma degree in electrical and computer engineering from the National Technical University of Athens (NTUA), Greece in 2002, and the M.S. and Ph.D. degrees in electrical engineering from Télécom Paris, France in 2004 and 2008, respectively. He is the recipient of a Consolidator Grant from the European Research Council (ERC) in 2020 on goal-oriented semantic communications. He has served as Editor for the IEEE Transactions on Wireless Communications, the IEEE Transactions on Signal Processing, and the IEEE Wireless Communication Letters. He has received several awards and distinctions, including the 2022 Blondel Medal, the 2020 IEEE ComSoc Young Author Best Paper Award, the 2016 IEEE ComSoc CTTC Early Achievement Award, the 2013 IEEE ComSoc Outstanding Young Researcher Award for the EMEA Region, the 2012 IEEE SPS Signal Processing Magazine Award, the IEEE SPAWC 2013 Best Paper Award, and the IEEE Globecom 2009 Communication Theory Best Paper Award. He is an IEEE Fellow, an AAIA Fellow, and a Professional Engineer of the Technical Chamber of Greece.

Keynote Title: Goal-oriented Semantic Communication for Distributed Edge Intelligence - How Semantic is Semantic Communication?

Abstract: As we are entering the era of hyperconnected intelligence, a fundamental paradigm shift is necessary to satisfy the pressing requirements for real-time communication, autonomous decision-making, and efficient distributed processing. In this talk, we introduce goal-oriented communication, in which the semantics of information, i.e., the significance and the usefulness of messages with respect to the goal of data exchange, is at the core of the communication process. Future communication systems are evolving to cater to cyber-physical and mission-critical interactive systems, such as swarm robotics, self-driving cars, and smart Internet of Things (IoT). The interconnection of myriad sensing- and learning-empowered devices will underpin the global functioning of our societies, enabling formidable progress in various sectors. The realization of this euphoric dream however hinges upon networks’ ability to deliver on an unprecedented number of highly demanding requirements. As we are entering the era of networked intelligence, fundamental advances are necessary to satisfy the pressing requirements for real-time communication, timely autonomous decision-making, and effective distributed processing. In this talk, we introduce goal-oriented semantic communication; a paradigm shift that aims at redefining data importance, timing, and effectiveness, looking through the prism of the semantics of information. We highlight fundamental concepts, essential principles, and key functionalities required for effectively conveying only information representations and features, which are timely, relevant, and valuable for achieving end users’ goals. We discuss several recent theoretical results in the realm of rate-distortion-perception, timely source coding, pull-based communication, and real-time tracking. We conclude this talk by discussing the potential and the technical challenges associated with this promising avenue of research. Future communication systems will cater to emerging cyber-physical and decentralized interactive systems, such as swarm robotics, self-driving vehicles, and smart IoT. The interconnection of myriad sensing- and learning-empowered devices will underpin the global functioning of our societies, enabling formidable progress at various sectors. The realization of this euphoric dream however hinges upon networks’ ability to deliver on an unprecedented number of highly demanding requirements. As we move into the era of distributed networked intelligence, a fundamental paradigm shift is necessary to satisfy the pressing needs for reliable real-time communication, autonomous decision-making, and timely distributed processing. In this talk, we introduce effective goal-oriented communication, which entails an application/context-dependent, non-separable optimization of information generation, transmission, and utilization, via the semantics of information, i.e., the significance and the usefulness of messages with respect to the goal of data exchange. We highlight fundamental concepts, essential principles, and key functionalities required for effectively conveying only information representations and features, which are timely, relevant, and valuable for achieving end users’ goals. Finally, we discuss the potential and the technical challenges associated with this promising avenue of research.




Marcel Worring

Marcel Worring

Professor, University of Amsterdam

Marcel Worring is a full professor in the Informatics Institute of the University of Amsterdam where he leads the MultiX research group. He is co-founder of the Innovation Center for Artificial Intelligence in which he is co-directing the AI4Forensics lab, the AI for Medical Imaging lab, and the National Police Lab AI. In addition, he was initiator of the AI4Fintech program of the University of Amsterdam.

Keynote Title: From data to insight using multimedia analytics

Abstract: Multimedia data is everywhere and a rich source of information. Recent progress in large-scale neural networks has boosted the performance of automatic understanding of multimedia data and allows the creation of images and text by prompting. For basic tasks, employing such models will soon become a standard way of working. Many professionals, however, require advanced insights in multimedia data and for that current AI tooling is far from sufficient. In this presentation, we argue that for insight in multimedia data, analysis, visualization, and mining need to come together in multimedia analytics solutions. We will present a general framework for multimedia analytics and illustrate it with a number of example systems developed for professionals with an emphasis on hypergraph and prototype based data representations.




Sören Auer

Sören Auer

Director TIB – Leibniz Information Centre for Science and Technology, Germany

Following stations at the universities of Dresden, Ekaterinburg, Leipzig, Pennsylvania, Bonn and the Fraunhofer Society, Prof. Auer was appointed Professor of Data Science and Digital Libraries at Leibniz Universität Hannover and Director of the TIB in 2017. Prof. Auer has made important contributions to semantic technologies, knowledge engineering and information systems. He is the author (resp. co-author) of over 200 peer-reviewed scientific publications. He has received several awards, including an ERC Consolidator Grant from the European Research Council, a SWSA ten-year award, the ESWC 7-year Best Paper Award, and the OpenCourseware Innovation Award. He has led several large collaborative research projects, such as the EU H2020 flagship project BigDataEurope. He is co-founder of high potential research and community projects such as the Wikipedia semantification project DBpedia, the Open Research Knowledge Graph ORKG.org and the innovative technology start-up eccenca.com. Prof. Auer was founding director of the Big Data Value Association, led the semantic data representation in the Industrial/International Data Space, is an expert for industry, European Commission, W3C, the German National Research Data Infrastructure (NFDI) and the European Open Science Cloud (EOSC).

Keynote Title: Towards Neuro-Symbolic AI with Knowledge Graphs and Large Language Models

Abstract: In this talk, we delve into the cutting-edge realm of Neuro-Symbolic Artificial Intelligence (AI), focusing on the synergistic integration of Knowledge Graphs and Large Language Models. Neuro-Symbolic AI represents a transformative approach that combines the robust, interpretable reasoning capabilities of symbolic AI with the adaptive, data-driven strengths of neural networks. Our discussion will illuminate how this fusion offers a promising pathway towards more intelligent, explainable, and reliable AI systems. As a showcase of our approach towards neuro-symbolic AI we will demonstrate the Open Research Knowledge Graph. The transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based-formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research and innovation is seriously weakened. We argue for representing research contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. The Open Research Knowledge Graph (ORKG) is a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques leveraging Large Language Models. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches. Further possible intuitive access interfaces to such scholarly knowledge graphs include domain-specific (chart) visualizations or answering of natural language questions.