Keynote Speakers
Keynote Speakers
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Professor Faïez GARGOURI
Computer Science Professor at the Higher Institute of Computer Science and Multimedia, University of Sfax, Tunisia
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Professor Faïez Gargouri is a Professor of Computer Science at the Higher Institute of Computer Science and Multimedia of the University of Sfax, Tunisia, where he served as Director from 2014 to 2020 and from 2007 to 2011. He also directed the Multimedia, Information Systems and Advanced Computing Laboratory between 2011 and 2020.
From 2020 to 2024, he was the Vice President of the University of Sfax, responsible for training programs, pedagogy, and employability, and represented the university on the Scientific Council of the Virtual University of Tunisia. He currently chairs the University of Sfax Quality Committee, coordinates its PAQ-DGSU project, and founded PaqNews, a journal specializing in quality initiatives within higher education (DGSU and DGEFU programs).
Since September 2020, he has also served as a scientific consultant representing Tunisia to the European Union in the field of Information and Communication Technologies. Professor Gargouri has been actively involved in numerous international cooperation projects, including Tempus, Erasmus+, PRIMA, Twinning, CMCU, PRC, and UTIC.
He obtained his Master’s degree in Computer Science from the University of Paris 6 (1990) and his Ph.D. from the University of Paris 5 (1995). In 2002, he earned a University Habilitation in Computer Science from the University of Tunis, Tunisia.
His research interests cover various areas of information systems, including design, quality measurement, verification, data warehousing, multimedia, knowledge management, and ontology. He has published over 400 papers in journals and international conferences, along with more than ten books (pedagogical and conference proceedings).
Professor Gargouri serves on scientific and steering committees of major international conferences and journals. He is notably one of the founding members of the JFO Conference (French Workshops on Ontologies) and the ASD Conference (Decisional Systems Conference).
In 2024, together with Tunisian colleagues from around the world, he co-founded TunAISia, the Tunisia Chapter of the Association for Information Systems (AIS).
He is also the founder of the AIG (Association Informatique de Gestion), President of APPAD (Association du Patrimoine et des Propriétaires des Anciennes Demeures), and a Knight of the National Order of Merit in Education and Science (Decree No. 2009–2195 of July 16, 2009).
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Title: AI Ethics: Paradigms and Concepts
Abstract: In recent years, Artificial Intelligence (AI) has experienced a rapid and sometimes unstructured expansion across virtually all disciplines and sectors of activity. This pervasive diffusion, often driven by technological enthusiasm rather than ethical reflection, raises crucial questions regarding the boundaries, responsibilities, and values that should govern the design and use of intelligent systems. This presentation aims to explore the fundamental paradigms and emerging concepts of AI ethics, highlighting the necessity of establishing coherent and context-sensitive ethical frameworks. Particular emphasis will be placed on the notion of scientific integrity as a cornerstone for trustworthy and responsible AI, ensuring that innovation remains aligned with human values, transparency, and social accountability.
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Professor Björn W. SCHULLER
Full Professor of Artificial Intelligence and the Head of GLAM at Imperial College London/UK
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Professor Björn W. Schuller received his diploma, doctoral degree, habilitation, and Adjunct Teaching Professor in Machine Intelligence and Signal Processing all in EE/IT from TUM in Munich/Germany where he has been called as Full Professor of Health Informatics. He is also Full Professor of Artificial Intelligence and the Head of GLAM at Imperial College London/UK, Full Professor and Chair of Embedded Intelligence for Health Care and Wellbeing at the University of Augsburg/Germany, co-founding CEO and current CSO of audEERING – an Audio Intelligence company based near Munich and in Berlin/Germany, amongst other Professorships and Affiliations. Previous stays include Full Professor at the University of Passau/Germany, Key Researcher at Joanneum Research in Graz/Austria, and the CNRS-LIMSI in Orsay/France. He is a Fellow of the IEEE and Golden Core Awardee of the IEEE Computer Society, Fellow of the BCS, Fellow of the ELLIS, Fellow of the ISCA, Fellow and President-Emeritus of the AAAC, Elected Full Member Sigma Xi, and Senior Member of the ACM. He (co-)authored 1,200+ publications (50,000+ citations, h- index=100+), is Field Chief Editor of Frontiers in Digital Health and was Editor in Chief of the IEEE Transactions on Affective Computing amongst manifold further commitments and service to the community. Currently, he was awarded IEEE Signal Processing Society Distinguished Lecturer 2024.
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Title: Artificial Emotional Intelligence: Multimodal Pathways from Code to Character
Abstract: Artificial Emotional Intelligence (AEI) is increasingly moving from concept to computation, aiming to embed affective mechanisms at the very core of machine intelligence. Beyond recognizing or simulating human emotions, current research investigates also how artificial emotions can serve as internal states that guide decision-making, adaptation, and interaction. Likewise, the emerging notion of artificial neurotransmitters offers a neuro-inspired framework for modulating affective dynamics, providing machine agents with tunable regulation mechanisms comparable to those found in biological systems. This keynote will trace the multimodal pathways from code to character, focusing on how speech, language, vision, and physiological signals can be integrated with such affective architectures. It will discuss computational models that move beyond static labels toward dynamic emotion representations, examine how artificial neuro-inspired processes can enhance responsiveness and robustness, and outline the challenges of evaluation, interpretability, and ethical design. By grounding AEI in multimodal signal processing, affective computing, and computational neuroscience, the talk highlights both the technical advances and the open research questions. Rather than speculative visions of “machines that feel,” the focus is on current scientific progress toward emotionally adaptive, neuro-inspired AI systems that can support applications in digital health, education, and assistive technologies.
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Professor Marek CHODNICKI
Gdansk University of Technology, Poland
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Professor Marek CHODNICKI works at Gdansk University of Technology as a research and teaching assistant professor at the Institute of Mechanics and Machine Construction. He also serves as vice dean for cooperation of the Faculty of Mechanical Engineering and Ship Technology. He completed the postgraduate course "Manager of research and development projects - for scientists". He has experience in academic competence and industrial engineering. He has organized, supervised, and participated in the implementation of dozens of scientific and educational domestic and foreign projects. He is the project manager of the Horizon Europe project "New Approach to Innovative Technologies in Manufacturing". He is the director of the international second degree program "Engineering and Management of Space Systems." His research interests include: mechatronics, mechanical engineering, and modern industry.
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Title: AI Across the Line: Decoding Human Intent and Orchestrating Distributed Production
Abstract: Artificial intelligence serves as a bridge between what people want and how they behave in intelligent environments. On the human side, we examine how to infer intent from surface electromyography and additional sensors. This creates a low-latency interface for helping with upper-limb support and rehabilitation. On the systems side, we describe how learning can improve cloud manufacturing. This enables fair load balancing and flexible scheduling across various resources. These elements come together in a human-in-the-loop pipeline. This pipeline covers edge perception, cloud decision-making, and safe actuation. It also relies on digital twins, constraint handling, and mechanisms for clarity. We focus on being modular, adapting to different areas, and being strong against changing conditions. A research agenda outlines plans for real-time validation, generalization across sites, and ethics in design. This aims to create trustworthy Industry 5.0 systems that range from assistive devices to networked production systems.
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Professor Mohamed BAKHOUYA
International University of Rabat, Morocco
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Professor Mohamed BAKHOUYA is a professor of computer science at the International University of Rabat. He obtained his HDR from UHA-France in 2013 and his PhD from UTBM-France in 2005. He has more than ten years experiences in participating and working in sponsored ICT projects. He was EiC of IJARAS journal and also serves as a guest editor of a number of international journals, e.g.,ACM Trans. on Autonomous and Adaptive Systems, Product Development Journal, Concurrency and Computation: Practice and Experience, FGCS, and MICRO. He has published several papers in international journals, books, and conferences. His research interests include various aspects related to the design, validation, and implementation of distributed and adaptive systems, architectures, and protocols.
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Title: Blockchain-based IoT Platforms for smart Farming Systems
Abstract: Recent advances in pervasive technologies, such as wireless ad hoc networks and wearable sensor devices, allow the connection of everyday things to the Internet, commonly denoted as Internet of Things (IoT). IoT is seen as an enabler to the development of intelligent and context-aware services and applications. These services could dynamically react to the environment changes and users’ preferences. The main aim is to make users’ life more comfortable according to their locations, current requirements, and on-going activities. However, handling dynamic and frequent context changes is a difficult task without a real-time event/data acquisition and processing platform. Big data, WSN, and IoT technologies have been recently proposed for timely gathering and analysing information (i.e., data, events) streams. In this talk, we shed more light on the potential of these technologies for continuous and real-time data monitoring and processing in different real-case applications (e.g, Healthcare, energy efficient building, smart grid).
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Professor Hamed TAHERDOOST
University Canada West
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Professor. Hamed Taherdoost is faculty member of University Canada West. He holds PhD of Computer Science and Master of Information Security. He has over 20 years of experience in both industry and academic sectors. He has worked at international companies from Cyprus, the UK, Malta, Iran, Malaysia, and Canada and has been highly involved in development of several projects in different industries including oil and gas, healthcare, transportation, and information technology, holding positions as varied as Project Manager, R&D Manager, Tech Lead, and CTO. He has spent the last nine years helping start-ups to grow by implementing new projects and business lines. Apart from his experience in industry, he also has some achievements in academia. He has been a lecturer in three different parts of the world, Southeast Asia, the Middle East, and North America. Besides, he has organized and chaired numerous workshops, conferences, and conference sessions respectively, and has delivered speeches as chief guest and keynote speaker. Moreover, he is the editorial, reviewer, and advisory board member of some authentic peer-reviewed journals publishing with Taylor & Francis, Springer, Emerald, Elsevier, MDPI, EAI, & IGI Publishing, and Inderscience. Hamed has been an active multidisciplinary researcher and R&D specialist involved in several academic and industrial research projects. He has been working with researchers from various disciplines and has been actively engaged in different research studies. His research achievements also include winning several best paper awards and outstanding reviewer awards. His views on science and technology have been published in top-ranked scientific publishers such as Elsevier, Springer, Emerald, IEEE, IGI Global, Inderscience, Taylor and Francis and Dr. Hamed has published over 160 scientific articles in authentic peer-reviewed international journals and conference proceedings (h-index = 31; i10-index = 52; May 2022), ten book chapters as well as eight books in the field of technology and research methodology. He was the finalist for the Innovation in Teaching of Research Methodology Excellence Awards at ECRM, UK in 2022 and was nominated as the finalists in Southeast Asian Startup Awards in 2020 by Global Startup Awards and has been listed on the Stanford-Elsevier list of World’s top 2% of scientist by August 2021. He is a Certified Cyber Security Professional and Certified Graduate Technologist. He is senior member of IEEE, IAEEEE, IASED, and IEDRC and WGM of IFIP TC11 - Assurance and Information Security Management, and member of CSIAC), ACT-IAC, and AASHE. Currently, he is involved in several multidisciplinary research projects, including studying innovation in information technology, blockchain and cybersecurity, and technology acceptance.
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Title: Threat Intelligence and Machine Learning: A Powerful Combination for Cybersecurity
Abstract: Machine learning and threat intelligence combined provide a potent cybersecurity tool. While unstructured data can be analyzed with machine learning, threat intelligence entails gathering and evaluating data to foresee new assaults. Additionally, risk exposure assessment, alert management, raw data analysis, and cyber threat intelligence can all benefit from machine learning. It is imperative that each customer concentrates on the threat landscape that pertains to them, as the majority of the threat landscape is unimportant to most firms. By automatically generating a customized threat profile and making it easier for analysts to enrich that threat profile by providing them with AI-based natural language processing capabilities, the threat environment may be made more personalized. The question of whether open-source intelligence can be successfully incorporated into a practical method that reliably categorizes cyber threat intelligence can also be answered using machine learning. Machine learning and rule-based algorithms are used in the processing pipeline of the threat intelligence machine to convert unstructured data from open, technical sources into organized, useful intelligence. To strengthen cybersecurity, machine learning can also be utilized to visualize trends in CTI data. In summary, this speech discusses how threat intelligence and machine learning together can offer a strong basis for artificial intelligence (AI) solutions that can safeguard companies from online attacks.
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Professor Imad ZEROUAL
Moulay Ismail University, Morocco
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Prof Imad Zeroual is an Associate Professor of Computer Science at Moulay Ismail University in Meknes and is affiliated with the Business Intelligence and Systems Modeling research team at the Faculty of Sciences and Techniques in Errachidia. His research interests encompass artificial intelligence, with a particular focus on natural language processing, machine learning, information retrieval/extraction, and language teaching/learning. Prof. Zeroual has been invited as a guest speaker at various national and international research institutions, including KU Leuven (Katholieke Universiteit Leuven) and ICESCO (Islamic World Educational, Scientific and Cultural Organization). Additionally, he is the founding president of the Moroccan Center for Information Literacy (MorCIL) and a professional member of the International Association for Educators and Researchers (IAER) based in London, UK. He serves as an associate editor and reviewer for several esteemed journals within the artificial intelligence field. Furthermore, Prof. Zeroual has participated as an organizing and program committee member in numerous international conferences and symposiums, such as ICAISE, CoNLP4LTL, ACLing, and ICALP.
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Title: The Era of Large Language Models
Abstract: Large Language Models (LLMs) have emerged as one of the most significant advancements in artificial intelligence and natural language processing. These models are trained on vast datasets to understand and generate human-like text. They analyze patterns in language, enabling them to perform various tasks, such as text completion, translation, summarization, and even complex question-answering. One of the key features of LLMs is their ability to generate coherent and contextually relevant responses. By leveraging architectures like transformers, they can capture the nuances of language and understand context, which allows them to produce high-quality text that often mimics human writing. This capability opens up numerous applications across various fields, including customer service (through chatbots), content creation, education, and even programming assistance. However, LLMs are not without challenges. Issues such as bias in training data, the potential for generating misleading or harmful information, and concerns around privacy and security are critical considerations. Researchers and developers are actively working on strategies to mitigate these concerns, such as fine-tuning models, implementing better filtering mechanisms, and establishing ethical guidelines. The future of LLMs looks promising, with ongoing improvements in algorithms and computing power. As technology evolves, we can expect even more sophisticated applications that harness the power of LLMs, making them an integral part of our daily interactions with technology.
Expert Speakers
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Mr Firas KAROUI
CEO of U-Accelerator
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Mr. Firas Karoui is Entrepreneur & Innovation-Based Growth Expert who leverages innovation and creativity to inspire decision-makers and empower companies, projects, and organizations with new capabilities that drive performance and accelerate growth.
With over ten years of experience, he has worked with companies across diverse sectors to develop innovation programs and strategies, strengthen intellectual property protection, and attract international grants and investments. His expertise consistently delivers impressive results — reducing development costs by up to 50%, shortening development time by over 60%, and increasing company valuation by an average of 300%.
Mr. Karoui collaborates with large corporations, investors, governments, and universities to create partnerships fostering growth and scalability. He is an expert in structuring and delivering innovation and business consulting programs for leading organizations such as the European Union, United Nations agencies, GIZ, and Orange, across six countries in Europe, North America, and North Africa.
He also advises North American and European technology startups exploring new market opportunities in the Middle East and Africa (MEA region) and serves as an advisor to several African incubators.
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Title: Accelerating the Ecological Transition through Innovation and Technology Transfer: Synergies between AI and Greentech for a Sustainable Future
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Mr Walid CHEBBI
CEO of TDS Tunisia
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Mr. Walid Chebbi is the founder & CEO of TDS Engineering Solutions and expert in Industrial Automation & Industry 4.0
He holds an Engineering degree in Electrical Engineering (2000) and a DEA (Master’s) in Automation and Industrial Computing (2001). He began his academic career as a university assistant, specializing in programmable logic controller (PLC) programming and industrial automation.
In 2006, driven by the lack of practical equipment in university laboratories, he founded TDS, a company dedicated to the design and manufacturing of didactic models and test benches for Electrical Engineering disciplines. Over the past 15 years, TDS has developed more than thirty innovative educational and industrial products across electronics, control systems, automation, photovoltaics, mechatronics, and embedded systems — empowering hands-on engineering education and industrial training.
In recent years, through collaborative missions in Germany with GIZ and the Sfax International Association, Mr. Chebbi has expanded his focus toward Industry 4.0, developing smart visual management solutions for manufacturing workshops. Currently, he leads projects on Industry 4.0 demonstrators for universities, bridging the gap between academic learning and emerging industrial technologies.
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Title: Industrial applications of Artificial Intelligence
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