Keynote Speakers
Prof. Adel Bouhoula
Arabian Gulf University, Bahrain
Formal Methods for Safe and Secure Critical Systems
Designing reliable and secure critical systems requires great care and precision, as errors in their development can lead to catastrophic consequences, especially in sectors such as medicine, finance, aviation, space, and defense.
For example, consider cybersecurity protocols that aim to secure communications on the Internet by relying on cryptographic primitives. These protocols are essential for various applications, including online shopping, bank transactions, electronic voting, and securing government communications and vital infrastructure. However, designing error-free cybersecurity protocols can be challenging as demonstrated by the discovery of a critical flaw 17 years after the original publication of the famous Needham-Schroeder public-key protocol. Even today, many flaws are still found in current cybersecurity protocols, resulting in financial losses and eroding user confidence. The analysis of cybersecurity protocols is very complex because the set of scenarios to consider can be infinite.
Thanks to their solid mathematical foundation, formal methods make it possible to manage endless possibilities, offering a powerful approach to validating cybersecurity protocols and, more generally, critical systems. This talk will explore how formal methods can be used for various applications, such as cybersecurity protocols and firewall configurations. Additionally, it will investigate the main challenges that formal method techniques face in the validation process and discuss ongoing efforts to overcome these challenges.
Biography
Prof. Bouhoula is a Senior Computer Scientist with thirty years of high-level academic and managerial achievements at several world-renowned institutions. He has previously held various prominent positions, including a Visiting Professor at the University of Tsukuba (Japan) for over a decade, a Visiting Researcher at the Mitsubishi Research Institute (MRI, Tokyo, Japan), and a Visiting Researcher at the Stanford Research Institute (SRI International, California, USA). Additionally, he served as a Senior Researcher at the French National Institute for Research in Digital Science and Technology (INRIA, Nancy, France), and the Chairman and CEO of the Research Institute for Computer Science and Telecommunication in Tunisia. Prof. Bouhoula owns three patents and has published over 200 articles in leading international journals, conferences, and refereed workshops. He has made significant contributions to prestigious A* conferences such as the International Joint Conference on Artificial Intelligence (IJCAI), Computer Aided Verification (CAV), and the ACM/IEEE Symposium on Logic in Computer Science (LICS). In addition, his research was published in numerous renowned journals, including Pattern Recognition, the Journal of Medical Systems and the ACM Transactions on Computational Logic. Prof. Bouhoula was invited to give seminars in renowned universities and laboratories worldwide, namely in France, the United Kingdom, Italy, Germany, Norway, Canada, China, the United States, and Japan.
Prof. Ali Ouni
University of Quebec, Montreal
The Next Generation of Data Driven AI solutions for Software Engineering
Modern software development has entered a mass production era with a multitude of engineering challenges in practice to provide higher performance and better functionality. To address these challenges, a growing trend has begun in recent years to move software engineering problems from human-based tasks to data driven machine-powered techniques. As a result, human effort is moving up the abstraction chain to focus on guiding the automated task, rather than performing the task itself. AI techniques such as machine/deep learning, optimization techniques and large language models (LLMs) have been applied to many problems in software engineering that span the spectrum of activities along the software lifecycle from requirements to maintenance and reengineering. Already, success has been achieved in requirements, refactoring, project planning, testing, maintenance and reverse engineering. However, several challenges have to be addressed to mainly tackle the growing complexity of software systems nowadays in terms of the number of objectives, constraints and inputs/outputs. This keynote discusses various software engineering challenges to develop, maintain and evolve modern large scale software systems. It presents some recent AI based solutions to support software maintenance and evolution activities. Finally, emerging software engineering challenges and the next generation of AI applications to software engineering will be discussed.
Biography
Ali Ouni is a Full Professor of computer science at École de technologie supérieure (ÉTS), Montreal, University of Quebec where he leads the Software Technology and Intelligence Research Lab (STIL). He obtained his PhD degree in computer science from the University of Montreal in 2014 with the best thesis excellence Award. He obtained his master’s degrees from ISIGK University of Kairouan in 2010, and his bachelor’s degree from ISSAT, University of Sousse in 2008. He has developed pioneering research work in the area of software engineering, software maintenance and evolution, software quality, and empirical software engineering. His research is centered around the use of advanced AI and data analytics techniques to address problems in software engineering involving software products, processes, and stakeholders. His work in these areas won several Distinguished/Best Paper Awards at top-tier conferences and has been done in collaboration with and/or adopted by major industrial software companies. He is recipient of several prestigious awards including, the Outstanding Early Career Computer Science Researcher Award from the Canada's national academic society for computer science (CS-Can / Info-Can), in 2023, the Research Excellence Award from the University of Quebec in 2023, and the Emerging Researcher Excellence Award from ÉTS Montreal in 2021.
Prof. Walid Maalej
Hamburg, Germany
Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing
AI and Software Engineering have co-evolved and profited from each other since their beginnings. During the last decade, Software Engineering has particularly profited from advances in Machine Learning and in Natural language Processing. By now, recommender systems, prediction models, and bots have become standard tools to support software engineering tasks: from requirements elicitation and documentation, to code generation and quality assurance.
The current decade will however be focusing on the opposite direction, i.e. how can AI profit from Software Engineering. This lecture will show how recent challenges faced by the Machine Learning, NLP, as well as the Data Science communities are primarily engineering challenges. The lecture lays out how traditional as well as modern Software and Requirements Engineering can help address these challenges: in order to increase the applicability, acceptance, and reliability of AI-based systems in practice.
Biography
Walid Maalej is an award-winning software & requirements researcher, passionate educator, and handicraft enthusiast. As Professor of Informatics at the University of Hamburg, he teaches software development basics to up to 700 students in a single course, using fun activities and pair-programming. His advanced software engineering courses address real challenges from industry and society by combining cutting-edge technology with a large portion of empiricism and communication skills. As head of informatics department, he successfully navigated the department through turbulent pandemic times including an exceptional budget cut. Prof. Maalej’s research interests include AI engineering, user involvement and feedback mining, sustainability in software, as well as knowledge creation and sharing in software projects. His work on these topics has been cited thousands of times and received a.o. the ACM SIGSOFT Distinguished Paper Award, IEEE RE Best Paper Award, MSR Most Influential Paper Award, as well as awards by Google and Microsoft. In 2014 Prof. Maalej was named “The Early Stage Scientist of the Year” by academics and the German Association of University Professors (DHV) across all disciplines. He currently serves as Steering Committee Chair of the Requirements Engineering conference series. Prof. Maalej has worked as developer and consultant with and for multiple companies and organizations including Siemens, Tata Consultancy Services, Rohde und Schwarz, and Telekom. He received his doctoral degree in informatics from TU Munich with distinction and is also a proud alumni of the Center for Digital Technology and Management.
Prof. Islem Rekik
Associate Professor, Imperial College London
AI and Brains: A Two-Way Journey
Artificial intelligence (AI) and neuroscience have long inspired each other. This keynote explores their evolving synergy: how brain-inspired AI models—from graph neural networks to multi-modal cognitive learning—advance our understanding of neurological disorders, and how the brain’s complexity drives innovations in robust, interpretable (reasoning-based), and fairness-aware AI. By examining this AI-Brain synergy, we reveal new opportunities for precision neurodiagnosis and the development of truly intelligent systems.
Biography
Islem Rekik is the Director of the Brain And SIgnal Research and Analysis (BASIRA) laboratory (http://basira-lab.com/) and an Associate Professor at Imperial College London (Innovation Hub I-X). She is the awardee of two prestigious international research fellowships. In 2019, she was awarded the 3-year prestigious TUBITAK 2232 for Outstanding Experienced Researchers Fellowship and in 2020 she became a Marie Sklodowska-Curie fellow under the European Horizons 2020 program. In 2025, she was the recipient of the prestigious “Tunisian AI Award” from the Tunisian AI Society (𝗧Λ𝗜𝗦), recognizing Top AI Pioneers and was featured in the I-X news as well as the Realites magazine. Together with BASIRA members, she conducted more than 100 cutting-edge research projects cross-pollinating AI and healthcare —with a sharp focus on brain imaging and network neuroscience. UG and PG Students under her supervision received 24 highly competitive academic honors, grants and awards. She is also a co/chair/organizer of more than 34 international first-class conferences/workshops/competitions (e.g., Affordable AI 2021-22, Predictive AI 2018-2024, Machine Learning in Medical Imaging 2021-24, WILL competition 2021-23). She delivered 40+ invited keynotes, talks, seminars and lectures including prestigious international conferences (MICCAI, CVPR, ISBI, Wellcome trust). She is a member of the organizing committee of MICCAI 2023 (Vancouver), 2024 (Marrakesh) and South-Korea (2025). She will serve as the General Co-Chair of MICCAI 2026 in Abu Dhabi. In addition to her 160+ peer-reviewed publications in top journals including IEEE TPAMI (IF: 20.8) and BMJ (IF: 93), she is a strong advocate of equity, diversity and inclusiveness in AI and research. She is the former President of the Women in MICCAI (WiM), and the co-founder (and now former President) of the international RISE Network to Reinforce Inclusiveness & diverSity and Empower minority researchers in Low-Middle Income Countries (LMIC)
Prof. Hervé PANETTO
University of Lorraine, France
AI in Industry 5.0: A Human-centric Perspective
Industry 5.0, also known as the Fifth Industrial Revolution, represents a new and emerging phase of industrialization. In this paradigm, humans collaborate alongside advanced technology and hybrid AI-powered Cyber-Physical systems to enhance workplace processes. Unlike its predecessor, Industry 4.0, which focused primarily on IoT, automation and data exchange, Industry 5.0 places a human-centric lens on production. Industry 5.0 emphasizes the wellbeing of workers, recognizing their central role in the production process. It seeks to empower employees, address evolving skills needs, and foster a positive work environment while providing prosperity beyond mere jobs and growth. It respects the planet’s production limits by using new technologies to create sustainable solutions. In the transition toward Industry 5.0, human-centricity emerges as a pivotal value. However, existing architectures often neglect safety, trustworthiness, and the human element. My talk will discuss about how cyber-physical systems can now move to cyber-physical-cognitive systems and cyber-physical-social systems that combine Hybrid Artificial Intelligence (including Active Learning, Forecasting, and Explainable AI), simulated and emulated reality, decision-making, and user feedback. The challenges and trends emerging trends in Industrial and Systems Engineering are now on fostering synergies between humans and machines, embracing an AI-powered future that empowers and collaborates with humanity
Biography
Dr. Hervé PANETTO is a Professor of Enterprise Information Systems at University of Lorraine. He teaches Information Systems modelling and development at TELECOM Nancy and conducts research at CRAN (Research Centre for Automatic Control), Joint Research Unit with CNRS where he is managing a research project on the use of neuro-symbolic AI for industry. He is working on the cyber-physical systems smart interoperability with neuro-symbolic techniques and cognitive digital twins. He is the main holder of the Research/Industry chair HUMAN-AI dealing with AI for human-systems interactions in Industry 5.0. He is expert at AFNOR (French National standardisation body), CEN TC310 and ISO TC184/SC4 and SC5. He participated in many European projects including IMS FP5-IST Smart-fm project (awarded by IMS) and the FP6 INTEROP NoE (Interoperability Research for Networked Enterprises Applications and Software). He is author or co-author of more than 300 papers in the field of Automation Engineering, Enterprise Modelling and Enterprise systems integration and interoperability. He is Editor-In-Chief of the Annual Reviews in Control, and member of various Editorial Boards, member of the Advisory Board of the Digital Twin International Journal (DTIJ), and the Regional Associate Editor Europe of the international Journal of Intelligent Manufacturing (JIM), Springer; He serves as Associate Editor of the Enterprise Information Systems (EIS) journal, Taylor & Francis, the Journal of Industrial Information Integration (JIII), Elsevier, the Engineering Applications of Artificial Intelligence (EAAI), Elsevier, and the Journal SN Computer Science (SNCS), Springer Nature. He has been elected Fellow of the Academia Europaea (Academy of Europe), Fellow of the AAIA (Asia-Pacific Artificial Intelligence Association) and Fellow of the AIIA (Artificial Intelligence Industry Alliance). He is IEEE senior Member.
Prof. Elise Bishoff
PNN Laborotory, Richland, WA, USA
Resilient Intelligent Systems: Ensuring Robustness and Reliability in AI Applications
As artificial intelligence continues to integrate deeply into various facets of modern life, the resilience and reliability of these intelligent systems have become paramount. The increasing complexity and autonomy of AI applications demand that these systems not only perform accurately under normal conditions but also maintain functionality in the face of unforeseen challenges and adversarial conditions. Focusing on this critical need, the presentation delves into the concept of resilient intelligent systems. It explores how robustness and reliability are being built into AI applications, ensuring they can withstand and adapt to a wide range of operational environments and potential disruptions. Key topics include the latest advancements in developing resilient AI models, techniques for enhancing robustness against adversarial attacks, and strategies for ensuring the reliability of AI-driven decisions. We will explore practical examples from industries such as finance, healthcare, and transportation and how they are implementing resilient intelligent systems. By examining both the technological innovations and the practical methodologies that are driving this field forward, the presentation provides deep insights into the importance of resilience in AI. Highlighting future trends and ongoing research, it underscores the necessity of continued eJorts to advance the robustness and reliability of AI applications, ensuring they remain eJective and trustworthy in an ever-evolving landscape.