Keynote Speakers
Prof. Azzedine Boukerche
FIEEE, FCAE, FEiC, FAAAS, FAAIA
Distinguished University Professor, Senior Canada Research Chair Tier-1
School of Electrical Engineering and Computer Science, University of Ottawa, Canada
Leveraging Urban Computing with Smart Internet of Drones for Smart and Sustainable Megacities
Urban computing (UC) is an interdisciplinary field that seeks to improve people's lives in urban areas. To achieve this objective, UC collects and analyzes data from several sources. In recent years, the Internet of Drones (IoD) has received significant attention from the academia community and has emerged as a potential data source for UC applications.
The goal of this talk is to examine how IoD can connect and leverage UC in variety of applications which include: public safety and security, environment, traffic improvement, drone-assisted networks, just to mention a few. In this context, data acquired by IoD can fill gaps in data collected from other sources and provide new data for UC considering the aerial view of drones.
We shall first introduce the relationship between the concepts of UC and IoD, and then discuss our proposed general framework considering the perspective of IoD for UC followed by design guidelines of the Internet of drones' location privacy protocols. Last but not least, we shall discuss some key challenges in this emerging area.
Azzedine Boukerche is a Distinguished University Professor and holds a Senior Canada Research Chair Tier-1 position at the University of Ottawa, Canada. He is a Fellow of IEEE, a Fellow of the Canadian Academy of Engineering, a Fellow of the Engineering Institute of Canada, and Fellow of American Association for the Advancement of Science, and a Fellow of Asia-Pacific Artificial Intelligence Association.
He is the founding director of the PARADISE Research Laboratory, and DIVA Strategic Research Network at the School of Electrical Engineering and Computer Science (EECS), Ottawa. Prior to this, he held a faculty position at the University of North Texas, and he was a senior scientist at the Simulation Sciences Division, Metron Corp., San Diego. He spent a year at the JPL/NASA California Institute of Technology, where he contributed to a project centered on specification and verification of the software used to control interplanetary spacecraft operated by JPL/NASA Laboratory.
His current research interests include Intelligent transportation, Connected and Autonomous vehicles, IoT, Wireless sensor networks, wireless networks, wireless multimedia, performance evaluation and modeling of large-scale distributed systems, distributed and urban computing, and distributed and parallel simulation. He has published several research papers in these areas, and he received about 18 Best research paper awards from Top-Tier ACM/IEEE Conferences.
He has been serving as an Associate Editor of several journals, ACM Computing Surveys, IEEE Transactions on Distributed Systems, IEEE Transactions on Cloud Computing, IEEE TVT, Elsevier Ad-Hoc Networks, Wiley International Journal of Wireless Communication and Mobile Computing, Wiley Security and Communication Network Journal, Elsevier Pervasive and Mobile Computing Journal, IEEE Wireless Communication Magazine, and Elsevier Journal of Parallel and Distributed Computing.
He was the recipient of the Premier of Ontario Premier Research Excellence Award, the Ontario Distinguished Researcher Award, the George Glinski Research Excellence Award, the IEEE CS Golden Core Award, the IEEE Canada Gotlieb Medal Award, the IEEE ComSoc Exceptional Leadership Award, the IEEE TCPP Exceptional Technical and Leadership Award, the IEEE ComSoft Exceptional Technical Achievement Award, and the IEEE ComSoc IoT AHSN Technical and Research Achievement Award.
He served as the General Chair for numerous IEEE/ACM sponsored International Conference. He is a Steering Committee chair for the IEEE Transactions on Sustainable Computing, and several IEEE/ACM conferences. He has served as Vice Chair of the IEEE CS Fellow Committee, and the IEEE VT Fellow Committee. He also served on the Hiring committee for EiC for ACM Computing Surveys, and IEEE Transactions on Sustainable Computing. He was the past EiC for ACM-ICPS. He is also the author of the textbooks on Ad hoc Networking, Wireless Sensor Networks and Wireless Communication and Mobile Computing.
Dr. Genoveva Vargas Solar
French Council of Scientific Research (CNRS), France
GALILEAN: Graph Analytics Workflows Enactment on Just in Time Architectures
This talk presents GALILEAN, a Just-in-Time Architecture for the enactment of graph analytics workflows across heterogeneous computing resources. Executing end-to-end graph pipelines spanning data ingestion, graph construction, iterative analytics, and graph learning requires orchestrating data processing, transmission, and sharing across CPUs, GPUs, FPGAs, and TPUs, often hosted in disjoint cloud, edge, and HPC environments.
Existing "one-size-fits-all" platforms typically externalise all tasks to a fixed backend, ignoring where each graph operation is most efficient or sustainable to execute. GALILEAN extends the JITA-4DS vision to graph analytics, offering a composable, cross-layer management system that is aware of both workflow characteristics (e.g., traversal intensity, graph sparsity, and graph dynamism) and infrastructure capabilities.
By vertically integrating the application, middleware/OS, and hardware layers, GALILEAN dynamically assembles virtual data centres and allocates just-in-time resources for graph workloads, meeting changing requirements in performance, availability, and energy consumption. The talk will discuss open challenges, present the GALILEAN architecture and resource management techniques, and show experimental results on scheduling and enacting complex graph analytics workflows.
Genoveva Vargas Solar is a French Council of Scientific Research (CNRS) principal researcher. She is a member of the DataBase group of Laboratory on Informatics on Image and Information Systems (LIRIS). She is a regular member of the Mexican Academia of Computing (AMEXCOMP). Her education includes two PhDs and two master's degrees respectively in Computing Science and Compared Literature (Mythocritics and mythanalysis) from University of Grenoble, and several certificates on feminist and gender studies from the National Autonomous University of Mexico (UNAM).
Genoveva Vargas-Solar is a gender equity officer of the Gender Equity Commission at the LIRIS lab and the Federation of Informatics in Lyon. She represents EDBT Endowment (a major European conference in databases) in the D&I database interconference initiative that she leads. She is a member of the Tierra Com¨²n activist group and participates in the European project Gender STI as part of the CNRS partner group. She is a member of the executive boards of database conferences like EDBT, SIGMOD, ADBIS, AMW and BDA.
She contributes to the construction of service-based database/data science management systems. The objective is to design data science workflows, new queries, and enactment services guided by Service Level Objectives (SLO). Her work mainly addresses data science queries exploiting graphs. She proposes query evaluation methodologies, algorithms, and tools for composing, deploying, and executing data science functions on just in time architectures (disaggregated data centres). She conducts fundamental and applied research activities for addressing these challenges on different architectures ARM, raspberry, cluster, cloud, and HPC.
Prof. Giancarlo Guizzardi
University of Twente, The Netherlands
Semantic Models for Trustworthy Systems: A Hybrid Intelligence Augmentation Program
Cyber-human systems are formed by the coordinated interaction of human and computational components. In this talk, I will argue that these systems can only be designed as trustworthy systems if the interoperation between their components is meaning preserving. For that, we need to take the challenge of semantic interoperability between these components very seriously.
I will discuss a notion of trustworthy semantic models and defend its essential role in addressing this challenge. Finally, I will advocate that engineering and evolving these semantic models as well as the languages in which they are produced require a hybrid intelligence augmentation program resting on a combination of techniques including formal ontology, logical representation and reasoning, crowdsourced validation, and automated approaches to mining and learning.
Giancarlo Guizzardi is a Full Professor of Software Science and Evolution as well as Chair and Department Head of Semantics, Cybersecurity & Services (SCS) at the University of Twente, The Netherlands. In Twente, he also co-founded the NeXAI Cluster on AI. He has also been a Guest Professor at Stockholm University (Sweden), the Technical University of Vienna (Austria), and the University of Trento (Italy).
He has been active for nearly three decades in the areas of Formal and Applied Ontology, Conceptual Modeling, and Information Systems Engineering, working with a multidisciplinary approach in Computer Science that aggregates results from Philosophy, Cognitive Science, Logics and Linguistics. He is the main contributor to the Unified Foundational Ontology (UFO) - the incoming ISO/IEC 21838-5, and to the OntoUML modeling language.
Over the years, he has delivered keynote speeches in several key international conferences in these fields (e.g., ER, CAiSE, BPM). He is currently an associate editor of a number of journals including Applied Ontology and Data & Knowledge Engineering, a co-editor of the Lecture Notes in Business Information Processing series, and a member of several international journal editorial boards.
He is currently the Chair of the Steering Committee of the International Conference on Conceptual Modeling (ER), a member of the Steering Committees of CAiSE, EDOC, and IEEE CBI, and a member of the Advisory Board of the International Association for Ontology and its Applications (IAOA). Finally, he is an ER fellow.
Prof. Ali Kashif Bashir
Manchester Metropolitan University, UK
Artificial Intelligence to achieve predictive analysis for the transformation of Industry 4.0
Industry 4.0 is revolutionising manufacturing and industrial processes through automation, connectivity, and real-time data analytics. Artificial Intelligence (AI) plays a crucial role in this transformation by enabling predictive analysis to optimise operations, reduce downtime, and enhance decision-making. This talk explores how AI-driven predictive models leverage machine learning, IoT data, and big data analytics to anticipate failures, improve efficiency, increase security, and drive innovation.
In this talk, we will further explore a few real-time projects, their challenges, and best practices for implementing AI-powered predictive analytics, and learn how AI can be used to achieve the goal of complete intelligent systems in Industry 4.0 and explore their competitive advantages.
Ali Kashif Bashir (Senior Member, IEEE) is Chair Professor of Computer Networks and Cybersecurity at Manchester Metropolitan University, UK. At Manchester Met, he leads the SISTEMS: Secure and Intelligent Systems Research Group, Future Networks lab, the Turing Network's AI Safety and Security Taskforce, and the cybersecurity pathway's line management.
He holds honorary and adjunct professor positions at universities in the US, Canada, China, Malaysia, India, Pakistan, and has advised businesses, government bodies, and companies such as IBM, Siemens, and Samsung. He has collaborated with Nippon Telegraph and Telephone (NTT), Japan; KEPCO, South Korea; ITER, South Korea; Telecom, etc., on world-leading initiatives. He completed his PhD from Korea University (QS#67) in Wireless Communication in 2012.
Throughout his career, Ali has published over 450 technology-leading articles and received citations over 17.5K, and h-index of 70. He has also invented several products and patents; managed 10 million in strategic funds; obtained over 4 million in additional funding from several government bodies; delivered over 100 keynote speeches and chaired 50+ conferences and workshops.
He is a member of several technical societies and a Distinguished Speaker of ACM; he received the Clarivate Highly Cited Researcher Award in 2023 and 2024; listed as an IEEE Featured Author in 2021. He is the EIC of IEEE Technology, Policy and Ethics, and Senior Editor of IEEE Transactions on Consumer Electronics, Deputy EIC of IEEE Transactions on Consumer Electronics Letters, and AE of several reputed journals, such as IEEE TNSE (received excellent editor award, 2024), and Ethics and Plagiarism Investigation Committee member of a few IEEE Journals.
Prof. Tatiana Kalganova
Brunel University, UK
Emerging technologies in dynamic Intelligent scheduling and optimization for industrial IoT using sustainable AI
The convergence of Industrial IoT (IIoT), software-defined systems, and sustainable artificial intelligence is reshaping the future of smart manufacturing and adaptive industrial ecosystems. This keynote explores how dynamic intelligent scheduling and optimization can leverage energy-efficient AI paradigms, including Aphids-driven ant colony algorithms. In addition, we will consider how spiking neural networks (SNNs) and low-precision arithmetic, to deliver scalable and sustainable solutions. Drawing on recent research in Green AI, and neuromorphic computing, we examine strategies for reducing computational overhead while maintaining reliability in dynamic environments. By integrating IoT-enabled intelligent scheduling with adaptive software-defined architectures, this talk outlines a roadmap for achieving energy-aware, secure, and resilient industrial operations, paving the way for next-generation smart factories.
Prof. Tatiana Kalganova BSc (Hons), PhD, is a Professor of Intelligent Systems at Brunel. She has over 25 years of experience in design and implementation of applied Intelligent Systems. She led the Brunel team to success in collaboration with Supply Dynamics and Blue roofs, becoming a Champions of 2019 AFWERX Microelectronics Supply Chain Provenance Challenge.
Her novel ensemble-based deep learning approaches are ranked No 1 between 2020-2022 in terms of the number of trainable parameters at paperswithcode for MNIST dataset and lately ranked No 2 overall. Her work on hybridisation of LSTM and capsules is ranked No 1 for change point and outlier detection for SKAB dataset.
Her research into Ant Colony Optimization (ACO) and graph mathematics have been deployed into Caterpillar's current GEMSTONE supply chain optimization process. This invention led to multiple internal and external international awards, including the 2016 Caterpillar Chairman's Award for Process/Business Innovation, the 2016 Global Excellence in Analytics Award by the International Institute of Analytics, 2017 Finalist for the INFORMS Innovation in Analytics prize and 2020 Winner of Research Impact Awards.
Inventor of 6 patented technologies and author of over 120 peer-refereed papers in highly respect journals and in conference proceedings, Prof. Kalganova's work has attracted in research funding from a combination of industrial, national and international research entities. She serves on the editorial board for several journals, including Genetic Programming and Evolvable Machines and member of EPSRC Full College.
Prof. Angela Bonifati
Reasoning over Property Graphs: Leveraging Large Language Models for Automated Data Consistency
Graph data structures are foundational for modeling complex relationships across a wide range of domains, including the life sciences, social media, healthcare, finance, security, and planning. Property graphs, in particular, have emerged as a dominant paradigm for encoding semantic relationships due to their expressiveness and flexibility. However, the increasing adoption of graph databases has intensified the need for robust mechanisms to ensure data quality and consistency. Traditional consistency maintenance techniques—such as domain-specific rules and mined constraints like functional and entity dependencies—face significant limitations in scalability, adaptability to evolving data, and interpretability by non-experts. In this talk, I will discuss the emerging role of Large Language Models (LLMs) as a tool for automatically discovering and refining consistency constraints in property graphs through guided natural language prompts. By bridging symbolic graph representations with the reasoning capabilities of LLMs, I will also pinpoint promising directions for automating integrity management in graph systems, along with a wide array of graph-based reasoning tasks.
Angela Bonifati is a Distinguished Professor of Computer Science at Lyon 1 University and at the CNRS Liris research lab, where she leads the Database Group. She is also an Adjunct Professor at the University of Waterloo in Canada from 2020 and a Senior member of the French University Institute (IUF) from 2023.
Her current research interests are on several aspects of data management, including graph databases, knowledge graphs, data integration and their applications to data science and artificial intelligence. She has co-authored more than 200 publications in top venues of the data management field, including five Best Paper Awards, two books and an invited paper in ACM Sigmod Record 2018.
She is a recipient of an ERC Advanced Grant 2024 dedicated to leading researchers in Europe. She is the recipient of the VLDB Women in DB Research Award 2025 and of the IEEE TCDE Impact Award 2023 as well as a co-recipient of an ACM Sigmod Research Highlights Award 2023. She is the General Chair of VLDB 2026 and has served as the Program Chair of IEEE ICDE 2025, ACM Sigmod 2022 and EDBT 2020. She is currently an Associate Editor for the Proceedings of VLDB Vol. 19 and for IEEE TKDE and ACM TODS. She is the President of the ACM SIGMOD (2025-2029) and was the President of the EDBT Executive Board and Association (2020-2024). She is a member of the IEEE Technical Committee on Data Engineering (2024-2029) and a member of the PVLDB Board of Trustees (2024- 2029).
