InnoComp 2026 Invited Speakers
Prof. Dr. El-Ghazali Talbi
El-Ghazali Talbi is a full Professor at the University of Lille. His research interests include metaheuristics, computational intelligence, parallel and distributed optimisation, learning-based optimisation, and neuromorphic computing. He has authored more than 250 international publications, including journal and conference papers, and has delivered 52 keynotes and tutorials. With a h-index of 67 and over 24,000 citations, he is globally recognised for his contributions to computational intelligence and large-scale optimisation.
Brain-Inspired Optimization: Computational Intelligence in Neuromorphic Systems
Abstract: Neuromorphic computing introduces spiking dynamics and event-driven efficiency into the field of optimization, offering a fundamentally different computational substrate. Evolutionary and Swarm Intelligence algorithms, long recognized for their flexibility and robustness, can now be re-envisioned within this brain-inspired paradigm.
In this talk, we first outline the theoretical foundations of neuromorphic metaheuristics, examining their motivations, taxonomy, and inherent trade-offs. We then present two neuromorphic evolutionary computation frameworks that illustrate how population-based search can be implemented through spiking dynamics. Finally, we explore practical applications using open-source tools, demonstrating how to design, execute, and analyze neuromorphic optimization experiments.
Overall, the presentation highlights how spiking computation can drive a new generation of computational intelligence–based optimization methods, paving the way for impactful applications in robotics, IoT, and embedded intelligent systems.
Prof. Dr. Christian Esposito
Christian Carmine Esposito is an Italian computer scientist and Associate Professor at the University of Salerno, specializing in distributed systems, cybersecurity, and emerging technologies such as blockchain and the Internet of Things. He earned his PhD in Computer Engineering from the University of Naples Federico II and has built a strong academic career through roles in both academia and research institutions, including the Italian National Research Council. Esposito has authored over 100 scientific publications and actively contributes to the international research community as a reviewer, editor, and program committee member for major journals and conferences. His work focuses on dependable and secure infrastructures, data modeling, and advanced networked systems, positioning him as a recognized expert in modern distributed computing environments and an influential speaker in the field.
Quantum-Enhanced Vigilance: Machine Learning for Anomaly Detection in the NISQ Era
Abstract: Quantum Machine Learning (QML) introduces superposition, entanglement, and high-dimensional Hilbert spaces into the field of pattern recognition, offering a fundamentally different computational substrate for identifying irregularities. Anomaly detection algorithms, long recognized for their critical role in cybersecurity, finance, and system health monitoring, can now be re-envisioned within this quantum-inspired paradigm.
In this talk, we first outline the theoretical foundations of quantum anomaly detection, examining its algorithmic motivations, taxonomy, and inherent trade-offs between classical and quantum approaches. We then present two QML frameworks—focusing on quantum variational circuits and quantum kernel methods—that illustrate how deviations in complex data structures can be isolated through quantum states. Finally, we explore practical applications using open-source quantum software development kits, demonstrating how to design, execute, and analyze quantum anomaly detection experiments on Noisy Intermediate-Scale Quantum (NISQ) processors.
Overall, the presentation highlights how quantum computation can drive a new generation of machine learning–based security and diagnostic methods, paving the way for impactful applications in cryptography, industrial IoT, and real-time fraud detection.
