Frank GOETHALS is a Full Professor and former Head of Department at IÉSEG School of Management, France, specializing in Information Systems and Technology. His academic journey includes visiting roles at KU Leuven and Tias Nimbas Business School. He earned a Ph.D. in Applied Economics from KU Leuven and a Master’s in Economics from Université catholique de Louvain. His research focuses on IS adoption, new technologies, and digital innovations, with notable work on sustainability in IT use, automation anxiety, and enterprise architecture. GOETHALS has received multiple awards, including Teaching Excellence Awards (IÉSEG, 2023/2021), Best Teacher recognitions (Tias Nimbas, 2016/2019), and paper awards at conferences like ItAIS (2012). His research spans over 20 years, covering topics such as B2B integration, data mining in education, and e-commerce behavior. He authored 20 Trends in Digital Innovations (2014) and contributed to numerous book chapters on enterprise systems and technology trends. Professional experience includes roles as a Post-Doctoral Researcher (BOF grant, KU Leuven) and Webmaster at Vlamvo. He teaches courses on digital innovations, MIS strategy, and Excel applications across bachelor’s, master’s, and postgraduate programs. His work bridges academic research with practical business challenges, emphasizing sustainable and mindful innovation strategies.
Sophie Chabridon is an Associate Professor in the Department of Computer Science at Télécom SudParis, part of the Institut Télécom. She is a member of the SAMOVAR laboratory, specifically the ACMES research team, focusing on algorithms, components, models, and services for distributed computing. Education: PhD in Computer Science, Université René Descartes - Paris V (1996) MSc in Computer Science, University of Oklahoma (1992) Ingénieur in Computer Science, ISI-CUST, Clermont-Ferrand (1991) Research Interests: Context management in IoT Privacy-preserving technologies Middleware architectures for distributed systems Quality of context (QoC) and data coherence Mobile and ambient computing Key Projects: ANR INCOME (2012–2015): Multi-scale context management for IoT Cappucino (2007–2010): Ubiquitous applications and middleware JEMTU (2006–2008): Mobile games and usage technologies Advising & Grants: Supervised over 10 PhD and Master students, including recent theses on context quality and privacy in IoT. Involved in grants such as ANR and European ITEA programs. Lab Affiliation: SAMOVAR laboratory, focusing on networking, communications, and distributed systems research.
Walid Gaaloul is a Professor at Télécom SudParis, part of the Institut Polytechnique de Paris (IP Paris) and Institut Mines Télécom. He serves as Deputy Director of the SAMOVAR research laboratory and leads the ACMES research team. He is also a member of the DIEGO group within the Computer Science Department at Télécom SudParis. Previously, he was a researcher at the Digital Enterprise Research Institute (DERI) and an adjunct lecturer at the National University of Ireland, Galway (NUIG). He holds an M.S. (2002) and Ph.D. (2006) in Computer Science from the University of Lorraine, France, and a habilitation (2014) from Pierre et Marie Curie University, Paris. His research focuses on Business Process Management, Process Mining, Cloud Computing, and Service-Oriented Computing. He has authored over 200 publications in these domains and actively contributes to international conferences and journals as a reviewer and committee member. His work spans topics like cloud resource allocation, process discovery from emails, IoT service optimization, and blockchain-based process execution. His articles explore cutting-edge topics such as energy-efficient IoT service migration, trustworthy decentralized auctions, and formal verification of edge service monitoring. He collaborates on national and European projects addressing cyber-physical systems and distributed cloud-edge infrastructures.
Valerio Incerti is an Assistant Professor at SKEMA Business School and researcher at GREDEG (a joint CNRS/Université Côte d'Azur/SKEMA unit), specializing in organizational behavior within complex team structures. His research spans ecological transition, organizational resilience, multiple team membership (MTM), and virtual teams, with recent focus on LGBT entrepreneurs' networking behavior and innovation management. He examines how MTM impacts knowledge sharing, career trajectories, and system performance across diverse organizational contexts. Publication analysis (2013-2025) reveals evolving MTM research from foundational performance studies to contemporary virtual collaboration challenges, emphasizing communication rules, contextual variety, and diversity integration in modern workplaces. Dr. Incerti contributes through PCIM (Pole Growth, Industry, and Markets) and leads Team TERO (Ecological Transition and Organizational Resilience) at GREDEG.
Dylan Laplace Mermoud is a Postdoctoral Researcher in the Department of Applied Mathematics at ENSTA Paris. His research focuses on Algebraic Combinatorics, Quantum Computing, and Cooperative Game Theory. He explores topics such as social organization structures through operad theory, core stability in game-theoretic frameworks, and variational quantum algorithms for combinatorial problems. His work bridges abstract algebraic methods with practical applications in network dynamics and optimization. His research interests span the intersection of mathematics and social sciences, particularly in modeling cooperative systems and analyzing network-based decision-making processes. He contributes to both theoretical advancements and computational tools for solving complex combinatorial challenges, including applications in quantum computing and market equilibrium correction. Key research trends in his publications include the algebraic foundations of social configurations, algorithmic approaches to core stability, and quantum algorithm design for permutation-based problems. His work emphasizes interdisciplinary applications, such as optimizing resource allocation in dynamic networks and developing frameworks for understanding steady coalition formation in game-theoretic contexts.
Professor Linda ZHANG holds the rank of Full Professor at IÉSEG School of Management in Lille, France, serving as Academic Director for the Operations Management track. She earned a Ph.D. in Industrial Engineering from Nanyang Technological University (2007) and an HDR in Management Sciences from Paris 13 University (2012). Her academic career includes visiting roles at leading institutions like the University of Science and Technology of China and Singapore Management University. Research focuses on operations management challenges: supply chain optimization, production configuration, and healthcare service design. Notable contributions include frameworks for emission trading schemes, inventory shrinkage modeling, and platform-based manufacturing systems. She has authored over 100 peer-reviewed articles and a textbook on process platforms for mass customization. Awardees of the IEOM Distinguished Service Award (2018) and multiple best paper honors. Active in interdisciplinary projects, including healthcare logistics design and digital transformation initiatives. Serves as Honorary Faculty at RMIT University and maintains collaborative ties with global institutions through grants and visiting scholar programs. Education: HDR, Management Sciences, University of Paris 13 (2012) Ph.D., Industrial Engineering, Nanyang Technological University (2007) Bachelor, Industrial Engineering, Tianjin University (1998) Grants: €750 for Finnish university collaboration (2010) €5,000 overseas visit grant (2010) €2,500 honorary scholarship (2010) Labs/Teams: Leads operations research group at IÉSEG, collaborating with industry partners on smart manufacturing and sustainable supply chain projects.
Simon Belieres is an Assistant Professor in the Department of Information, Operations and Management Sciences at Toulouse Business School. His research focuses on logistics network design, operations research, and optimization algorithms with applications in transportation and supply chain management. He holds a full-time academic position and is affiliated with TBS Education. Key research areas include Benders decomposition methods for stochastic programming, scheduling algorithms for automated systems, and mathematical heuristics for logistics network optimization. His work bridges theoretical optimization frameworks with practical applications in multi-product supply chains and sustainable delivery networks. Belieres' publications (2018-2025) demonstrate a strong emphasis on network design challenges, stochastic resource allocation, and algorithmic innovation. Notable contributions include partial Benders decomposition techniques, time-expanded network reduction, and metaheuristic approaches for logistics problems. His research also addresses real-world systems such as automated kitchens and urban spare part delivery networks. While no specific awards or grants are listed, his active publication record reflects continuous scholarly engagement in operations management and optimization fields.
Pedro Castillo is a DR CNRS Researcher at the Heudiasyc laboratory within the University of Technology of Compiègne, France. He leads the SyRI team (Systèmes Robotiques en Interaction) and oversees drone-related activities at the laboratory. His research focuses on nonlinear control, robust navigation, multi-agent systems, and resilient control strategies for unmanned aerial vehicles (UAVs). He has contributed significantly to the theoretical and experimental validation of control schemes for under-actuated systems, emphasizing real-time implementation on robotic platforms. His work bridges theoretical advancements and practical applications, particularly in aerial robotics and cooperative control architectures. Dr. Castillo has supervised 19 PhD students (15 defended, 3 ongoing, and 1 cotutelle), managed 29 research projects, and organized over 50 seminars at Heudiasyc. He is an Associate Editor for the IEEE Robotics and Automation Letters (2020–2024) and has participated in numerous academic committees, including ANR and HCERES evaluations. His research has resulted in over 170 scientific publications, including two influential books on aerial vehicle modeling and control. Key contributions include the design of resilient control algorithms, multi-agent coordination frameworks, and hardware prototypes such as the PVTOL, octarotor, and ROMEO quadrotor. His work has been showcased at international conferences and trade fairs, solidifying Heudiasyc's reputation as a leader in UAV control research.
DALI YOUCEF Manel is an Assistant Lecturer at École Nationale Supérieure d'Électronique, Automatique et Informatique (ENSEA), affiliated with the Quartz Laboratory and the Non-Linear Automatic research group. His research focuses on applied mathematics, dynamical systems, optimization, differential equations, and chemostat models. He teaches courses on linear systems, digital signal processing, random signal modeling, and mathematics. Community activities include co-hosting the Quartz weekly seminar and co-organizing the 2022 Doctoral Students' Day in Paris. His publications explore chemostat productivity, bioreactor dynamics, and mathematical epilepsy modeling, accessible via Google Scholar and ResearchGate. No scientific awards are listed, but his work emphasizes interdisciplinary applications of mathematical modeling in engineering and biology. Advising and grants details are not provided, though ongoing research likely involves lab collaborations at Quartz.
Christian Kästner is a Professor at Carnegie Mellon University , actively contributing to software engineering, machine learning systems, and open-source software research. He serves on numerous conference committees, including the OOPSLA Review Committee (2025), ICSE Research Track (2024), and ESEC/FSE Program Committee. Research Interests His work focuses on: Integrating machine learning into production systems Open-source dependency management and security Automated program repair and performance modeling Collaboration challenges in ML-enabled systems Software engineering education for AI/ML Conference Contributions Christian has authored and reviewed papers on topics spanning supply chain security, notebook tooling, and system-wide ML engineering. Notably: Keynote: From Models to Systems at CAIN 2024 Research on LLM integration, dependency abandonment, and fairness analysis Committee roles in ICSE, ESEC/FSE, ASE, and SPLASH
Chih-Kai Ho is an active researcher specializing in robotics and computer science with recent publications spanning 2018-2023. Their work demonstrates consistent collaboration with C. King (214 joint publications) and focuses on solving complex problems in robotic motion planning and inverse kinematics. Research interests center on robotics with emphasis on inverse kinematics for redundant manipulators , motion planning algorithms , and reinforcement learning for robot control . Key contributions include developing deep learning approaches for navigating joint solution spaces, creating stage-based power optimization for mobile applications, and designing novel constrained path planning methods. The research shows strong interdisciplinary connections between computer vision, mobile computing, and mechanical engineering. Publication trends reveal increasing focus on data-driven robotics solutions since 2022, with significant contributions to accelerating inverse kinematics computations and improving reinforcement learning efficiency. The work bridges theoretical algorithms with practical implementations for robotic arms and mobile devices. No scientific awards or honors were documented in the source materials. As a researcher, Ho appears to focus on technical development rather than academic advising, with no student listings in the provided materials. The collaborative nature of the work suggests involvement in research teams focused on robotics and AI, particularly through frequent partnerships with computer science and engineering colleagues.
Fuyuki Ishikawa is an Associate Professor at the Information Systems Architecture Science Research Division of the National Institute of Informatics (NII) in Tokyo, Japan, where he also serves as Director of the GRACE Center. Additionally, he holds positions as an Associate Professor at Sokendai (The Graduate University for Advanced Studies) and as a Visiting Associate Professor at The University of Electro-Communications, focusing on Trustworthy & Smart Software Engineering. His research spans multiple institutions and international collaborations, with a strong emphasis on dependable software systems. Dr. Ishikawa's research interests focus on "Smart Systems and Smart Dependability Assurance," with particular emphasis on dependability in Cyber-Physical Systems and Machine Learning Systems. His work investigates techniques of verification, reasoning, optimization, automated test generation, and self-adaptation by making use of various models for requirements, specifications, and designs. Key research areas include Formal Methods (Formal Specification, Refinement), Testing (Search-based Testing, Model-based Testing), Goal-Oriented Requirements Analysis, Self-Adaptation (Models@run.time), and Machine Learning Systems Engineering. His research also extends to Service-Oriented Computing, Cloud Computing, IoT, and Cyber-Physical Systems, particularly in automotive applications. Analysis of Dr. Ishikawa's recent publications reveals a strong focus on applying formal methods and search-based techniques to ensure the reliability of AI-enabled systems, particularly in safety-critical domains. His work increasingly addresses challenges in testing and repairing deep neural networks within cyber-physical contexts, with significant contributions to autonomous driving systems verification. There's also a growing interest in quantum-classical hybrid systems and the application of evolutionary computation to software engineering problems. His research bridges theoretical formal methods with practical industrial applications, often through large-scale collaborative projects. Dr. Ishikawa has received numerous prestigious awards: Awards for Science and Technology (Research Category) by the Minister of Education (April 2024) IPSJ/IEEE Computer Society Young Researcher Award (March 2020) Multiple Best Paper Awards at ICFEM, SEKE, ICECCS, and ISSRE Best Artifact Award at ISSTA 2015 Dr. Ishikawa leads the Ishikawa Lab at NII, which participates in several major research projects including the ERATO-MMSD Project, MIRAI-eAI Project, REFENG Project, and PerQAS Project. His lab consists of members from different organizations and actively promotes international and industry-academia collaborations. He also offers internship opportunities through NII for students interested in trustworthy software engineering, particularly focusing on generative AI applications and testing of AI systems.
Carola Doerr is a CNRS Research Director at Sorbonne University's LIP6 Laboratory, specializing in black-box optimization algorithms. She leads the Operational Research team and serves as Scientific Delegate for Section 2 at CNRS (40% time commitment since September 2023). Her research bridges theoretical foundations and practical applications of optimization heuristics, with significant industrial collaborations including Thales, Honda, and Facebook. Her primary research interests include black-box optimization, algorithm configuration, benchmarking methodologies, and discrepancy theory. Doerr develops mathematical models predicting minimum evaluations required for optimization problems while creating practical tools like IOHprofiler for algorithm analysis. Her work enables dynamic algorithm selection that adapts to different problem instances and optimization phases. Her recent publications reveal strong trends in dynamic algorithm configuration, theory-guided benchmarking, and biomedical applications. She has pioneered approaches combining machine learning with optimization theory to create more efficient techniques for industrial and scientific problems, particularly in sensor configuration and medical prediction. CNRS Bronze Medal 2022 for black-box optimization research ERC Consolidator Grant 2024 for dynaBBO project (€2M) Feodor Lynen Research Fellowship from Alexander von Humboldt Foundation Otto Hahn Medal from Max-Planck-Society Multiple best paper awards at GECCO, FOGA, and CEC conferences Doerr actively supervises numerous PhD students across multiple institutions and leads the Benchmarking Network. Her research group develops open-source tools like IOHprofiler that facilitate empirical comparisons of optimization algorithms. Current projects focus on dynamic algorithm selection for biomedical applications and mechanical design optimization, with strong emphasis on explainability and real-world implementation.
Jorge Mario Cruz-Duarte serves as a Postdoctoral Researcher at the Centre Inria de l'Université de Lille, where he is affiliated with the BONUS research team. His current research focuses on Neuromorphic Optimisation and Automated Algorithm Design and Configuration, contributing to advanced computational methodologies for complex problem-solving. His research spans interdisciplinary domains with core emphasis on algorithmic innovation: Optimization techniques including evolutionary algorithms and metaheuristics Machine learning integration for adaptive problem-solving Applied electronics and energy systems optimization Thermodynamics and control systems modeling Fractional calculus applications in engineering contexts As an active member of the BONUS team at Inria Lille, Dr. Cruz-Duarte bridges theoretical computer science with practical engineering challenges. No information regarding scientific awards, student supervision, or grant funding was provided in the source materials.
Dominique Geniet serves as an Associate Professor at the University of Poitiers, France, working within the College of Engineering. He is affiliated with the LIAS Laboratory (Laboratoire d'Ingénierie des Applications de la Signalétique), which maintains dual locations at ENSIP (École Nationale Supérieure d'Ingénieurs de Poitiers) and ISAE-ENSMA (École Nationale Supérieure de Mécanique et d'Aérotechnique). His research spans theoretical computer science and practical applications in real-time systems, with significant contributions to scheduling theory, system validation, and more recently, data management. Professor Geniet's research focuses on hard real-time systems with strict temporal constraints, employing formal methods including regular languages, generating functions, and discrete geometry. His early work concentrated on theoretical foundations of scheduling algorithms for uniprocessor and multiprocessor systems, evolving toward distributed real-time systems validation, and more recently expanding into data warehousing optimization and query scheduling. His interdisciplinary approach bridges theoretical computer science with practical applications in critical systems where timing constraints are paramount. Analysis of his publication trajectory reveals a consistent focus on real-time systems with evolving applications. The earliest works (1995-2005) emphasize theoretical foundations using formal language theory and mathematical models for scheduling and validation. The middle period (2005-2012) shows expansion into distributed systems and geometric approaches to validation. The most recent publications (2012-2018) demonstrate a strategic pivot toward data management problems while maintaining the core focus on timing constraints and optimization. This evolution reflects both theoretical depth and practical adaptation to emerging computational challenges. Professor Geniet has been actively involved with the Real Time Team and Data Engineering Team within LIAS Laboratory. His work demonstrates strong collaboration with researchers including Gaëlle Largeteau-Skapin, Annie Choquet-Geniet, and Ladjel Bellatreche, suggesting participation in both theoretical research groups and applied projects with potential industrial relevance. The dual affiliation with ENSIP and ISAE-ENSMA indicates work spanning broader engineering contexts beyond pure computer science.
