جستجوی استاد راهنمای دانشگاهی

Luc Rollandمشاهده پروفایل

مدرس

• Robotics
• Advanced Robotics
• Parallel Robot Design
• +۷ مورد دیگر

Luc Rolland is a Lecturer at the University of the West of Scotland, affiliated with the School of Computing, Engineering and Physical Sciences. He has maintained an active research profile since 1996 with recent collaborations at Centrale Nantes, Memorial University, and other international institutions. His research focuses on high-performance robotics systems design across multiple application domains. Rolland's work spans material handling systems, high-speed milling machinery, solar tracking mechanisms, palettizing solutions, reconfigurable robots, and stair-climbing platforms. His expertise extends to robot modeling, simulation, and control systems with particular emphasis on path planning and path pursuit certification. Recent publications (2021-2023) demonstrate a strategic expansion into healthcare robotics, decontamination systems, and autonomous vehicle validation. His work shows strong interdisciplinary connections between mechanical engineering, environmental science, and public health applications, particularly in ozone-based decontamination systems and UAV technology. Advanced robotics system design and implementation Parallel manipulator development for specialized applications UAV path planning and sense-and-avoid systems Hydrogen fuel cell systems for maritime applications Validation methodologies for autonomous vehicle features Rolland actively supervises three PhD students on projects related to UAV path planning, sense-and-avoid systems, and hydrogen-powered ship design. His research collaborations span Memorial University of Newfoundland, Centrale Nantes, and other international institutions. He has served as a visiting researcher at Ostfalia University of Applied Sciences and Changchun Institute of Technology, while also hosting academic visitors and providing peer review services for academic journals.

• Data Privacy
• Database Security
• Information Systems
• +۳ مورد دیگر

Gaël Le Mahec serves as a Lecturer at the University of Picardie Jules Verne within the Networks and Data Domain, where he leads research activities in the REDO group. His office is located in room 308 with direct phone access (5912). As an active researcher in data privacy, he maintains strong contributions to both theoretical frameworks and practical implementations of anonymization technologies. Dr. Le Mahec's research program centers on privacy-preserving data publishing , with particular expertise in k-anonymity variants (l-diversity, t-closeness) and distributed anonymization systems . His work addresses the critical challenge of maintaining analytical utility while ensuring robust privacy guarantees, especially for large-scale datasets. Current projects focus on optimizing privacy-utility tradeoffs in distributed memory environments and developing novel metrics for information loss quantification. The REDO research group under his involvement has established itself as a significant contributor to data privacy literature, with consistent publications in top venues including IEEE Big Data and Transactions on Data Privacy. Their research demonstrates strong practical relevance to industry challenges in GDPR compliance and secure data sharing. Prospective collaborators should note the group's emphasis on implementable solutions that bridge theoretical privacy models with real-world system constraints. Dr. Le Mahec maintains active research supervision through the REDO group, with particular opportunities for students interested in distributed systems security and privacy engineering. His publication trajectory since 2020 indicates sustained research productivity with increasing focus on scalable implementations for enterprise environments. The group's work shows promising direction toward practical privacy solutions for big data ecosystems.

• Combinatorial Optimization
• Operations Research
• Artificial Intelligence
• +۳ مورد دیگر

Corinne LUCET-VASSEUR is a University Professor at Université de Picardie Jules Verne (UPJV), leading Research Unit UR 4290 (OCIA - Optimisation Combinatoire, Images et Applications). Her office (Room 302, Tel: 5900) serves as the hub for her research group focused on combinatorial optimization and artificial intelligence applications. Her research spans: Combinatorial Optimization : Developing metaheuristics for NP-hard problems Healthcare Logistics : Patient flow optimization, facility location, simulation training Logistics Engineering : Parcel distribution, vehicle routing with time windows Algorithm Design : Ant Colony Optimization, Adaptive Large Neighborhood Search, portfolio methods She applies these methodologies to solve complex real-world problems, particularly in healthcare systems where resource constraints and scheduling complexity demand innovative optimization approaches. Her work bridges theoretical advances with practical implementation through industrial partnerships. Current research projects include: SMILE PICK UP (CIFRE industrial partnership) Simusanté (healthcare simulation) LORH (logistics optimization) These projects secure ongoing funding and provide doctoral training opportunities through industry collaboration. Her publication record demonstrates consistent methodological innovation applied to healthcare and logistics challenges across multiple European conferences and journals. Professor Lucet-Vasseur actively mentors junior researchers through co-authorship on conference papers and journal articles. Her supervision style emphasizes practical problem-solving with industry relevance, preparing students for both academic and industrial careers in optimization. The OCIA research unit provides a collaborative environment for tackling complex combinatorial problems with real-world impact. The OCIA laboratory serves as UPJV's center for combinatorial optimization research, specializing in metaheuristic development for healthcare and logistics applications. The lab maintains strong industry connections through CIFRE contracts and applied projects, ensuring research relevance while providing students with exposure to real business challenges. Current focus areas include adaptive algorithm selection using reinforcement learning and fitness landscape analysis for optimization problems.

• Combinatorial Optimization
• Parallel Optimization
• Sequential Optimization
• +۵ مورد دیگر

Toufik SAADI is a Senior Lecturer in Computer Science at the University of Picardie Jules Verne, Faculty of Sciences. He has been a member of the EPROAD EA 4669 research unit since January 2012 and leads the ROD research team, which focuses on Operational Research, Parallel computing, Decision support theory, Polynomial approximation and complexity, and Quantum computing. His research centers on: Sequential and parallel optimization of combinatorial problems High-performance computing tools development Collaborative solutions for information flow optimization in complex systems Mobile context applications Dr. SAADI's methodological framework involves comprehensive problem modeling from industrial contexts, precision analysis, implementation of sequential or parallel optimization approaches, technical environment evaluation, and performance analysis of optimization methods. His theoretical work utilizes mathematical models including graph theory, probability, logic, and data analysis, combined with both exact and approximate resolution methods such as heuristics and meta-heuristics. He remains actively engaged in the academic community, recently presenting a seminar on 'Parallel Computing' in December 2023, demonstrating his continued contribution to advancing computational optimization techniques.

Marie-Christine ROUSSETمشاهده پروفایل

استاد

• Knowledge Representation
• Information Integration
• Ontology-based data access
• +۷ مورد دیگر

Marie-Christine ROUSSET is a Professor of Computer Science at the University of Grenoble Alpes (UGA) in France, where she is a member of the LIG (Laboratoire d'Informatique de Grenoble) in the SLIDE group. Previously affiliated with Paris-Saclay (LRI), she has established herself as a leading researcher in Knowledge Representation and Information Integration. She holds the distinguished position of Senior member of the Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) and serves as co-responsible for the chair Explainable and Responsible AI within MIAI Grenoble Alpes. Her research focuses on ontology-based data access, logic-based mediation between distributed data sources, query rewriting using views, data linkage, and distributed reasoning for the Semantic Web. She skillfully combines artificial intelligence and database techniques to address complex information integration challenges, with applications spanning biomedical informatics, educational technology, and trustworthy AI. Her work demonstrates consistent innovation from foundational research to practical implementations, as evidenced by her co-authorship of the book 'Web Data Management' published by Cambridge University Press. Professor ROUSSET's recent publications (2019-2022) reveal a growing emphasis on data privacy, RDF graph anonymization, and interactive ontology engineering, while maintaining her strong contributions to semantic web technologies and knowledge representation. Her research shows increasing attention to trustworthy AI concerns, aligning with her leadership roles in relevant projects. Scientific Recognition Senior member of Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) Junior member of Institut Universitaire de France (IUF) from 1997 to 2002 Chevalier de l'Ordre National du Merite (July 11, 2011) EurAI Fellow (nominated ECCAI Fellow in 2005) Best Paper Award at AAAI'96 for 'Verification of Knowledge Bases based on Containment Checking' Professor ROUSSET maintains an active role in the scientific community through editorial work and organizational leadership. She serves on the Editorial Board of Communications of the ACM (CACM) and has held significant roles including PC chair of EGC 2019, Workshops co-Chair of WWW 2018, and Area Chair of IJCAI 2017. Her consistent service on program committees of major international conferences demonstrates her standing in the field. Her laboratory, the SLIDE group within LIG, focuses on semantic web technologies, knowledge representation, and data integration. The group maintains strong connections with the international research community and participates in collaborative projects addressing cutting-edge challenges in artificial intelligence and data management, with particular emphasis on trustworthy and explainable AI systems.

El-Ghazali TALBIمشاهده پروفایل

استاد

• Combinatorial Optimization
• Multi-objective Optimization
• Metaheuristics
• +۵ مورد دیگر

El-Ghazali TALBI is a Professor of Exceptional Class at Polytech Lille, University of Lille, where he leads the BONUS research team at Inria Euratechnologies. His career spans over 35 years with continuous academic progression from Researcher to full Professor. He earned a Master in Computer Science from ENSIMAG/INPG (1989), a PhD in Computer Sciences from INPG under T. Muntean (1993) on distributed memory parallel architectures, and a Habilitation from University of Lille 1 (2000) on combinatorial optimization. His academic trajectory includes positions at Grenoble institutions and uninterrupted service at Lille since 1994. TALBI's research centers on combinatorial and multi-objective optimization through metaheuristics, with pioneering work in parallel/distributed algorithms and machine learning integration. His expertise bridges theoretical optimization with industrial applications in bioinformatics, telecommunications, smart grids, and cloud computing, notably advancing Bayesian optimization for neural architecture search and deep learning systems. As a prolific mentor, he has supervised 12 PhD theses including recent defenses on Gaussian processes for aerospace systems (2021), Bayesian neural architecture search (2024), and sustainable power system optimization (2025). His BONUS team develops frameworks like PARADISEO for metaheuristics and GUIMOO for multi-objective evaluation. He has delivered over 100 international invited talks across 20+ countries, with recent focus (2020-2022) on data-driven metaheuristics, neural architecture search, and machine learning optimization synergies. His academic service includes keynote addresses at major conferences including IEEE WCCI, MIC, and ROADEF.

Pierre-Antoine Thouveninمشاهده پروفایل

استادیار

• Signal Processing
• Inverse Problems
• Hyperspectral Image Unmixing
• +۵ مورد دیگر

Pierre-Antoine Thouvenin is an Assistant Professor at Centrale Lille, a prestigious engineering school within the University of Lille community in France. He is a member of the SigMA team from the CRIStAL laboratory (Centre de Recherche en Informatique, Signal et Automatique de Lille), where he conducts research on inverse problems with applications to remote sensing and astronomy. His academic journey began with an Engineering degree in Electronics and Signal Processing from INP - ENSEEIHT Toulouse in 2014, followed by a Master of Science in "Signal, Image, Acoustics" from the same institution. He completed his Ph.D. in "Signal, Image, Acoustics" from Institut National Polytechnique de Toulouse between 2014 and 2017, with research focused on modeling spatial and temporal variabilities in hyperspectral image unmixing. Thouvenin's research interests span several interconnected areas in signal processing and computational imaging. His primary focus is on solving inverse problems, particularly in the context of radio-interferometric imaging for astronomy and hyperspectral image unmixing for remote sensing applications. He has made significant contributions to developing advanced algorithms for handling spectral variability in hyperspectral data and for image reconstruction in radio astronomy. His work often combines Bayesian statistical methods with optimization techniques to address challenging high-dimensional problems. A distinctive aspect of his research is the development of distributed and parallel computational methods that enable processing of extremely large datasets that would be intractable with conventional approaches. An analysis of his recent publications reveals a strong trend toward developing distributed and parallel computational methods for large-scale inverse problems. His work increasingly integrates machine learning approaches, particularly neural networks, with traditional signal processing techniques. There's a clear progression from theoretical developments in hyperspectral unmixing to practical applications in astronomy, particularly through his involvement in the ORION-B project where he applies statistical methods to infer physical conditions in star-forming regions. His most recent work demonstrates sophisticated integration of spatial regularization techniques with Bayesian inference for astrophysical parameter estimation. Prix Léopold Escande from Institut National Polytechnique de Toulouse (2017) - awarded to the best PhD theses defended at INPT Prix de l'Institut National Polytechnique de Toulouse (2014) - awarded for outstanding academic achievement during engineering studies Thouvenin is actively involved in mentoring the next generation of researchers. He currently co-supervises the PhD thesis of Pierre Palud on "Statistical methods for model inversion and spatial distribution of physico-chemical properties of the molecular cloud Orion B" as part of the CNRS 80|Prime project OrionStat. His research is supported through various academic collaborations and projects, including the ORION-B project led by Jérôme Pety, which involves molecular line observations from the IRAM-30m Large Program. He has established productive international collaborations, particularly with researchers at Heriot-Watt University in Edinburgh where he worked as a Research Associate from 2017-2019. Thouvenin is a key member of the SigMA team within the CRIStAL laboratory, a joint research unit between Centrale Lille, INRIA, and University of Lille. His work often intersects with the ORION-B project, where he collaborates with astrophysicists to develop statistical methods for inferring properties of Galactic and extra-galactic star forming regions. This interdisciplinary environment fosters innovation at the intersection of signal processing, statistics, and astronomy. He has developed expertise in translating complex statistical methodologies into practical computational tools that address real-world challenges in both remote sensing and astronomical imaging.

Luigi Santocanaleمشاهده پروفایل

استاد

• Order Theory
• Category Theory
• Universal Algebra
• +۱۲ مورد دیگر

Luigi Santocanale is a Full Professor in Computer Science at Aix-Marseille University, affiliated with the Laboratoire d'Informatique et Systèmes (LIS), UMR CNRS 7020, and member of the LSC research group. He is responsible for the 3rd year of the Licence Informatique, parcours Informatique Mathématique at Aix-Marseille University. His academic career includes previous affiliations with LIRICA@LIS, MOVE@LIF, LABRI, CPSC@UCalgary, BRICS, and UQAM. Professor Santocanale's research spans three interconnected domains: Order Theory, Category Theory and Universal Algebra (including Lattice theory, Topos Theory, and Enriched Category Theory); Combinatorics (focusing on Ordered sets, species theory, and game theory); and Logic in Computer Science (covering Intuitionistic logic, mu-calculus, decision problems for free algebraic objects, semantics of programming languages, and algebraic structures of distributed computing). His current research directions include lattice theory applications to databases, logics with fixed points, ordered sets in combinatorics, and circular proofs in mu-bicomplete categories. He has served as project coordinator for the ANR SOAPDC (Order Structures and Applications to Distributed, Parallel and Concurrent Computing) project and has been involved in numerous other research initiatives including LAMBDACOMB, RECIPROG, TICAMORE, MFL, TRECOLOCOCO, CHOCO, and TAGADA. Professor Santocanale is actively engaged with the academic community as a Program Committee member for major conferences including RAMiCS 2024 and TACL 2024. His significant contributions include: Decidability of the equational theory of relational lattices with applications to database query optimization Characterization of closure ordinals in modal mu-calculus Work on equational theories of permutation lattices (Permutohedra) and associahedra Development of circular proof systems for mu-bicomplete categories Professor Santocanale has organized and participated in the Marseille Lattice Working Group and has collaborated extensively with researchers including Fred Wehrung, M. J. Gouveia, and Nathalie Caspard. His work bridges theoretical computer science, mathematical logic, and algebraic structures, with implications for programming language semantics and distributed computing.

• Data Engineering
• Human-Computer Interaction
• Task Modeling
• +۲ مورد دیگر

Allan Fousse is an Associate Professor in Data Engineering at ISAE-ENSMA, France, affiliated with Laboratory LIAS (a joint research unit between ISAE-ENSMA and ENSIP). His work bridges engineering and human-centered computing through task modeling and simulation research. His core research areas include: Data Engineering Human-Computer Interaction Task Modeling Software Engineering Simulation He specializes in developing task model simulators like ProtoTask to address inaccuracies in user interface design processes. Publications from 2012-2016 reveal consistent focus on model validation techniques, with trends showing evolution from tool development (2012) to comprehensive methodology reviews (2015) and accuracy refinement (2016) in task modeling. He operates within ISAE-ENSMA's Data Engineering team at Laboratory LIAS, which maintains parallel research groups in Automatic Control and Real Time systems across Poitiers and Chasseneuil campuses.

Clément QUINTONمشاهده پروفایل

دانشیار

• Computer Science
• Parallel Computing
• High-Level Synthesis
• +۳ مورد دیگر

Clément QUINTON is an Associate Professor at the University of Lille, affiliated with both Inria and the CRIStAL laboratory (Centre de Recherche en Informatique, Signal et Automatique de Lille). He is an elected member of the laboratory council and maintains offices in both the Inria building (Room B312) and the M3 building (Room 224) at the Cité Scientifique campus. Dr. QUINTON is a member of the Spirals research team and holds the HDR (Habilitation à Diriger des Recherches), a French academic qualification that allows him to supervise PhD students. Dr. QUINTON's research spans several areas within computer science, with a primary focus on parallel computing, polyhedral models, and hardware acceleration. His work bridges theoretical computer science with practical applications in embedded systems and biomedical monitoring. He has made significant contributions to the fields of: Polyhedral compilation and loop optimization techniques High-level synthesis for FPGA-based hardware acceleration Systolic array design and parallel architectures Disruption-tolerant wireless sensor networks for biomedical applications Sustainable software engineering and cloud configuration Application of Large Language Models to software development Analysis of Dr. QUINTON's publication history reveals a consistent research trajectory that began with foundational work in parallel algorithms and systolic arrays, evolving toward more contemporary applications involving FPGA acceleration, polyhedral compilation, and biomedical sensor networks. His recent work shows increasing interest in sustainable computing, cloud architectures, and the application of Large Language Models to software engineering challenges. This evolution demonstrates his ability to adapt theoretical computer science concepts to address emerging technological challenges. Dr. QUINTON has supervised numerous PhD students whose research aligns with his expertise: Virginie Amand: Sustainable software services using large-scale models Alexandre Bonvoisin: Frugal software architectures for cloud-native microservices Tristan Coignion: Energy impact of Large Language Models for code Edouard Guegain: Software optimization through configuration (defended September 2023) Brell Péclard Sanwouo Chekam: Automatic detection and correction of side-channel vulnerabilities in cryptographic libraries Nada Zine: Complex and self-adaptive software systems Maxime Huyghe: Sustainable software services development based on Language Models (LLM) As a member of the Spirals research team at Inria and CRIStAL, Dr. QUINTON contributes to a collaborative environment focused on software engineering, system architecture, and sustainable computing. His work bridges theoretical computer science with practical applications in healthcare monitoring, cloud computing, and energy-efficient software development.