جست‌وجوی استاد و پژوهشگر

Rob H. Bisselingمشاهده پروفایل

استاد

• Parallel Algorithms
• Sparse Matrix/Tensor Computations
• Graph and Hypergraph Algorithms
• +۵ مورد دیگر

Rob H. Bisseling is a Full Professor in Scientific Computing at Utrecht University's Mathematical Institute and a visiting professor at ENS de Lyon's LIP laboratory (March–May 2024). He holds a BSc/MSc in Mathematics (cum laude) from the Catholic University of Nijmegen and a PhD in Theoretical Chemistry from the Hebrew University of Jerusalem. His research focuses on parallel algorithms, sparse matrix/tensor computations, and hypergraph partitioning, with applications in high-performance computing and numerical methods. He has authored a seminal textbook on parallel scientific computing and contributes to pedagogical resources like video lectures. During his visit to LIP, Bisseling collaborates with the ROMA team under Bora Uçar to advance parallel algorithms for large-scale irregular applications. His work includes developing tools like PMondriaan for sparse matrix partitioning and promoting knowledge transfer through lectures on BSP programming. He engages with researchers, PhD students, and engineers at LIP, extending collaborations to Lyon's Institut Camille Jordan and LabPhys for tomographic reconstruction and statistical physics modeling. His academic career includes roles at Royal Dutch Shell and as Utrecht University's Director of Education (2012–2015). He advocates interdisciplinary approaches, bridging computational methods with applied sciences and engineering challenges.

Martin Schulzمشاهده پروفایل

استاد

• Parallel and Distributed Architectures
• High Performance Computing
• Performance Modeling and Analysis
• +۴ مورد دیگر

Martin Schulz is a Full Professor and Chair for Computer Architecture and Computer Organization at the Technical University of Munich (TUM), where he has been employed since 2017. Previously, he held research positions at Lawrence Livermore National Laboratory (LLNL) and Cornell University. He holds a Doctorate in Computer Science from TUM (2001) and an MS in Computer Science from the University of Illinois Urbana-Champaign (UIUC). His research encompasses: Parallel/distributed systems architecture HPC performance monitoring and optimization Memory hierarchy efficiency Parallel programming models and tools Power/energy-aware computing System-level fault tolerance Schulz leads the MPI Forum standardization body and has authored over 200 peer-reviewed publications. Major scientific recognitions: IEEE/ACM Gordon Bell Prize (2006) for high-performance computing achievements R&D 100 Award (2011) for innovative research

Roberto Di Cosmoمشاهده پروفایل

استاد

• Theoretical Computing
• Functional Programming
• Parallel and Distributed Programming
• +۶ مورد دیگر

Roberto Di Cosmo is a Full Professor of Computer Science at University Paris Diderot since 1999, currently on leave at Inria. He is a prominent researcher and advocate for Free Software, contributing to its adoption since 1998 through seminars, articles, and software. He co-founded the Software Heritage initiative in 2016, aiming to build a universal archive of publicly available source code, and served as director of IRILL, a research structure dedicated to Free and Open Source Software quality, since 2010. His research focuses on theoretical computing, including functional programming, parallel and distributed programming, semantics of programming languages, type systems, rewriting, and linear logic. He has led significant projects like the European Mancoosi research initiative, addressing challenges in static analysis of large software collections. His work with Software Heritage has archived over 3 billion source code files and 700 million commits across 60 million projects. This effort preserves scientific and technological knowledge, enables software reuse, and supports research through a curated archive infrastructure. Roberto Di Cosmo actively bridges academic research with societal impact, emphasizing the role of software in digital culture and advocating for open knowledge sharing.

Matthieu EXBRAYATمشاهده پروفایل

مدرس

• Machine Learning
• Data Analysis
• Data Visualization
• +۷ مورد دیگر

Matthieu EXBRAYAT is a Lecturer in Computer Science at the University of Orleans, affiliated with the Fundamental Computer Science Laboratory of Orléans (LIFO). He holds significant administrative responsibilities including Vice President for Digital and Educational Innovation at the University of Orleans and previously served as Co-head of the IT department (2019-2021) and Head of the IMIS Computer Science Masters (2015-2021). His research spans multiple domains with a primary focus on Machine Learning applications in data analysis and visualization, time series analysis, and computer vision applications in archaeology. His work demonstrates strong interdisciplinary connections between computer science and archaeological documentation, particularly in ceramic sherd classification using deep learning techniques. Earlier in his career, he contributed significantly to high-performance databases and probabilistic relational learning, especially Markov logic networks. Analysis of his publication history reveals an evolving research trajectory: beginning with database systems and parallel join algorithms in the early 2000s, progressing through Markov logic networks and clustering algorithms in the 2010s, and more recently focusing on machine learning applications in archaeological imaging and programming language analysis. His recent publications show a clear shift toward practical applications of machine learning in diverse domains while maintaining theoretical rigor. Throughout his career, EXBRAYAT has maintained extensive collaborative research networks, with Lionel Martin appearing as a co-author on 20 publications, followed by Guillaume Cleuziou (14), Jacques-Henri Sublemontier (9), and Quang-Thang Dinh (7). These collaborations span multiple research domains and demonstrate his ability to work across disciplinary boundaries. As an educator, he teaches distributed information systems, programming languages, databases, AI/data mining, and geographic information systems. He has also been actively involved in scientific mediation, delivering numerous public lectures on artificial intelligence topics since 2017, including conferences such as "Living in harmony with AIs" (2023) and "Artificial Intelligence: My new toaster is an AI!" (2019). His administrative contributions are substantial, including roles as Communications Correspondent for the Computer Science Disciplinary Center, Facilitator of the DataCenters ComUE Centre Val de Loire reflection group, and Vice President of Digital Resources at the Leonardo da Vinci Confederal University. He also served as LIFO website webmaster for over a decade (2002-2014) and was responsible for the STIC degree program (now IT degree) from 2004-2008.

• Synchronous Reactive Programming
• Concurrency
• Parallelism
• +۷ مورد دیگر

Frédéric DABROWSKI is a Lecturer with HDR at the University of Orleans, Faculty of Sciences, affiliated with the LIFO laboratory. He serves as head of the Master in Computer Sciences program since 2021 and participates in university governance through the Academic and Research Councils. His research bridges theoretical computer science and practical system verification, with emphasis on reactive and parallel programming models. His core research spans Synchronous Reactive Programming, Concurrency/Parallelism, Program Semantics, Deductive Verification, Static Analysis, Temporal Logic, and Formal Methods mechanized primarily in Coq. He investigates effect systems in functional reactive languages, textual alignment in SPMD programs, and verification techniques for synchronous systems, contributing foundational semantics and analysis tools. Analysis of his 20+ publications reveals consistent focus on applying formal methods to concurrent systems, evolving from BSP/SPMD semantics (2006-2019) toward functional reactive programming with effects and IoT security (2020-2025). Recent work emphasizes Coq-mechanized verification, effect handling, and safe reactive language design for critical embedded systems. DABROWSKI has supervised eight advisees including PhD candidates Terence Clastres (deductive verification), Darine Rammal (IoT reactive programming), and Jordan Ischard (FRP semantics), plus Master's students like Loic Kerbaul. He participates in funded projects: SIOMediC (IoT medical security, 2023-2024), DeSSUF (safe IoT languages, 2022-2023), BSP Program Analysis (Huawei, 2016-2019), and PaPDAS (parallel skeletons, 2011-2014). Based at LIFO, he co-leads the GT CLAP research group and serves on HLPP symposia steering committee, fostering collaborations with INSA Center Val de Loire, University of Maribor, and Japanese institutions to advance high-level parallel programming and verification.

Lakhdar Saisمشاهده پروفایل

استاد

• Artificial Intelligence
• Propositional satisfiability
• Quantified boolean formula
• +۴ مورد دیگر

Lakhdar Sais is a Professor of Computer Science at the Centre de Recherche en Informatique de Lens (CRIL), CNRS UMR 8188, at Université d'Artois, Faculty of Jean Perrin Sciences in Lens, France. His research focuses on search and representation problems in Artificial Intelligence, including propositional satisfiability, quantified boolean formulas, constraint programming, knowledge representation and reasoning, data mining, and AI applications in Social and Human Sciences. He has supervised numerous PhD students throughout his career, with recent students including David ING (2021-present) working on migration data knowledge extraction, and previously Ikram NEKKACHE (2021), Sofiane TOUATI (2021), and Kahina BOUCHAMA (2020). His research has been recognized with multiple awards including best paper awards at SAT'11 and ICTAI'2009, and first place in the International SAT 2009 competition. His current research projects include the ANR project HYCI (2023-2026) on Hyper-places, Crises, Migrations and Inequalities, Project ERA (2022-2025) on producing new knowledge in juvenile justice and mental health, and ANR project POSTCRYPTUM (2021-2023) on algebraic cryptanalysis for post-quantum cryptography. He has also edited the Handbook of Parallel Constraint Reasoning (Springer, 2018). Scientific Awards: Best paper award at SAT'11 for 'On freezing and reactivating learnt clauses' Best paper award at ICTAI'2009 for 'Learning for Subsumption' ManySAT - First rank at International SAT 2009 competition (Parallel Track) LySAT - Two bronze medals at International SAT 2009 competition (Sequential Track) ManySAT - First rank at SAT Race 2008 competition Professor Sais has taught numerous courses including Artificial Intelligence, Constraint Programming, Knowledge Representation and Reasoning, Expert Systems, Complexity Theory, Advanced Data Structures, Algorithmics, and Functional Programming. He has served as leader of the inference and decision process research group at CRIL (2002-2013) and as Delegate Director of the CRIL laboratory (2013-2018).

Panayotis Mertikopoulosمشاهده پروفایل

استادیار

• Game Theory
• Machine Learning
• Optimization
• +۴ مورد دیگر

Dr. Panayotis Mertikopoulos is a CNRS researcher (chargé de recherche) at the Laboratoire d'Informatique de Grenoble, part of Université Grenoble Alpes. He is affiliated with the Inria/LIG joint team POLARIS and has held visiting positions at UC Berkeley, EPFL, LUISS University of Rome, and NKUA. His academic journey includes completing his PhD at the University of Athens in 2010 on "Stochastic perturbations in game theory and applications to networks" and his Habilitation à Diriger des Recherches (HDR) in 2019 on "Online optimization and learning in games: Theory and Applications". Dr. Mertikopoulos' research spans several interconnected fields at the intersection of mathematics, computer science, and economics. His primary research interests include: Game theory and its applications to network design and resource allocation Online learning algorithms and their convergence properties Optimization methods for non-convex and stochastic problems Applications to machine learning, signal processing, and wireless networks Quantum game theory and quantum computing applications His extensive publication record shows a clear evolution from foundational work in game dynamics and learning theory toward increasingly sophisticated applications in machine learning and network optimization. Recent work demonstrates growing interest in quantum game theory, non-convex optimization, and the mathematical foundations of deep learning. His research consistently bridges theoretical insights with practical applications, particularly in communication networks and distributed systems. Among his notable achievements is receiving the INFORMS best paper award in the network analytics section in 2022 for his work on "Robust power management via learning and game design". His publications have appeared in top venues including NeurIPS, ICML, COLT, IEEE Transactions, and leading economics and operations research journals. Dr. Mertikopoulos has supervised numerous PhD students and postdoctoral researchers, though specific names are not listed in the available information. He has secured research funding for projects at the intersection of game theory, optimization, and machine learning, with applications to network design and resource allocation. His collaborative work spans multiple institutions across Europe and North America. As a member of the POLARIS research team at Inria/LIG, he contributes to a vibrant research environment focused on parallel and distributed systems. His work often intersects with colleagues researching optimization algorithms, machine learning theory, and network science, creating opportunities for cross-disciplinary collaboration on complex computational problems.

• 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.

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.

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.