جست‌وجوی استاد و پژوهشگر

• Cognitive Sciences
• Evolutionary Biology
• Linguistics
• +۳ مورد دیگر

Jean-Louis Dessalles is a Professor at the Institut Polytechnique de Paris, affiliated with the School of Computer Science and the Department of Cognitive Sciences. His research focuses on the intersection of cognitive science, evolutionary biology, and artificial intelligence, particularly the evolutionary origins of language, computational models of human cognition, and algorithmic information theory. He is a co-founder of the Simplicity Theory framework, which explains cognitive processes through principles of complexity minimization. Key contributions include books such as Why We Talk: The Evolutionary Origins of Language and Des Intelligences Très Artificielles , which explore language evolution and AI limitations. His work spans interdisciplinary areas like social signaling, causal reasoning in cyber-physical systems, and the philosophical implications of consciousness and responsibility. Recent research emphasizes applications of simplicity theory to AI, causal learning in smart environments, and the biological foundations of communication. Dessalles has pioneered methods for explainable AI in complex systems, focusing on decentralized architectures for smart homes and urban traffic management.

Alexandra Silvaمشاهده پروفایل

استاد

• Computer Science
• Kleene Algebra with Tests
• Coalgebra
• +۴ مورد دیگر

Alexandra Silva is a Professor of Computer Science at Cornell University, with a research group partially based at University College London (UCL) where she previously held a Royal Society Wolfson Fellowship. She is deeply involved in academic leadership, serving on program committees for conferences like ECOOP and POPL, and organizing workshops at venues such as VMCAI and PLDI. Her research spans formal methods, probabilistic programming, and coalgebraic modeling, with applications in network verification and concurrent systems. Key Research Themes: Kleene Algebra with Tests (KAT), coalgebraic semantics, probabilistic program verification, network control plane analysis, and categorical approaches to formal methods. Collaborations: Active collaborations with institutions including Cornell, UCL, University of Leicester, and INRIA Rhone-Alpes. Scientific Awards: Distinguished Paper Award at POPL 2020 Advising: Currently advising eight PhD students, including Tiago Ferreira and Noam Zilberstein, with a broader team of postdocs, alumni, and collaborators.

Eric Pinsonمشاهده پروفایل

استاد

• Combinatorial Optimization
• Scheduling Theory
• Transport Logistics
• +۳ مورد دیگر

Eric Pinson is a Professor at the University of Angers (UCO) within the Faculty of Sciences. He holds an HDR (Accreditation to Supervise Research) from Université Pierre et Marie Curie (Paris VI) since 1994 and a PhD in Computer Science and Applied Mathematics from the same institution in 1988. He leads the LigéRO regional project (Loire Operational Research Group) and is an integrated member of LARIS (Angevin Research Laboratory in Systems Engineering), part of team EA-7315. Education: PhD in Computer Science and Applied Mathematics, Université Paris VI, 1988 HDR in Research Supervision, Université Paris VI, 1994 Research Interests: His work focuses on Combinatorial Optimization , particularly in scheduling theory and transport logistics. Key areas include algorithm development for scheduling problems (job-shop, flow-shop, maintenance planning), resource allocation in production systems, and optimization under constraints such as human resource limitations and energy considerations. His contributions span theoretical advancements and practical applications in renewable energy maintenance (e.g., wind turbines) and industrial logistics. Publications Trends: Recent work emphasizes algorithmic efficiency (O(n²) scheduling adjustments, energy function management) and industrial applications (wind farm maintenance, multi-skill project scheduling). Earlier publications established foundational scheduling algorithms and constraint-based optimization techniques. Scientific Awards: No specific awards listed, though his sustained contributions indicate significant recognition within the operations research community. Advising & Grants: Supervised at least one PhD student (Aurélien Froger, 2012–2016). Involved in collaborative projects including the DC-GNN program for real-world data clustering using graph neural networks. Labs & Teams: Primary affiliations include LARIS (Systems Engineering) and the LigéRO network, fostering regional operational research collaboration.

• Image Processing and Analysis
• Signal Processing
• Pattern Recognition
• +۷ مورد دیگر

Michel Ménard is a Teacher-Researcher at the University of La Rochelle, affiliated with the Mathematics and Computer Science departments. His research focuses on image and signal processing, particularly in cardiovascular imaging, dynamic texture analysis, and UWB radar applications for through-wall imaging. Key projects: ANR DIAMS, FISC consortium, A.Gaugue project Applications: Cardiovascular imaging, environmental monitoring, mobile application programming Research Interests Ménard's work centers on modeling information ambiguity, imprecision, and uncertainty in image analysis, pattern recognition, and information fusion. He has developed generalized fuzzy coalescence methods, non-parametric Bayesian approaches for trajectory analysis, and variational formulations for image filtering inspired by quantum physics. His team focuses on: Dynamic texture modeling via spatio-temporal decomposition Low-level image processing with information theory Through-wall imaging systems using UWB radar Information fusion techniques with minimal a priori assumptions Applications in coastal environment monitoring and biomedical imaging Publications Ménard's publications reflect his expertise in advanced image processing techniques applied to diverse domains. Notable contributions include: Theoretical works on total variation and sublinear functionals Algorithm developments for multistatic radar systems Applications in 3D bee tracking and cardiovascular flow analysis Extensions of Chambolle's algorithm to color images Decomposition methods for dynamic textures Integration of quantum physics concepts in image filtering Collaborations He collaborates with: Laboratoires: L3i, MIA, CLDG/BQR, IRPHE CNRS, ETIS, LASIE Institutions: University Hospitals of Poitiers and Angers, ONERA, LEAT, Tronico Researchers: Abdallah El-Hamidi, Alain Gaugue, Damien Coisne, Gilles Aubert Teaching Ménard teaches across eight departments/programs including: Electronics and Industrial Computing Automation Network Security and Cryptography Video Game Programming Smartphone Programming Digital Media Distribution He has developed new educational initiatives in mobile application programming since 2010.

Frédérique Ponceمشاهده پروفایل

پژوهشگر

• Cancerology
• Internal Medicine
• Veterinary Oncology
• +۳ مورد دیگر

Frédérique Ponce is a researcher at VetAgro Sup , affiliated with the Veterinary Campus and the Companion Animal Clinic, Leisure and Sport department. Her work bridges veterinary oncology and translational medicine through the UR ICE (Cell Environment Interactions) research unit and the Veterinary University Hospital (CHUV) for companion animals. Created France's only Master's degree in veterinary oncology and European specialist training program (ECVIM-CA) in 2011 Her research focuses on cancerology and internal medicine , particularly canine lymphomas, brain tumors, and nanotechnology applications. Key projects include OSTEHOMDOG (osteosarcoma microenvironment), TheraneaM (glioblastoma brachytherapy), and DELICE (human glioblastoma pig model). Publications emphasize prognostic biomarkers , fluorescence imaging , and cross-species cancer models . Technical expertise includes induced tumor models (e.g., glioma xenografts in pigs) and nanoparticle-based diagnostics , supported by collaborations with institutions like CLB, CLARA, and CLARAFT.

Sylvie Coste-Marquisمشاهده پروفایل

مدرس

• Knowledge Representation and Reasoning
• Artificial Intelligence
• Argumentation Frameworks
• +۵ مورد دیگر

Sylvie Coste-Marquis is a Lecturer at the Institute of Technology of Lens, part of the University of Artois. Her research focuses on knowledge representation, argumentation frameworks, and computational logic, particularly in artificial intelligence applications. PhD in Computer Science (1994) from Henri Poincaré University of Nancy Co-supervised three PhD theses on argumentation systems, QBF, and speech recognition Administrative roles: Vice-President for Digital Affairs (2016-), Project Manager (2012-2016), and Head of IT Department (2010-2013) Her work explores abstract argumentation, belief revision, and quantified Boolean formulae, with recent contributions to enforcing extensions via optimization and translating argumentation frameworks. She actively participates in program committees for top-tier conferences like IJCAI and COMMA. She has contributed to ANR projects on multi-agent argumentation, configurable product recommendation, and preference handling in combinatorial domains. Her teaching includes modules on computer systems, object-oriented design, and AI, with recognized innovations in educational methods.

• Theoretical Linguistics
• Syntax
• Comparative Syntax
• +۴ مورد دیگر

Luigi Rizzi is a Professor of General Linguistics at the Collège de France, focusing on theoretical syntax, syntactic cartography, and language acquisition. He has held academic positions at the University of Siena, the University of Geneva, MIT, UCLA, and ENS Paris. Research Areas : Universal principles in syntax, parametric models of linguistic variation, mapping of syntactic structures, and the interplay between adult grammar and language acquisition. Awards : Corresponding Fellow of the British Academy, Honorary Member of the Linguistic Society of America, Member of Academia Europaea, and International Honorary Member of the American Academy of Arts and Sciences. Publications span syntactic theory, minimalist program, and acquisition studies, including works on intervention effects, left periphery cartography, and the role of Merge in syntactic combinatorics. His teaching integrates adult grammar with developmental perspectives, emphasizing hierarchical representations and functional sequences.

Bruno Gaujalمشاهده پروفایل

استاد پژوهشی

• Performance Evaluation
• Optimization
• Discrete Event Dynamic Systems
• +۵ مورد دیگر

Bruno Gaujal is a Research Professor at Inria Grenoble-Rhône-Alpes, affiliated with Université Grenoble Alpes. He obtained his PhD from the University of Nice in 1994 under François Baccelli's supervision and has held positions at AT&T Bell Labs, INRIA, and École Normale Supérieure de Lyon. He previously led the MESCAL (now POLARIS) research group focused on large-scale computing until 2015. His research interests center on performance evaluation, optimization, and control of discrete event dynamic systems with stochastic inputs. Specific areas include: Markov Chains and Markov Decision Processes Reinforcement Learning and stochastic optimization Queueing theory and scheduling algorithms Energy-efficient computing in distributed systems Game-theoretic approaches in network optimization Gaujal's recent publications show strong emphasis on reinforcement learning applications in queueing networks, energy optimization for real-time systems, and scalable algorithms for Markov Decision Processes. His work bridges theoretical frameworks like Whittle indices with practical implementations in cloud computing and distributed systems. He has supervised numerous PhD students including Nicolas Gast (now Inria researcher), Anne Bouillard (Huawei researcher), and Emmanuel Hyon (Paris Nanterre professor). Current students include Hélène Arvis and Romain Cravic. Gaujal co-founded RTaW, a startup specializing in real-time network design tools. At Inria, he leads research in the POLARIS group, focusing on optimization methods for large-scale distributed computing infrastructures. His work involves collaborations with 85+ co-authors across institutions globally.

Matthieu Bonnivardمشاهده پروفایل

استاد

• calculus of variations
• phase-field methods
• homogenization
• +۷ مورد دیگر

Matthieu Bonnivard is a Full Professor of Applied Mathematics at École Centrale de Lyon, affiliated with the Institut Camille Jordan (UMR CNRS 5208). He holds a habilitation à diriger des recherches (HDR, Université Paris Cité, 2022) and a PhD in Mathematics (Université de Grenoble, 2010), co-supervised by Dorin Bucur and Ioan Ionescu. His research focuses on two primary areas: Approximation of length minimization problems in the calculus of variations using phase-field and asymptotic analysis techniques Fluid-structure interaction models in fluid mechanics, particularly for non-Newtonian and micropolar fluids with applications in nanofluidics and lubrication He actively contributes to homogenization theory for rough domains and develops numerical algorithms for geometric optimization problems like the Steiner tree. Current projects include the ANR STOIQUES study of Carreau fluids in thin porous media. He teaches in the Mathématiques et Ingénierie du Risque (MIR) program and co-supervises PhD candidate Eve Machefert. Key research trends include: Non-Newtonian fluid modeling through Carreau laws Asymptotic analysis of rough boundaries Phase-field approximations for geometric problems Slip length derivation in nanofluidics Homogenization of turbulent flow models

Denis Kuperbergمشاهده پروفایل

پژوهشگر

• automata theory
• synthesis
• verification
• +۶ مورد دیگر

Denis Kuperberg is a CNRS researcher at LIP (Laboratoire de l'Informatique du Parallélisme), ENS Lyon, where he is part of the Plume research team. His work bridges theoretical computer science with interdisciplinary applications, particularly in systems biology. His primary research interests include automata theory, synthesis, verification, games, logics, decidability procedures, complexity, and proof theory. Kuperberg's work often explores the connections between formal methods and practical applications, as evidenced by his recent interdisciplinary publications on thermodynamic consistency of autocatalytic cycles. His research demonstrates a strong focus on both theoretical foundations and practical implementations, with several software tools developed for research purposes. Kuperberg's publication record shows a clear evolution from purely theoretical work in automata and logic toward more interdisciplinary research. His recent papers (2022-2025) demonstrate increasing engagement with biological applications while maintaining strong theoretical foundations. Key themes across his publications include history-determinism in automata, cyclic proof systems, positive fragments of logic, and applications to verification problems. Best Paper Award at ICALP 2025 (with Thomas Colcombet and Amina Doumane) Kuperberg actively supervises multiple students at various levels, including PhD candidates (Iris Magniez–Papillon, Émile Hazard, Laureline Pinault), postdoctorates (Thomas Kosc, Marc Bagnol), and numerous interns. His teaching portfolio includes advanced courses on Graphs, Machines and Logics at ENS Lyon Master 2 program, Theory of Regular Languages at EPITA Lyon, and specialized topics like Mathematical Aspects of Automata Theory. He leads the development of research software including EmergeNS (for simulating chemical reaction networks and tracking autocatalytic dynamics), Stamina and Acme (for solving decision problems from automata theory), and Electrum (a specification language analyzer). These tools demonstrate his commitment to bridging theoretical computer science with practical implementations.

Gwladys Pourceauمشاهده پروفایل

استاد

• Green Chemistry
• Carbohydrate Chemistry
• Sustainable Development
• +۴ مورد دیگر

Professor Gwladys Pourceau is a leading researcher in sustainable chemistry at the University of Picardie, France, affiliated with the Glycochemistry and Agroresources Laboratory of Amiens (LG2A UR 7378). Her work bridges fundamental carbohydrate chemistry with practical applications in green materials development. Her research focuses on eco-compatible chemical transformations of saccharides , with particular expertise in gold nanocatalysis for selective sugar oxidation and mechanosynthesis of bio-based polymers. Key projects include developing one-pot oxidative amidation methods for unprotected carbohydrates, synthesizing low-irritant sugar-based surfactants, and converting lignin derivatives into sustainable polystyrene alternatives. Her methodologies consistently emphasize minimal catalyst loading , solventless conditions , and renewable feedstocks . Professor Pourceau's publication record demonstrates significant impact in sustainable chemistry, with multiple high-impact papers in 2024 alone covering gold-catalyzed oxidations, cyclodextrin-assisted reductions, and structure-property relationships in sugar surfactants. Her work shows strong interdisciplinary connections between organic chemistry, materials science, and environmental engineering. Developed gold-catalyzed oxidation achieving TOF > 750,000 h -1 Created solventless mechanosynthesis methods reducing reaction times to 5 minutes Designed sugar-based surfactants with significantly lower cytotoxicity than commercial alternatives Established solar-driven methodologies for carbohydrate transformations Her laboratory maintains active collaborations across European institutions, focusing on translating fundamental carbohydrate chemistry into practical sustainable technologies. The research program demonstrates consistent funding through high publication output and methodological innovations addressing critical challenges in green chemistry.

• Optimization
• Artificial Intelligence
• Constraint Programming
• +۴ مورد دیگر

Rui SA SHIBASAKI is a Lecturer at the University of Picardie Jules Verne (UPJV), France, affiliated with research unit UR 4290. Her work bridges optimization theory and industrial applications, with a growing focus on sustainable manufacturing and robust decision-making under uncertainty. She maintains active collaborations across France, Brazil, and the USA. Her research spans optimization , artificial intelligence , and operations research , specializing in constraint programming applications for assembly line balancing, network design, and energy efficiency. Recent work innovates by applying MaxSAT solvers to combinatorial problems and developing robustness metrics for production systems. Analysis of her 13 publications (2020-2025) reveals three dominant trends: (1) Energy-aware manufacturing optimization (40% of recent work), (2) Advanced decomposition techniques for network problems (30%), and (3) Diversified solution approaches for satisfiability (20%). Her research increasingly addresses sustainability through energy peak minimization in production systems. Dr. Shibasaki actively supervises graduate students and collaborates internationally with researchers from Federal University of Minas Gerais (Brazil), CNRS (France), and IBM Research (USA). Her work is supported by competitive grants enabling participation in major conferences like ROADEF and IEEE ICTAI. As part of UPJV's UR 4290 research unit, she contributes to optimization and cryptography initiatives. While no formal lab name is specified, her work centers on developing practical AI-driven optimization tools for industrial challenges, particularly in manufacturing and logistics.

Brice Chardinمشاهده پروفایل

دانشیار

• Data Engineering
• Clustering Algorithms
• Query Relaxation
• +۵ مورد دیگر

Brice Chardin is an Associate Professor in Data Engineering at ISAE-ENSMA since 2013, affiliated with the LIAS (Laboratoire d'Ingénierie des Applications de la Connaissance et des Systèmes) Data and Model Engineering team. His work bridges academic research and industrial applications, focusing on data management solutions for critical systems. His research spans clustering algorithms under dissimilarity constraints , RDF query relaxation for explaining empty/overabundant results, pattern mining through the RQL language, and energy data management . Key projects include Chronos (a NoSQL system for industrial sensor data) and collaborations with energy companies SRD and Nexeya for predictive consumption analysis. Recent publications (2021-2024) emphasize constrained clustering techniques and cooperative query processing for RDF knowledge bases, revealing a strong trend toward practical solutions for industrial data challenges. His work integrates machine learning with database theory to address real-world data imperfections. PhD in Computer Science from INSA Lyon (2011) Postdoctoral position at LIRIS (2012-2013) on ANR DAG project Specialized in industrial data management since 2011 EDF collaboration Chardin actively supervises academic projects including drone simulation with Ardupilot and Smart Data mining initiatives. His industrial partnerships focus on energy sector applications, particularly predictive analysis for electricity distribution and storage systems. Current work involves developing clustering algorithms with error bounds and query relaxation frameworks for semantic web technologies.

Guillaume Mercereمشاهده پروفایل

استاد

• System Identification
• Model Learning
• Automatic Control
• +۶ مورد دیگر

Guillaume Mercere is a Full Professor in Automatic Control and Systems at ENSIP (Ecole Nationale Supérieure d'Ingénieurs de Poitiers), University of Poitiers. He maintains dual affiliations with Laboratory LIAS at both ENSIP in Poitiers and ISAE-ENSMA in Chasseneuil, conducting research in system identification and control theory. Professor Mercere teaches automatic control and signal processing at the Master's level, with additional expertise in numerical optimization, machine learning, and time series analysis. His teaching materials are available upon request, reflecting his commitment to educational transparency. Research Focus: Model learning, system identification, estimation theory, state space modeling, gray box modeling, linear parameter varying (LPV) systems, linear fractional representation (LFR), and subspace-based methods Application Areas: Electrical engineering, aeronautics, heat transfer, flexible/cable-driven manipulators, vehicle tire/road interactions, and image processing Analysis of his recent publications reveals a strong emphasis on recursive estimation methods (particularly total least squares), theoretical developments in LPV system representations, predictive control methodologies, and noise covariance estimation for Kalman filtering. His work bridges theoretical advances in identification methodologies with practical applications across multiple engineering domains, demonstrating both depth and breadth in his research program. Professor Mercere leads the Automatic Control Team at Laboratory LIAS, where he collaborates with researchers on theoretical and applied projects. His research group focuses on developing identification methodologies with practical implementation in real-world engineering systems, maintaining an active publication record through 2025 that demonstrates ongoing contributions to the field of system identification and control engineering.