جست‌وجوی استاد و پژوهشگر

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

Omar Rifki is an Associate Professor (Maître de Conférences) specializing in combinatorial optimization and artificial intelligence applications. His research bridges theoretical computer science with practical logistics challenges, focusing on routing problems, process mining, and machine learning integration for complex decision systems. His core research interests include phase transitions in NP-hard problems, vehicle routing optimization under time constraints, and healthcare process modeling. Rifki's work demonstrates a consistent pattern of integrating reinforcement learning with traditional optimization techniques to solve large-scale real-world problems in transportation and logistics, with particular emphasis on spatio-temporal data effects and collaborative systems. Analysis of his 15 publications (2019-2025) reveals three dominant research thrusts: (1) Fundamental studies of combinatorial problem hardness using phase transition frameworks, (2) Practical applications of deep reinforcement learning in vehicle routing and taxi assignment, and (3) Healthcare process optimization through advanced process mining techniques. His work consistently addresses scalability challenges in real-world implementations while maintaining theoretical rigor. No scientific awards were documented in the provided materials. His collaborative work with researchers like Christine Solnon and Thierry Garaix indicates active participation in European operations research communities, though specific grant details remain unreported. Rifki's research shows increasing integration of graph theory and machine learning in transportation applications, particularly evident in his Lyon City case studies on autonomous ride-sharing systems.

• Theoretical Deep Learning
• Uncertainty Quantification
• Reliable Artificial Intelligence
• +۱۰ مورد دیگر

Gianni Franchi is an assistant professor at ENSTA Paris , affiliated with the Computer Science and Systems Engineering Unit (U2IS) . His work focuses on theoretical deep learning , with a strong emphasis on uncertainty quantification, robustness, and explainability in machine learning models. Current affiliation: ENSTA Paris (U2IS) Academic rank: Assistant Professor Key collaborators: David Filliat, Emanuel Aldea, Andrei Bursuc, Antoine Manzanera His research spans uncertainty quantification , explainable AI , and reliable machine learning . He investigates methods like Bayesian neural networks, ensemble approaches, and deterministic uncertainty models. His work also addresses domain adaptation , self-supervised learning , and autonomous systems , particularly in trajectory forecasting and semantic segmentation for autonomous driving. Recent publications analyze probabilistic modeling for robustness, symmetry-aware Bayesian methods , and multi-modal datasets like InfraParis. He develops frameworks like Torch-Uncertainty and benchmarks such as MUAD for uncertainty types in autonomous driving. Key themes: Uncertainty Quantification Deep Learning Theory Autonomous Systems Explainable AI Dataset Creation Bayesian Methods

Nadjib BRAHIMIمشاهده پروفایل

دانشیار

• Supply Chain Management
• Production Planning and Scheduling
• Operations Research
• +۴ مورد دیگر

Nadjib Brahimi is an Associate Professor at Rennes School of Business, specializing in Supply Chain Management and Operations Research. He holds a PhD in Production and Logistics Systems from Université de Nantes and has extensive academic experience, including roles as Academic Head of the Supply Chain Management & Information Systems department (2019–2020) and Programme Manager of the MSc in Data and Business Analytics. His expertise spans Production Planning, Supply Chain Optimization, and Reconfigurable Manufacturing Systems. Education: PhD in Production and Logistics Systems, Université de Nantes, 2004 M.Sc. in Automatic Control and Applied Computing, Ecole Centrale de Nantes, 2001 Engineering Degree in Electronics and Electrical Engineering, National Institute of Electronics and Electrical Engineering, Boumerdes, Algeria, 1999 Research Focus: His work emphasizes optimization of production systems, reconfigurable manufacturing, and sustainable supply chains. Key areas include task reassignment in assembly lines, biomass supply chain design, and disruption resilience strategies. Key Contributions: Over 40 peer-reviewed articles in journals like International Journal of Production Economics and European Journal of Operational Research . Developed robust optimization models for supplier selection and production routing. Advocated for circular economy principles in hybrid manufacturing systems. Labs/Teams: He collaborates with research groups on reconfigurable manufacturing systems and bioenergy supply chain design, contributing to projects in France, UAE, and Algeria.

• Supply Chain Management
• Operations Research
• Sustainable Operations
• +۴ مورد دیگر

Dr. Reza Alikhani is an Assistant Professor of Operations and Supply Chain Management at the MBS School of Business in France. He holds a PhD in Production and Operations Management from the University of Tehran and has held postdoctoral and visiting research positions at EM Normandie Business School and DePaul University. His primary research focuses on applying operations research methodologies to optimize supply chain and logistics systems, with expertise in supply chain resilience, sustainable operations, and collaborative logistics. Education and Positions: PhD in Production and Operations Management, University of Tehran, Iran (2018) Postdoctoral Researcher at EM Normandie Business School Visiting Research Fellow at DePaul University, Chicago Research Interests: Dr. Alikhani’s work emphasizes supply chain resilience , sustainable operations , and collaborative logistics optimization . His studies often integrate fuzzy logic, stochastic programming, and multi-objective models to address uncertainties and strategic decision-making in supply chains. Recent trends in his publications highlight advancements in concurrent resilience strategies and cross-industry collaboration for logistics efficiency. Collaborations and Industry Links: He actively collaborates with international companies to enhance operational efficiency through innovative supply chain solutions. This bridges academic research with real-world applications, particularly in retail logistics and humanitarian relief considerations. Professional Contributions: Dr. Alikhani serves as an ad hoc reviewer for top-tier journals such as Production and Operations Management and European Journal of Operational Research , contributing to the rigor of supply chain literature.

Camilla Fioriniمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics
• Numerical Analysis
• Partial Differential Equations
• +۴ مورد دیگر

Camilla Fiorini is an Associate Professor at the National Conservatory of Arts and Crafts (CNAM) in Paris, where she conducts research at the Mathematical and Numerical Modeling Laboratory (M2N). She serves as Principal Investigator for the ANR-funded SPARCL project (2025-2029) focusing on structure-preserving reduced order models for conservation laws. Her academic background includes a PhD in Applied Mathematics from the University of Versailles and both MSc/BSc degrees in Mathematical Engineering from Politecnico di Milano. Her research centers on computational fluid dynamics, numerical analysis of PDEs, and sensitivity methods, with specific applications in uncertainty quantification and reduced order modeling. Current projects develop novel approaches for conservation laws that maintain structural properties while improving computational efficiency and reliability. Fiorini's publication record demonstrates consistent focus on sensitivity analysis techniques for complex fluid systems, shock-capturing methods, and uncertainty propagation in hyperbolic PDEs. She received the SMAI-GAMNI PhD Award 2019 (French ECCOMAS Award) for her doctoral dissertation on sensitivity analysis for nonlinear hyperbolic systems. As Principal Investigator of the SPARCL project, she leads a team developing new reduced basis construction techniques for conservation laws. Fiorini actively advises graduate researchers including PhD students Nathalie Nouaime (2021-2024) and Nicolas Lepage (2022-present), plus multiple Master's candidates. Her research group collaborates with institutions including Inria, Sorbonne University, ONERA, and CEA. Current projects include ANR JCJC-funded SPARCL and ANR AHEAD initiatives. She leads the SPARCL research group at M2N laboratory, collaborating with researchers including Alessia Del Grosso, Iraj Mortazavi, and Taraneh Sayadi on reduced order modeling techniques. The team focuses on developing computationally efficient ROMs that preserve physical structures in conservation laws.

Yohan PETETINمشاهده پروفایل

دانشیار

• Monte Carlo methods
• Bayesian filtering
• Hidden Markov models
• +۵ مورد دیگر

Yohan PETETIN is an Associate Professor at Telecom SudParis (Institut polytechnique de Paris) in the CITI Department. His research focuses on Bayesian filtering, Monte Carlo methods, hidden Markov models, and multi-object tracking. He has authored over 20 peer-reviewed articles since 2011, with notable contributions in IEEE Transactions on Signal Processing and other top venues. His work bridges statistical signal processing with machine learning applications. PhD: Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles Markoviens (2013, Telecom SudParis) HDR: Generative models for time series data (2023, Institut polytechnique de Paris) Research interests emphasize sequential Monte Carlo algorithms, particle filtering optimizations, and deep learning integration for time-series analysis. Recent work explores expressivity comparisons between recurrent neural networks and hidden Markov models. Teaching includes courses on probabilistic graphical models, Bayesian filtering, and deep learning across undergraduate and graduate programs at Telecom SudParis and affiliated institutions.

• Explainable Artificial Intelligence
• Machine Learning
• Data Clustering
• +۴ مورد دیگر

Nicolas Labroche serves as a Researcher in the Department of Computer Science at the Polytechnic School of the University of Tours. He is affiliated with the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT) within the UFR of Sciences and Technology. His academic work spans multiple research domains with significant contributions to machine learning methodology and applications. His primary research focuses on Explainable Artificial Intelligence , particularly for medical applications where model interpretability is critical. He has pioneered methods in clustering of local attributive explanations and discernibility in medical ML models . Additional expertise includes exploratory data analysis through mathematical programming for comparison queries, time series forecasting for environmental monitoring, and preference-based explanations in recommender systems. His work bridges theoretical computer science with practical applications in healthcare, environmental science, and business intelligence. Analysis of his publication trends reveals consistent innovation in making data exploration more efficient and interpretable. Recent work emphasizes user-centric explainability (2024-2025), mathematical optimization for analytics (2022-2023), and synthetic data generation (2022). His research demonstrates strong interdisciplinary connections between computer science, medicine, and environmental science. His laboratory affiliation with LIFAT provides the infrastructure for his computational research. While specific grant details aren't public, his publication record indicates sustained research productivity across multiple European-funded projects focused on data science applications.

Thibault Tricardمشاهده پروفایل

دانشیار

• Computer Graphics
• Procedural Modeling
• 3D Printing
• +۵ مورد دیگر

Thibault Tricard is an Associate Professor at Ensimag Grenoble and a permanent researcher in the Maverick team at Inria Grenoble. His work bridges Computer Graphics and Additive Manufacturing , focusing on procedural methods for virtual world content generation. His research interests include: Procedural modeling for surface and microstructure details Mesh shaders and GPU-accelerated volume rendering 3D printing optimization with staggered infill structures Recent publications like Interval Shading (HPG 2024) and Procedural Phasor Noise (ACM Siggraph 2020) highlight trends in real-time rendering and fabrication-aware procedural synthesis. Awards include the GDR IG-RV Thesis Award 2023 . He advises PhD students and collaborates with researchers such as Sylvain Lefebvre and Hans-Peter Seidel. Teaching includes GP-GPU High-Performance Computing and 3D Graphics at Inp Grenoble and UGA Mosig. Personal projects like the LavaCake Vulkan library emphasize developer accessibility.

• Operational Research
• Scheduling
• Production Planning
• +۳ مورد دیگر

Mr. Jean-Charles Billaut is a Professor at the Polytechnic School of Tours (EPU) within the University of Tours, affiliated with the Computer Science Department and the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT). His primary research focuses on Operational Research, particularly in scheduling theory, production planning, and logistics optimization, with notable contributions to healthcare and food supply chain systems. He has held leadership roles, including Director of the Computer Science Laboratory since 2007 and Editor-in-Chief of the European Journal of Operational Research since 2007. His work bridges theoretical advancements and real-world applications, addressing challenges in multi-agent scheduling, robust production systems, and emergency logistics. Key collaborations include optimizing chemotherapy production and medical sample dispatching, reflecting his commitment to impactful operational research. His research often employs metaheuristics and exact methods to solve complex scheduling and routing problems, emphasizing sustainability and resilience in supply chains.

• Production Systems Engineering
• Supply Chain Design and Optimization
• Operational Research
• +۳ مورد دیگر

Professor Alexandre Dolgui is the Director of the Department of Automation, Production, and Computer Science (DAPI) at IMT Atlantique, France. A Fellow of IISE and Highly Cited Researcher (Clarivate), he serves as Editor-in-Chief of the International Journal of Production Research. His expertise spans production systems, supply chain engineering, and operational research, with over 360 journal articles and 37 books. Education: HDR (University of Technology of Compiègne, 2000), PhD in Cybernetics (Academy of Science of Belarus, 1990) Research focuses on production system design , supply chain resilience , and discrete optimization , addressing challenges in human-robot collaboration, Industry 5.0, and yield uncertainty. His work integrates machine learning with stochastic programming for adaptive manufacturing solutions. Key contributions include constraint programming models for assembly lines, robust optimization frameworks for biorefineries, and digital twin methodologies for supply chain risk management. He has received multiple best paper awards and led international research projects like DREAMS and DISC. Scientific leadership: Board member of IFPR, Senior advisor to Chinese Academy of Sciences, founder of EVI European Virtual Institute

• Operations Research
• Decision Analytics
• Supply Chain Management
• +۵ مورد دیگر

Laurent ALFANDARI is a Full Professor at ESSEC Business School, specializing in Operations Research and Decision Analytics within the Information Systems, Decision Sciences and Statistics (IDS) Department. He has held key academic roles including Academic Co-Director of the ESSEC-CentraleSupélec Master in Data Sciences & Business Analytics (2019–2023) and Coordinator of the Operations & Data Analytics PhD concentration (2018–2021). His research focuses on optimization techniques applied to supply chains, logistics, healthcare, and disaster preparedness. He has authored over 50 journal articles in top venues like European Journal of Operational Research and Transportation Science. Education : - Doctorate in Operations Research (Université Paris Dauphine-PSL, 1999) - Master of Research in Management Science (Université Paris Dauphine-PSL, 1995) - M.Sc in Management (ESSEC Business School, 1993) - Bachelor in Mathematics & Social Sciences (Université Paris Dauphine-PSL, 1990) Research Interests : His work addresses complex optimization challenges in urban logistics, healthcare networks, and disaster response. Notable contributions include freight-on-transit systems, autonomous robot delivery, and pandemic intervention sequencing. He emphasizes practical solutions for real-world problems through mixed-integer programming and robust optimization frameworks. Teaching & Mentoring : Teaches Decision Analytics and Operations Research across ESSEC programs (Grande École, Executive MBA, PhD). Supervised 14 PhD theses, including recent works on sustainable last-mile deliveries and healthcare analytics. Awards & Activities : 2020 ESSEC Top 4 Teaching Award, Vice-President of ROADEF (2012–2015), and leader in industrial collaborations with SNCF and Aid-Impact. Active in organizing academic conferences and serves as a journal reviewer for Annals of Operations Research and others. Labs & Collaborations : Member of LIPN (Sorbonne Université) and LAMSADE (Paris Dauphine). Consults for organizations like Babcock-Wanson and contributes to public-sector projects like EDF's ROADEF challenge.

• Statistical Learning
• High-Dimensional Data Analysis
• Network Statistics
• +۷ مورد دیگر

Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur , Nice, France, and holds a Chair in Artificial Intelligence. He serves as Director of the Institut 3IA Côte d’Azur and leads the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. He is an associate editor for The Annals of Applied Statistics and founded the Statlearn workshops . Research Interests: Statistical learning in high dimensions Learning on networks and functional data Deep latent variable models Adaptive learning with uncertain labels Applications in Medicine, Image Analysis, Astrophysics, and Humanities Notable Contributions: Developed multiple R packages including HDclassif , FisherEM , and FunLBM . Created the Linkage.fr platform for network analysis with textual edges. PhD Students: Current: Seydina Niang (Deep Generative Models), Kilian Burgi (Marine Diversity Monitoring), Baptiste Pouthier (Multimodal Learning) Former: Giulia Marchello (Dynamic Networks), Rémi Boutin (Network Analysis), Dingge Liang (Recommender Systems), Nicolas Jouvin (Latent Variable Models), Alexandre Saint-Dizier (Image Aggregation), Warith Harchaoui (Optimal Transport), Pierre-Alexandre Mattei (Sparse Clustering), Rawya Zreik (Temporal Networks), Anastasios Bellas (Anomaly Detection), Camille Brunet (Sparse Clustering) Contact: Email: charles.bouveyron@univ-cotedazur.fr / charles.bouveyron@inria.fr Postal: Equipe Maasai, Inria Sophia Antipolis, 2004 route des Lucioles, 06902 France

José Netoمشاهده پروفایل

استاد

• Combinatorial Optimization
• Mathematical Programming
• Operations Research
• +۳ مورد دیگر

Prof. José Neto is a Professor at Télécom SudParis, part of the Institut Polytechnique de Paris, affiliated with the SAMOVAR research department. His work focuses on combinatorial optimization, mathematical programming, and graph theory. He has contributed to polyhedral studies of cut and assignment polytopes, spectral bounds for graph partitioning, and optimization algorithms for mixed-variable and blackbox problems. His research spans topics like network pricing complexity, domination in graphs, and robustness verification in neural networks. He has published extensively in top journals such as Mathematical Programming, Discrete Applied Mathematics, and Networks. Key contributions include developing efficient algorithms for combinatorial pricing, analyzing optimization relaxations, and exploring structural properties of discrete mathematical objects. His work bridges theoretical foundations and practical applications in operations research, with recent interests in binarized neural network verification and mixed-variable optimization. He has collaborated on projects related to cloud virtual machine mapping and combinatorial pricing models. Labs/Teams: Active member of the SAMOVAR laboratory, specializing in applied mathematics and optimization research.

Kaniav Kamaryمشاهده پروفایل

پژوهشگر

• Bayesian Inference
• Bayesian Model Choice
• Prior Distribution
• +۳ مورد دیگر

Kaniav Kamary is a researcher specializing in Bayesian inference and mixture distribution modeling, affiliated with the Laboratory of Mathematics and Computer Science for Complexity and Systems. Their work bridges computational statistics, probabilistic modeling, and applied mathematics. Research Interests Kamary's research focuses on Bayesian model choice Non-informative prior development Reparameterization techniques for mixture models Statistical validation of computational methods Scientific Contributions Recent publications address methodological challenges in Bayesian inference and applications in health informatics and environmental modeling.

Shahin Gelarehمشاهده پروفایل

دانشیار

• Operations Research
• Logistics and Supply Chain Management
• Transportation Planning
• +۹ مورد دیگر

Shahin Gelareh is an Associate Professor in Operations Research and Logistics at the Université d'Artois, France, affiliated with the Department of Networks and Telecommunications at the IUT de Béthune. He holds a PhD in Optimization from the Technical University of Kaiserslautern (2008), an MSc in Applied Mathematics from the University of Sistan and Baluchestan (2005), and a BSc from Ferdowsi University of Mashhad (2003). His research focuses on Locational Analysis , Integer Programming , Metaheuristics , Global Network Design , and applications in Maritime Transport , Supply Chain , and Telecommunications . Notable areas include hub-and-spoke network optimization, liner shipping fleet deployment, and competitive analysis using game theory. Recent publications emphasize innovations in Neural Benders Decomposition for optimization, cross-docking systems , and hub location routing . His work spans theoretical advancements and industry collaborations in transportation and telecom sectors. Professional activities include memberships in leading OR societies (EWGLA, EWGT, VeRoLog) and roles as an ad-hoc reviewer for high-impact journals like Transportation Research Part E and European Journal of Operational Research .