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Safia Kedad-SidhoumView profile

Professor

• Operations Research
• Combinatorial Optimization
• Linear Programming
• +6 more

Safia Kedad-Sidhoum is a Full Professor at the Conservatoire National des Arts et Métiers (CNAM), affiliated with the CEDRIC Laboratory's Combinatorial Optimization team. Her research focuses on Operations Research, particularly in combinatorial optimization, scheduling, lot-sizing, energy management, and supply chain optimization. She holds a Ph.D. from École Centrale Paris (1997) and an HDR from UPMC (2010). She has held academic positions at UPMC from 1999 to 2017, including roles as Associate and Assistant Professor, before becoming a Full Professor at CNAM in 2018. She has extensive industrial experience, including as a Supply Chain Planning Project Manager at Dynasys (1998–1999). She co-leads the Master Parisien de Recherche Opérationnelle (MPRO) and has advised numerous Ph.D. students. Her work includes projects funded by Google, CNRS, and FUI initiatives, addressing energy optimization, remanufacturing, and supply chain efficiency. She actively participates in academic societies such as EURO/ROADEF, IWLS, and serves on editorial boards. Her teaching spans scheduling, optimization, and mathematical tools at CNAM and UTC.

Olivier BailleuxView profile

Research Professor

• Constraint Programming
• Stochastic Local Search
• Genetic Algorithms
• +3 more

Olivier Bailleux is a Research Professor at the University of Burgundy within the Faculty of Science and Technology . His work focuses on computational logic and optimization. Teaching: C/C++ programming, constraint programming, logical programming Research: SAT resolution, constraint decomposition, genetic algorithms Team: Data Science Research Interests: SAT solvers, constraint programming, and algorithmic optimization. His projects explore efficient translations of complex constraints into Boolean models. Publications span topics like Pseudo-Boolean encoding, DPLL/CDCL algorithm comparisons, and minimal resolution refutations, reflecting interdisciplinary work in logic and artificial intelligence.

Nicolas BarnierView profile

Researcher

• Constraint Programming
• Combinatorial Optimization
• Air Traffic Management
• +3 more

Nicolas Barnier is a Researcher at the National School of Civil Aviation (ENAC) in the AI Axis . His work focuses on Constraint Programming , Combinatorial Optimization , and Air Traffic Management , particularly for Conflict Resolution and Deconfliction in aviation systems. Academic Affiliation : ENAC, MAIAA-OPTIM Team Key Collaborations : Cyril Allignol, Ruixin Wang, Nicolas Durand Nicolas Barnier’s research spans decades, integrating Evolutionary Computation , Graph Coloring , and Hybrid Optimization Techniques for air traffic flow and mission planning. He co-developed the FaCiLe Constraint Library and advanced models for 4D Trajectory Deconfliction , Flight Level Allocation , and UAS Detect & Avoid Systems . His publications highlight applications in Large-Scale 3D Conflict Resolution , Robust Gate Allocation , and Mission Planning for Micro Drones . Though no explicit awards are listed, his sustained contributions to constraint programming and air traffic management through 50+ publications and collaborations with institutions like FAA , Eurocontrol , and IEEE underscore his impact.

Mohammed SbihiView profile

Research Professor

• Operations Research
• Mixed Integer Optimization
• Digital Optimization
• +4 more

Mohammed Sbihi is a Research Professor at the Ecole Nationale de l'Aviation Civile (ENAC), affiliated with the OPTIM team within the MORO axis. He serves as Head of the Operational Research Masters program and focuses on mixed integer optimization, digital optimization, and robustness of machine learning algorithms for air traffic management applications. PhD in Mathematics from Université de Franche-Comté (2005) HDR (Habilitation) in Mathematics of Optimization and Operational Research His research integrates mathematical optimization and machine learning to address air traffic control challenges, including trajectory prediction, runway scheduling, and uncertainty management. Recent work explores robust SVM in Banach spaces and Lipschitz constant estimation for ReLU networks. Key publication trends span 2005–2025, covering air traffic optimization (2005–2025), robust machine learning (2022–2024), and industrial machining optimization (2016–2021). Collaborative projects involve co-authors like Daniel Delahaye, Marcel Mongeau, and Ji Ma.

• Combinatorial Optimization
• Applied Mathematics
• Integer Linear Programming
• +3 more

Diego DELLE DONNE is an Assistant Professor (tenure-track) at ESSEC Business School in Paris, France, within the Information Systems, Data Analytics and Operations department. He holds a PhD in Computer Science from the University of Buenos Aires (2016), along with earlier degrees from the same institution. Prior to his current role, he held post-doctoral positions at ESSEC, École Polytechnique, and Sorbonne Paris Nord University. His research focuses on combinatorial optimization, applied mathematics, and their real-world applications in logistics, urban planning, and resource management. Education: PhD in Computer Science, University of Buenos Aires (2016) MSc in Computer Science, University of Buenos Aires (2009) BSc in Computer Science, University of Buenos Aires (2008) Research Interests: His work emphasizes optimization algorithms for sustainable urban logistics, such as integrating public transport for last-mile deliveries to reduce CO2 emissions, and integer linear programming applications in hydrocarbon field planning, waste management, and salmon farm operations. He also explores theoretical challenges in graph coloring, polyhedral studies, and sparse approximation techniques. Recent Article Trends: Recent publications highlight advancements in column generation methods for multi-echelon delivery systems, branch-and-price algorithms for clustering and coloring problems, and strategic planning for freight integration with public transit. His work bridges theoretical optimization with practical urban and industrial challenges, emphasizing sustainability and efficiency. Advising & Grants: He has supervised multiple master’s theses, including works on leaf collection optimization and logistics routing. While specific grant details are not detailed, his research projects reflect collaboration with industry partners like petroleum companies in Argentina and urban management initiatives. Labs/Teams: His current project focuses on leveraging public transport’s excess capacity for urban parcel delivery, part of broader efforts to enhance sustainable logistics through mixed-integer programming and operational research techniques.

Simon BelieresView profile

Assistant Professor

• Operations Research
• Logistics Network Design
• Supply Chain Management
• +3 more

Simon Belieres is an Assistant Professor in the Department of Information, Operations and Management Sciences at Toulouse Business School. His research focuses on logistics network design, operations research, and optimization algorithms with applications in transportation and supply chain management. He holds a full-time academic position and is affiliated with TBS Education. Key research areas include Benders decomposition methods for stochastic programming, scheduling algorithms for automated systems, and mathematical heuristics for logistics network optimization. His work bridges theoretical optimization frameworks with practical applications in multi-product supply chains and sustainable delivery networks. Belieres' publications (2018-2025) demonstrate a strong emphasis on network design challenges, stochastic resource allocation, and algorithmic innovation. Notable contributions include partial Benders decomposition techniques, time-expanded network reduction, and metaheuristic approaches for logistics problems. His research also addresses real-world systems such as automated kitchens and urban spare part delivery networks. While no specific awards or grants are listed, his active publication record reflects continuous scholarly engagement in operations management and optimization fields.

• Operations Research
• Distributed Computing
• Linear Programming
• +9 more

Mohammad Ali Raayatpanah is an Associate Professor in the Department of Mathematical and Computer Sciences at Kharazmi University, Iran. He served as a Visiting Scientist at the Montpellier Advanced Knowledge Institute on Transitions, hosted by the Laboratory of Computer Science, Robotics and Microelectronics of Montpellier (LIRMM) from September 2024 to July 2025. Education: B.Sc. in Applied Mathematics from Kashan University, Iran (2004) M.Sc. in Applied Mathematics from Tehran University, Iran (2006) Ph.D. in Applied Mathematics from Tehran University, Iran (2013) Research Focus: Dr. Raayatpanah specializes in operations research and distributed computing, with expertise spanning linear/integer programming, network flows theory, communication networks, and network coding. His work extends to sustainable food supply chains, integrating food safety, environmental concerns, and uncertainty analysis through mathematical programming. His current research project addresses 'Mathematical Programming and Robust Optimization Techniques for Resilient and Sustainable Green Food Supply Chains'. Publication Trends: Analysis of his 2018-2025 publications reveals consistent emphasis on robust optimization for network design, particularly in wireless networks (Wireless Body Area Networks, vehicular networks) and healthcare systems. His work systematically addresses energy efficiency, resilience under attacks/uncertainty, and mathematical programming applications across supply chains and telecommunications, demonstrating methodological rigor in handling parameter uncertainty. Research Environment: During his fellowship, he collaborated at LIRMM, leveraging their infrastructure for network optimization research. His work bridges theoretical operations research with practical applications in critical infrastructure and sustainability challenges.

Eric KoskinenView profile

Associate Professor

• Software Verification
• Concurrency
• Programming Languages
• +5 more

Eric Koskinen is the Charles Berendsen Associate Professor (with tenure) of Computer Science at Stevens Institute of Technology in Hoboken, New Jersey. He is also a co-founder of the Commute Workshop at PLDI 2022 and has previously held positions as Researcher at Yale University and Visiting Professor at New York University. Education: Ph.D. in Computer Science, University of Cambridge (UK) Research Interests: Koskinen's research lies at the intersection of software verification , programming languages , and concurrency . He develops automated techniques to ensure reliable and efficient software, with a particular emphasis on commutativity analysis , parallelization , and language-level abstractions for multicore and distributed systems. His recent focus includes the Veracity programming language that introduces commute blocks to simplify concurrent programming. Scientific Awards & Honors: POPL 2023 Distinguished Paper Award Provost's Early Career Award for Research Excellence (2020) CAV 2011 Award Paper Advising & Funding: Prof. Koskinen currently advises three Ph.D. students—Adam Chen, Parisa Fathololumi, and Mihai Nicola—and has recently graduated Cyrus Liu (Ph.D. 2022). His research group has been generously supported by more than $5 million in competitive grants from the NSF, ONR, and DARPA. Recent awards include: NSF: Concurrent Objects ($593k, 2023) ONR: AVTA Transition ($215k, 2022) NSF: Dynamic Analysis ($399k, 2021) NSF: Commutativity Analysis ($495k, 2020) ONR: Temporal Alignment ($3.2M, 2017) Labs & Tools: His group maintains an active open-source tool suite including Veracity (interpreter for commute blocks), Servois2 (commutativity synthesizer), Dynamite (termination/non-termination checker), DarkSea (temporal verifier for binaries), CityProver (commutativity verifier), Knotical (trace-refinement synthesizer), and DrNLA (non-linear to linear integer arithmetic rewriter).

David RivreauView profile

Professor

• Combinatorial Optimization
• Scheduling Theory
• Operations Research
• +4 more

David Rivreau is a Professor and currently Dean of the Faculty of Sciences at the University of Angers (UCO). He previously served as General Delegate for Digital and Information Systems at UCO (2014–2019) and Director of the Institute of Applied Mathematics (2009–2014). He holds an HDR in Systems Control (2013) and a PhD in Systems Control from the University of Technology of Compiègne (1999). Research Interests : His work focuses on combinatorial optimization, scheduling theory, and exact methods for complex resource allocation problems. Key areas include surgical scheduling in healthcare systems, multi-skill project scheduling, and energy-constrained scheduling. He emphasizes practical applications of optimization in production logistics and healthcare operations. Publications : Over 30 peer-reviewed articles and conference papers since 2004, including work on robust surgical scheduling, multi-skill project scheduling, and energy-aware scheduling. Recent contributions address dynamic operating room scheduling with sterilization constraints and hybrid optimization approaches for resource allocation. Grants & Advising : Supervised 4 PhD students (e.g., Hasan Al Hasan on surgical scheduling, Clément Jamet-Glemin on gas network maintenance). Active in grant-funded research projects combining operations research with real-world applications. Labs & Teams : Associated with LARIS (Laboratory of Applied Mathematics and Informatics) and IDHAL (Digital Humanities and Artificial Intelligence Lab) at UCO.

René ThiemannView profile

Associate Professor

• Program Verification
• Interactive Theorem Proving
• Termination Analysis
• +7 more

René Thiemann is an Associate Professor in the Department of Computer Science at the University of Innsbruck, Austria, where he is a member of the Computational Logic group. His research is centered on formal methods, with a focus on program verification, termination analysis, and interactive theorem proving using Isabelle/HOL. He has led and contributed to several FWF-funded research projects, including Automation of Rewriting Infrastructure and Certifying Termination and Complexity Proofs of Programs . His research interests include: Program Verification via Interactive Theorem Proving (Isabelle, CeTA) Termination and Complexity Analysis of Programs and Rewriting Systems Term Rewriting and Dependency Pairs SAT and SMT Solving Formalization of Algebraic Structures (e.g., algebraic numbers, Smith normal form) Automated Reasoning and Certified Software The most recent articles highlight his continued work in formal verification, particularly in certifying termination, confluence, and complexity proofs. His publications show a strong trend toward mechanizing mathematical and logical foundations in Isabelle/HOL, with applications in rewriting, arithmetic, and program analysis. He frequently publishes in top venues such as CPP, ITP, LICS, and FSCD. He has served on program committees for conferences including CPP and RTA, and he regularly advises and collaborates on formalization projects. While no explicit grants or advising roles are listed, his leadership in multiple FWF projects indicates significant grant involvement. He teaches courses such as Program Verification, Functional Programming, Constraint Solving, and Interactive Theorem Proving in Isabelle/HOL, reflecting his expertise in formal methods and programming languages.

Pierre FouilhouxView profile

Professor

• Combinatorial Optimization
• Mathematical Programming
• Polyhedral Approaches
• +4 more

Pierre Fouilhoux is a Full Professor in Computer Science and Discrete Mathematics at Sorbonne Paris Nord University (formerly Paris 13 University), affiliated with the LIPN Laboratory and the Algorithmics and Combinatorial Optimization (AOC) Team. He previously held a position at Sorbonne Université's LIP6 Laboratory. His research focuses on Combinatorial Optimization, Mathematical Programming, Polyhedral Approaches, and Operational Research, with contributions to scheduling, network design, and robust optimization. Education: PhD in Computer Science (2004): Université Blaise Pascal, Clermont-Ferrand. Habilitation à Diriger des Recherches (HDR, 2018): Sorbonne Université. DEA in Computer Science and Operational Research, License and Master in Computer Science, and Mathematics License. Research Interests: His work addresses polyhedral structures, exact algorithms, and combinatorial optimization problems such as scheduling, unit commitment, and network reliability. He has developed Branch-and-Cut methods and symmetry-breaking techniques for integer programming. Publications: Over 30 peer-reviewed articles in journals like European Journal of Operational Research , Mathematical Programming , and Discrete Applied Mathematics . Recent work includes anchor-robust scheduling, unit commitment polytopes, and orbitopal fixing. Awards: 2006 Grand Prize for Young Researcher from Clermont-Ferrand for his contributions to science popularization and research communication. Teaching: Courses on Operations Research, Combinatorial Optimization, and Linear Programming at Sorbonne University and SUP Galilée. Supervised over 20 graduate and doctoral students, including topics like multi-objective optimization and virtual network embedding. Labs/Teams: Active in the AOC Team at LIPN, GDR-RO research group, and ISCO conferences. Collaborates with institutions like EDF R&D and Colt-Telecom on applied optimization problems.

Walid BEN AMEURView profile

Professor

• Operations Research
• Network Optimization
• Mathematical Programming
• +3 more

Prof. Walid Ben-Ameur is a Professor at Telecom SudParis, affiliated with the SAMOVAR laboratory. His research focuses on Operations Research, Network Optimization, and Algorithmic Game Theory. He has contributed significantly to areas such as combinatorial optimization, robust network design, and mathematical programming. His work includes advancements in polyhedral combinatorics, stochastic games, and distributed computing frameworks. Recent studies explore strategic investments in social networks, robust routing algorithms, and the theoretical foundations of optimization under uncertainty. While no scientific awards are explicitly mentioned, his prolific publication record highlights impactful contributions to the field. His research often bridges theoretical insights with practical applications in telecommunications and distributed systems. Key research interests include the design of efficient algorithms for network infrastructure, optimization under uncertainty, and game-theoretic models for distributed systems. His articles frequently address challenges in robust network design, opinion dynamics in social networks, and the computational aspects of combinatorial problems. Prof. Ben-Ameur collaborates extensively, with notable contributions to journals like Operations Research Letters, SIAM Journal on Optimization, and Networks. His work often intersects with telecommunications, as evidenced by studies on fiber cable network design and traffic engineering. He has also explored advanced topics such as multipolar robust optimization and the application of game theory to distributed computing environments.

Natalia KushikView profile

Lecturer

• Model-Based Testing
• Finite State Machines
• Software-Defined Networks (SDN)
• +7 more

Natalia Kushik is a Lecturer at Telecom SudParis, part of the Institut Polytechnique de Paris. Her primary affiliation is with the ACMES (Algorithmics and Computer Models for Engineering and Systems) research group. Her research focuses on model-based testing methodologies, formal verification of systems, and applications in software-defined networking (SDN), cloud computing, and distributed systems. Her work extensively employs finite state machines (FSMs) and automata theory to design test strategies for complex systems. Notable areas include deriving homing/synchronizing sequences for automata, race condition detection in distributed systems, and optimizing network configurations using timed models. She has contributed to improving the reliability and security of SDN controllers and cloud infrastructures through formal methods. Recent trends in her publications emphasize probabilistic approaches for test suite minimization, emulation-based validation of dynamic networks, and formal analysis of reactive systems. She has collaborated on projects involving QoE (Quality of Experience) evaluation for multimedia services and fault models for digital circuits. Her work often bridges theoretical computer science with practical system implementation challenges. Her articles frequently explore intersections between formal methods and real-world applications, such as applying cellular automata for network parameter modeling and using SMT solvers for configuration validation. She has also published on optimizing interpreted programming languages using state models and proactive trust assessment mechanisms for service systems. Kushik’s research is characterized by interdisciplinary collaborations, with contributions to both academic conferences (e.g., ICTSS, ENASE) and industry-oriented venues (e.g., IEEE NCA). Her work addresses challenges in system reliability, security, and scalability across diverse domains like telecommunications, cloud computing, and embedded systems.

Mahdi MoeiniView profile

Associate Professor

• Operations Research
• Combinatorial Optimization
• Mathematical Programming
• +5 more

Mahdi Moeini is an Associate Professor in Operations Research and Machine Learning at ENSIIE (École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise), affiliated with the SAMOVAR laboratory at Télécom SudParis, Institut Polytechnique de Paris. His research focuses on optimization techniques, including DC programming, combinatorial optimization, and their applications in logistics, healthcare, and finance. Academic Roles: Associate Professor (2022–present), Adjunct Lecturer (2014–2022) at TU Kaiserslautern, Germany. Research Expertise: Portfolio optimization, vehicle routing with drones, emergency medical systems, and metaheuristics. Key Contributions: Published 14 journal papers, 24 conference chapters, and 8 technical reports, with a Habilitation in 2018. Research Interests: Combinatorial optimization, mathematical programming, sustainability in digitization, and machine learning applications. Teaching: Courses include Operations Research, Data Science, Computational Intelligence, and Financial Data Analysis.

• Artificial Intelligence
• Constraint Programming
• Max-SAT
• +2 more

Sami Cherif is a Lecturer at the University of Picardie Jules Verne, specializing in Optimization and Cryptography, AI - OCIA . His research focuses on advanced applications of Constraint Programming and Max-SAT in solving complex industrial and cryptographic problems. PhD in Computer Science (2022, Aix-Marseille University) Key collaboration networks: Chu-Min Li, Sorina Ionica, Corinne Lucet Research Interests Dr. Cherif’s work bridges Artificial Intelligence with Cryptography , emphasizing: Max-SAT resolution techniques Energy-efficient manufacturing systems Conference scheduling optimization Cryptographic attack modeling Publication Trends His recent publications (2025–2024) highlight Max-SAT as a central method for: Self-stabilization analysis Assembly line energy optimization Cryptographic problem solving Conference scheduling Academic Collaborations Active collaborations in constraint programming include co-authors such as Chu-Min Li , Sorina Ionica , and Corinne Lucet , with applications spanning from industrial engineering to cryptanalysis.