جست‌وجوی استاد و پژوهشگر

• Operations Research
• Logistics Optimization
• Scheduling Algorithms
• +۵ مورد دیگر

Jeroen Belien is a Full Professor at the Faculty of Economics and Business (FEB) at KU Leuven. He leads the Operations Management Research Group and serves as Head of Subdivision 42 at the Brussels Campus. His roles include coordinating research activities and contributing to the KU Leuven Institute for Mobility (LIM). His research focuses on operations management, with emphasis on optimization models for logistics, waste collection, sports scheduling, and healthcare operations. He has pioneered educational tools like role-playing games for teaching linear programming and inventory management concepts. His work integrates simulation, mathematical programming, and real-world case studies. Key contributions include developing timetabling algorithms for international competitions, optimizing bio-waste collection in urban areas, and analyzing collaborative shipping strategies in the sharing economy. His publications span over 50 articles in journals like Operations Research for Health Care and INFORMS Transactions on Education. Belien actively engages in educational innovation through game-based learning and case competitions, emphasizing practical applications of operations research methods.

• Integer Programming
• Optimization Algorithms
• Computational Infrastructure
• +۲ مورد دیگر

Matthew J Saltzman is an Associate Professor in Mathematical and Statistical Sciences at Clemson University. His research develops optimization algorithms and computational infrastructure, particularly for integer programming and parallel computing applications. He contributes to COIN-OR open-source optimization projects and has published on simplex method implementations. Educational work includes computational mathematics curriculum development and textbook writing on complex analysis applications. Research addresses large-scale computational problems through parallel tree search algorithms and memory-aware optimization techniques. Teaching spans from introductory calculus to specialized graduate courses in optimization mathematics.

• Mathematical Optimization
• Conic Optimization
• Interior Point Methods
• +۳ مورد دیگر

Prof. Dr. Florian Jarre is affiliated with the Mathematical Institute at Heinrich-Heine-Universität Düsseldorf, specializing in mathematical optimization. His research spans conic optimization, interior point methods, semidefinite programming, and nonlinear optimization with applications in computational mathematics. Research interests focus on theoretical and applied aspects of optimization algorithms, including convergence analysis of iterative methods, complexity theory for convex problems, and development of efficient computational techniques for large-scale optimization challenges. Work extends to applications in machine learning and systems biology. Publications demonstrate consistent focus on optimization theory advancements, particularly in interior-point methods and convex programming. Recent work explores connections between optimization and machine learning, including SVM training methods and stochastic gradient descent variants.

• Artificial Intelligence
• Data Science
• Applied Mathematics
• +۳ مورد دیگر

Jialin Liu is an Assistant Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF) and a member of the UCF AI Initiative. His academic background includes a B.S. in Automation from Tsinghua University (2015) and a Ph.D. in Applied Mathematics from UCLA (2020). Education: B.S. in Automation, Tsinghua University (2015) Ph.D. in Applied Mathematics, University of California, Los Angeles (2020) Liu's research bridges AI, data science, and mathematics. He focuses on developing AI/data-driven methodologies to address computational mathematical problems such as optimization, differential equations, and numerical linear algebra. His work emphasizes the need for stable, safe, and explainable data-driven methods while establishing rigorous theoretical foundations for integrating AI into mathematics and science. The 15 most recent publications highlight interdisciplinary advancements in areas like matrix completion, graph neural networks for optimization, sparse coding for phase restoration, and theoretical frameworks for learning-to-optimize. These works span algorithm design, mathematical foundations of AI, and applications in computational mathematics.

Claudio Contardoمشاهده پروفایل

دانشیار

• Operations Research
• Logistics Management
• Vehicle Routing
• +۳ مورد دیگر

Claudio Contardo is an Associate Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University. His research focuses on Operations Research, Logistics Management, Vehicle Routing, Location Analysis, Integer and Combinatorial Optimization, and Artificial Intelligence. He holds an ORCID identifier (0000-0001-7595-3904) and maintains an active academic presence through publications and collaborations. His work emphasizes optimization algorithms for complex systems, including transportation networks, biomedical logistics, and automated systems. Recent contributions include iterative exact algorithms for biomedical sample transportation and counterfactual explanations for k-Nearest Neighbors models. His research bridges theoretical optimization with practical applications in urban mobility, healthcare logistics, and AI explainability. Contardo’s methodologies often involve decomposition techniques (e.g., Dantzig-Wolfe, column generation) and integration of mathematical programming with data-driven approaches. Despite prolific output, no explicit awards or grants are listed in the provided materials. His CV and full publication list are accessible via the provided link.

• Operations Research
• Mathematical Optimization
• Integer Programming
• +۷ مورد دیگر

Professor Kerem Akartunali is a faculty member at the Department of Management Science, Strathclyde Business School, University of Strathclyde, where he joined as the John Anderson Research Lecturer (JARL) in Optimization in 2010. He holds a PhD in optimization from the University of Wisconsin-Madison (2007) and served as a postdoctoral research fellow at the University of Melbourne. He is also a Visiting Professor at the Institute of Mathematics and Computer Science (ICMC-USP) of the University of São Paulo since 2017. His expertise spans operational research, mathematical optimization, integer programming, robust optimization, network optimization, and applications in production planning, transportation logistics, healthcare (e.g., radiation treatment planning), and energy (e.g., offshore windfarm installation). His research has been funded by EPSRC, SFC, and industry partners like Capita and the US Air Force. Key awards include the 2020 Best Reviewer Award and a Postdoctoral Research Fellowship (2007). He teaches courses such as Data Analytics in Practice and Optimization for Analytics and has contributed to strategic online learning initiatives. His professional roles include membership in EPSRC Peer Review College, the Isaac Newton Institute Management Committee, and the OR Society Research Panel. Award-winning research projects include production planning under uncertainty, multi-level robust optimization, and logistics for offshore windfarm installations. He collaborates with organizations like NHS, Scottish Power, and Technip on industry-research partnerships.

Bernd Dammannمشاهده پروفایل

دانشیار

• High-Performance Computing
• Scientific GPU Computing
• Parallel Programming
• +۳ مورد دیگر

Bernd Dammann is an Associate Professor in Scientific Computing at the Technical University of Denmark (DTU) , Department of Applied Mathematics and Computer Science. In parallel he serves as Scientific Lead & Architect in the DTU Computing Center (DCC) and co-founded DTU’s GPUlab in 2007, giving him dual roles in both academic research and institutional HPC services. Education Dipl.Phys. in Statistical Physics, University of Mainz, 1992 PhD in Physical Chemistry, DTU, 1996 Research interests Over almost three decades Dammann has focused on computational methods for large-scale scientific problems. His core expertise lies in: High-Performance Computing and parallel programming paradigms (OpenMP, MPI, GPU) Scientific GPU computing and many-core architectures Application tuning and performance optimization for heterogeneous systems Uncertainty quantification, Monte-Carlo methods, and real-time control applications in biomedicine (diabetes technology) Ultrasound beamforming and advanced imaging techniques Publication trends His 34 publications (1996-2025) trace a clear evolution from theoretical physics and surface-science simulations to GPU-accelerated engineering and biomedical systems. Recent work clusters around real-time biomedical control (artificial pancreas, virtual clinical trials) and high-resolution ultrasound imaging, all leveraging GPU acceleration and high-performance computing to achieve real-time performance. Scientific awards & recognition Contributor to 1 patent family and widely downloaded technical reports (>10 k downloads for OpenCL tutorial) Consistently high citation counts on key optimization and control papers (e.g., 93 citations for 2014 Management Science article) Teaching, supervision & grants He teaches or co-teaches multiple DTU courses: High-Performance Computing , Large-scale Computations , Python & HPC , Mathematical Software , and HPC with FORTRAN . While individual students are not named, his role in GPUlab and DCC implies ongoing PhD and MSc supervision, and his projects list six active collaborations including EU and national HPC infrastructure grants. Labs & teams Dammann co-founded GPUlab (2007) and presently leads the HPC architecture team in the DTU Computing Center , a central facility supplying cluster resources to the entire university.

Jon Leeمشاهده پروفایل

استاد

• Mathematical Optimization
• Combinatorial Optimization
• Integer Programming
• +۳ مورد دیگر

Jon Lee is the G. Lawton and Louise G. Johnson Professor of Engineering at the University of Michigan's College of Engineering. He previously held faculty positions at Yale University and the University of Kentucky, and served as an adjunct professor at New York University. Before his academic career, he was a Research Staff member at IBM T.J. Watson Research Center where he managed the mathematical programming group. Lee's research focuses on mathematical optimization, particularly combinatorial optimization, integer programming, and maximum-entropy sampling. His work bridges theoretical foundations with practical applications in experimental design, statistical modeling, and computational mathematics. He has made significant contributions to D-optimal design theory, perspective relaxations for nonconvex optimization, and generalized inverse computations. His recent publication trends (2022-2025) show consistent work in maximum-entropy sampling problems, D-optimal design algorithms, convex relaxations for nonconvex optimization, and generalized inverse computations. The articles demonstrate increasing sophistication in handling large-scale optimization problems while maintaining theoretical rigor, with particular emphasis on algorithmic efficiency for real-world applications. Lee has received notable recognition including: INFORMS Computing Society Prize (2010) Fellow of INFORMS (since 2013) As an academic leader, Lee has served as founding Managing Editor of Discrete Optimization (2004-06), currently serves as Co-Editor of Mathematical Programming, and is on editorial boards for Optimization and Engineering and Discrete Applied Mathematics. He chaired the Mathematical Optimization Society (2008-10) and the INFORMS Optimization Society (2010-12). His textbook A First Course in Combinatorial Optimization (Cambridge University Press) and open-source book A First Course in Linear Optimization have become standard references in the field. Lee maintains active research collaborations through his work with the Mathematical Optimization Society and INFORMS, and has participated in significant research programs including the Fall 2017 program on Bridging Continuous and Discrete Optimization at the Simons Institute.

Nicolas Barnierمشاهده پروفایل

پژوهشگر

• Constraint Programming
• Combinatorial Optimization
• Air Traffic Management
• +۳ مورد دیگر

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.

Issmaïl El Hallaouiمشاهده پروفایل

استاد

• Operations Research
• Optimization
• Mathematical Modeling
• +۵ مورد دیگر

Issmaïl El Hallaoui is a Full Professor in the Department of Mathematical and Industrial Engineering at Polytechnique Montréal. His research focuses on operations research, optimization, and mathematical modeling, with applications in transportation, vehicle routing, and software development. He is affiliated with the Institute for Data Valorization (IVADO) and the Research Group in Decision Analysis (GERAD). Operations Research and Management Science Combinatorial Optimization Online Optimization Vehicle Scheduling Transportation Logistics Software Development With over 116 publications, his work spans 2025 INFORMS Journal on Applied Analytics, 2024 Transportation Research Part E, and 2023 European Journal of Operational Research. His recent projects include supply chain optimization for OCP, real-time dial-a-ride problem solving, and energy system modeling via game theory. Dr. El Hallaoui has supervised 11 PhD and 3 Master's students, including Bani, A., Er Raqabi, E. M., and Tahir, A. His teaching portfolio includes courses on Integer Programming (MTH6404) and Real-time Optimization (MTH6601).

• Operations Research
• Optimization
• Healthcare Operations
• +۳ مورد دیگر

Farbod Farhadi is a researcher at Roger Williams University , affiliated with the School of Business . His work bridges operations research, optimization, and healthcare logistics, with a focus on solving complex scheduling problems in clinical and industrial settings. His research spans multiple domains: Healthcare operations (hemodialysis center scheduling) Lean manufacturing (U-shaped cells, human factors) Air traffic scheduling (branch-and-price algorithms) Metaheuristics for runway and facility location problems Recent publications emphasize exact optimization models, predictive analysis, and practitioner-oriented solutions for technician cost reduction.

• Energy Economics
• Energy Systems
• Renewable Energy
• +۷ مورد دیگر

Aaron Praktiknjo is a University Professor and Chair holder for Energy Systems Economics at RWTH Aachen University, leading the FCN-ESE group within the E.ON Energy Research Center. He contributes to energy economics research with a focus on system integration, market dynamics, and sustainability. Research Areas: Energy system analysis Energy market regulation Security of supply Sustainability assessment Behavioral energy economics Energy transition effects Selected Article Trends: Recent work explores hydrogen supply scenarios, grid infrastructure sustainability, and techno-economic modeling for energy systems. His publications emphasize renewable integration, market volatility, and cross-border energy dynamics. Leadership Roles: Co-Director of E.ON Energy Research Center Chair of Gesellschaft für Energiewissenschaft und Energiepolitik (GEE) Vice-President of International Association for Energy Economics (IAEE) Editorial Board member of Scientific Data (Nature Portfolio) Advising: Currently advising external doctoral candidates Elisabeth Wendlinger and Veronika Engwerth. Collaborates with researchers across FCN-ESE on topics like DC/AC grid comparisons and HVDC integration. Team & Collaborations: Works with technical staff including Christina Kockel, Jakob Kulawik, and Karl Seeger. His group engages in international conferences (e.g., IAEE, IET AC/DC) and interdisciplinary workshops (RWTH EME Doctoral Workshop).

• Model Predictive Control
• Water Resources Systems
• Intelligent Transportation Systems
• +۵ مورد دیگر

Pau Segovia Castillo is a Research Fellow in the Department of Automatic Control at Universitat Politècnica de Catalunya (UPC) since September 2023, holding the prestigious Beatriz Galindo Fellowship. His research focuses on large-scale systems management using non-centralized predictive control approaches, with applications in water resources, transportation, and energy systems. He holds a joint PhD in Automatic Control from UPC and IMT Lille Douai (France), completed in 2019. Previously, he held postdoctoral positions at IMT Lille Douai (2019–2020), TU Delft (2020–2023), and UPC (2023). His educational background includes a BS/MS in Industrial Engineering from UPC (2015). He is a member of the Advanced Control Systrems (SAC) group within the CS2AC research center. His work emphasizes model predictive control (MPC), observer design for nonlinear systems, and digital twin technologies applied to waterway infrastructure. Notable contributions include multilayer control strategies for canals and coordinated scheduling for inland vessel navigation. Key research areas span fault diagnosis in hydraulic systems, optimization algorithms for transportation logistics, and sustainable energy production from waterways. His publications address topics like MPC-based digital twins, observer schemes for noisy nonlinear systems, and lock scheduling using max-plus linear systems. He currently supervises Javier Pedrosa Alias's PhD on job-shop scheduling with AI approaches. Awards: Beatriz Galindo Fellowship Labs/Teams: Advanced Control Systrems (SAC) group, CS2AC Research Center

• Databases
• Artificial Intelligence
• Text Mining
• +۲ مورد دیگر

Ingo Feinerer serves as an Associate Professor at Vienna University of Technology's Faculty of Informatics, specifically within the Institute of Information Systems Engineering (E192-02). His research bridges theoretical computer science and practical applications in database systems, artificial intelligence, and text mining, with significant contributions to configuration management and formal methods. His educational background includes: PhD Dissertation (2007): A formal treatment of UML class diagrams as an efficient method for configuration management Diploma Thesis (2005): Formal program verification: a comparison of selected tools and their theoretical foundations Feinerer's research focuses on database theory (particularly schema mapping and dependencies), AI-driven configuration systems using integer linear programming, and text mining infrastructure development in R. His work combines theoretical rigor with practical tool development, notably through the textcat and tm packages. The integration of formal methods in software engineering remains a consistent thread throughout his publications. Analysis of his 15 most recent publications reveals strong specialization in database constraints (40%), text mining applications (30%), and software configuration systems (30%), with increasing interdisciplinary work connecting computer science to digital humanities. His notable recognition includes: INiTS Award (2007) for technology commercialization potential Feinerer has supervised doctoral research including F. I. Chertes' work on schema mapping languages and diploma theses on UML semantics and probabilistic databases. He led major research projects HINT (2012-2017) and SEE (2012-2016) focusing on database technologies and information systems. His work demonstrates consistent funding through Austrian national research programs. As a core member of the Databases and Artificial Intelligence research group, he collaborates extensively on R package development for text analysis and contributes to international workshops on theoretical aspects of software engineering.

Bernard Gendronمشاهده پروفایل

استاد

• Combinatorial Optimization
• Logistics
• Transportation Networks
• +۵ مورد دیگر

Bernard Gendron is a Full Professor at the Department of Computer Science and Operational Research (DIRO) at the University of Montreal and a researcher at the Interuniversity Research Centre on Enterprise Networks, Logistics, and Transportation (CIRRELT). His research focuses on combinatorial optimization, including polyhedral theory, decomposition methods, metaheuristics, constraint programming, and applications in logistics, transportation, telecommunications, and healthcare. He has held leadership roles, such as Director of CIRRELT (2008–2015) and President of Canadian Operational Research Society (CORS) and INFORMS Transportation Science & Logistics section. He has received awards including CORS Practice (2004), Service (2006), and Merit (2010) Awards. Education: Not explicitly stated in texts, but extensive academic and professional experience implies doctoral-level qualifications. Research Interests: Combinatorial optimization, logistics networks, transportation systems, integer programming, and algorithm design. His work bridges theoretical developments and practical applications, particularly in network design and large-scale optimization problems. Publications: Over 50 peer-reviewed articles, with recent contributions on facility location, network design, and heuristic algorithms. Key areas include subgradient methods, Lagrangian relaxation, and metaheuristic approaches. Editorial & Leadership: Editor-in-Chief of INFOR, member of editorial boards for journals like EURO Journal on Transportation and Logistics. Active in program committees (e.g., Odysseus 2018). Taught courses on optimization, network flows, and mathematical programming. Labs/Teams: Associated with CIRRELT and DIRO research groups focused on transportation and logistics optimization.