Find Academic Supervisors

• Fuzzy Logic
• Swarm Intelligence
• Computational Finance
• +7 more

Dr. Hubert Zarzycki is a researcher at the Department of Computer Science and Systems Engineering within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. His work focuses on computational intelligence, fuzzy systems, and algorithm design for optimization problems. Research Interests: Dr. Zarzycki specializes in Ordered Fuzzy Numbers and their arithmetic operations Swarm Intelligence algorithms (e.g., bacterial foraging, cuckoo search, firefly) Applications in financial modeling, sensor placement, and routing optimization Integration of blockchain technology for supply chain transparency Publication Trends: His research spans multiple domains including fuzzy logic, computational finance, and industrial IoT. Recent work emphasizes directional representation in fuzzy systems, robust risk management frameworks, and modular control systems for time-sensitive applications. Contact: Available at hubert.zarzycki@pwr.edu.pl

Czesław SmutnickiView profile

Professor

• Manufacturing Systems
• Operations Research
• Optimization
• +3 more

Prof. Czesław Smutnicki is the Dean and Head of the Department of Computer Engineering at the Faculty of Information and Communication Technology, Wrocław University of Science and Technology. His research focuses on manufacturing systems, operations research, and optimization, with a particular emphasis on scheduling algorithms and metaheuristics. Current Position: Dean, Department of Computer Engineering Institution: Wrocław University of Science and Technology Research Areas: Cyclic scheduling, Lagrangian relaxation, quantum annealing, and multi-criteria optimization Recent publications show a trajectory from classical scheduling (1996-2018) to modern applications involving quantum computing and distributed systems. The articles cluster around three main themes: (1) quantum-enhanced scheduling optimization, (2) Lagrangian relaxation techniques for tardiness minimization, and (3) robust scheduling under uncertainty in multi-vehicle/worker environments. Contact: czeslaw.smutnicki@pwr.edu.pl

• Computational Linguistics
• Natural Language Processing
• Text Technology
• +3 more

Yu Wang is a Lecturer at Bielefeld University's Faculty of Linguistics and Literary Studies, affiliated with the Computational Linguistics department. He is associated with the research project Monitoring the understanding of explanations under Prof. Dr. Hendrik Buschmeier, focusing on digital linguistics and text technology. While his institutional affiliation highlights computational linguistics, his Google Scholar publications reveal interdisciplinary work in optimization algorithms, graph neural networks, and machine learning applications. Research Interests: Yu Wang's work bridges computational linguistics with advanced algorithmic techniques. His publications explore Multi-objective optimization Graph neural network architectures Swarm intelligence applications Evolutionary computation methods Sparse learning systems Biologically-inspired algorithms Labs & Projects: Yu Wang contributes to the Digital Linguistics Group at Bielefeld University, specifically working on explainability monitoring in explanatory processes. His technical publications suggest collaborations with researchers in optimization and machine learning domains.

• Learning to optimize
• Optimization under uncertainty
• Efficient logistics and transport
• +4 more

Prof. Dr. Kevin Tierney is a Full Professor for Decision and Operation Technologies at Bielefeld University's Faculty of Business Administration and Economics. He also serves at the Department of Management Science & Business Analytics and is affiliated with the Bielefeld Center for Data Science (BiCDaS) and Center for Uncertainty Studies (CeUS). Chair of Business Administration, Decision and Operation Technologies Member of BIGSEM Graduate School PhD (2013) - IT University of Copenhagen Sc.M. (2010) & BS (2008) - Brown & RIT Research Interests His work focuses on: Learning to Optimize: Using deep reinforcement learning to automate solution heuristics for complex problems like routing and scheduling. Optimization under Uncertainty: Developing models that incorporate probabilistic elements for decision-making in unpredictable environments. Efficient Maritime Logistics: Specializing in container shipping, terminal operations, and fleet routing with real-world constraints. Recent publications demonstrate expertise in algorithm configuration, constraint programming, and machine learning applications to logistics challenges. Scientific Recognition Distinguished Paper Award - European Conference on Artificial Intelligence (2020) Projects & Grants Principal Investigator in projects: Self-learning methods with Deep Reinforcement Learning (DFG 2026) itsowl-MOVE (Land NRW 2024) AIPlan4EU Meta-planning engine (EU H2020 2023) Academic Leadership Module responsible for: Quantitative Business Administration Data Science Production and Operations Management

Robbert ReijnenView profile

Researcher

• Multi-Objective Optimization
• Deep Reinforcement Learning
• Graph Neural Networks
• +4 more

Robbert Reijnen is a Researcher at the Department of Information Systems within the Industrial Engineering and Innovation Sciences school at Eindhoven University of Technology . His work focuses on algorithmic approaches for complex optimization problems in industrial contexts. Research interests include: Deep Reinforcement Learning for dynamic systems Graph Neural Networks in scheduling optimization Adaptive parameter control in evolutionary algorithms Multi-objective optimization frameworks Recent publications highlight applications in job shop scheduling, vehicle routing, and differential evolution techniques. He has received recognition through scientific awards : Winner of MLVRP2023 GECCO competition (2023) Active in organizing academic workshops like EURO Meets NeurIPS 2022 Vehicle Routing Competition , and teaching Algorithmic programming for operations management . His work contributes to UN Sustainable Development Goals related to efficient resource management.

David REYView profile

Professor

• Optimization
• Integer Programming
• Bilevel Optimization
• +7 more

David Rey is a Professor at SKEMA Business School in Sophia Antipolis, France. His research focuses on optimization, game theory, and artificial intelligence applied to transportation and logistics. He holds a PhD in Operations Research from Université Grenoble Alpes (2012) and a Habilitation à Diriger des Recherches (2023) from Université de Toulouse. Previously, he was a Senior Lecturer at UNSW Sydney (2016–2021) and has held postdoctoral positions at rCITI UNSW. He leads the URBANE project, exploring green last-mile delivery solutions, and serves on editorial boards like Transportation Letters . Awards include multiple ARC grants and the INFORMS TSL Best Paper Award Committee role (2025). Education: Habilitation à Diriger des Recherches (2023), Operations Research, Université de Toulouse PhD (2012), Operations Research, Université Grenoble Alpes Master (2008), Sciences/Mathematics, Pontifical Catholic University of Rio de Janeiro Engineering Degrees (2004–2005), Electrical Engineering, University of Montpellier Research Interests: David’s work emphasizes optimization techniques (bilevel, stochastic, mixed-integer programming), game theory, and AI applications in transport systems. Key areas include drone delivery, autonomous vehicle integration, congestion pricing, and resilient logistics networks. His methods address real-world challenges like pandemic response, electric vehicle charging infrastructure, and fair resource allocation. Grant Highlights: URBANE (EU Horizon-funded), 2022–present 4 ARC grants as Lead Chief Investigator, including projects on concrete mixes, ethics in transport systems, and workforce logistics optimization Advising & Grants: Directed/co-directed 15+ PhD theses across SKEMA and UNSW Sydney Active in grant writing and editorial roles, including Transportation Science and Logistics Society Workshop (2024–2025) Labs/Teams: Leads the SKEMA Centre for Analytics and Management Science, focusing on analytics-driven solutions for business and urban systems.

• Transportation and Logistics
• Vehicle Sharing Systems
• Vehicle Routing Problems
• +4 more

Ataç Selin is a Researcher at the University of Applied Sciences and Arts Western Switzerland (HES-SO), affiliated with the Interdisciplinary Institute for Business Development (IIDE) at HEIG-VD. She holds a PhD in Transportation Science from EPFL (2023) and focuses on optimizing vehicle sharing systems, logistics, and sustainable urban mobility. Her work integrates simulation-optimization frameworks to address challenges in bike/car sharing systems, electric vehicle infrastructure, and craft beer distribution logistics. Education: PhD in Transportation Science (2023), École Polytechnique Fédérale de Lausanne (EPFL) MSc in Operations Research (2016), Middle East Technical University (METU) BSc in Industrial Engineering (2013), Middle East Technical University (METU) Research Interests: Her research spans vehicle routing optimization, demand forecasting in transportation networks, and decision-making frameworks for sustainable systems. She has pioneered a holistic management framework for vehicle sharing systems, emphasizing rebalancing strategies, clustering algorithms, and environmental impact mitigation. Key Projects: "Craft Beer Distribution Optimization" (2024), funded by Innosuisse, focuses on collaborative logistics platforms for Swiss craft breweries. "Light Electric Vehicle Sharing Systems" (2024), developing decision support tools for EV infrastructure. Awards: Recipient of the EPFL EDCE Mobility Award (2021) for contributions to VSS optimization and the METU Best Graduate Performance Award (2016).

• Multi-objective Decision Making
• Combinatorial Optimization
• Scheduling
• +3 more

Prof. Dr. Özgür ÖZPEYNİRCİ is a faculty member at İzmir Ekonomi Üniversitesi's İşletme Fakültesi, leading the Lojistik Yönetimi department since 2015. He holds a PhD in Industrial Engineering from Orta Doğu Teknik Üniversitesi (2008). His research focuses on multi-objective decision making, combinatorial optimization, and logistics system design. He has authored influential papers in journals like Management Science and European Journal of Operational Research , with notable contributions to pharmacy duty scheduling and shipment consolidation problems. Prof. ÖZPEYNİRCİ has received the MCDM Doctoral Dissertation Award (2011) and serves on editorial boards such as the İzmir Review of Social Sciences. He has advised over 10 PhD and master's students, and his current projects include multi-modal logistics optimization and inverse multi-criteria sorting algorithms. His academic career includes roles as Department Chair and Deputy Dean, alongside leadership in national research projects funded by TÜBİTAK. Teaching responsibilities span logistics planning, optimization, and supply chain management, with courses like LOG 301 Lojistik Planlama ve Modelleme I . His work bridges theoretical advancements with practical applications in healthcare, emergency response, and international logistics.

• Artificial Intelligence
• Scheduling Algorithms
• Metaheuristics
• +4 more

Prof. Nysret Musliu is an Associate Professor at TU Wien's Department of Databases and Artificial Intelligence within the Faculty of Informatics. His primary affiliation is with the E192-02 research area, focusing on optimization, scheduling, and AI applications. He leads projects like 'Artificial Intelligence in Employee Scheduling' and 'Predictive Analytics for Emergency Call Infrastructure,' demonstrating expertise in combinatorial optimization and real-world problem-solving. Academic Rank: Associate Professor Institution: TU Wien Key Projects: AI-driven scheduling, constraint programming, metaheuristics Research interests include scheduling algorithms, constraint programming, and hybrid optimization methods. His work bridges theoretical advancements with industrial applications, addressing challenges in manufacturing, healthcare, and transportation. Recent publications emphasize hyper-heuristics, large neighborhood search, and AI integration for complex scheduling problems. Notable contributions include systematizing test laboratory scheduling and developing exact methods for oven scheduling. His research group collaborates on projects involving personnel scheduling, production leveling, and parallel machine optimization.

Damianos GavalasView profile

Professor

• Mobile Computing
• Pervasive Computing
• Wireless Networks
• +6 more

Prof. Damianos Gavalas is a Professor and Vice Head of the Department of Product and Systems Design Engineering at the University of the Aegean, Greece. He also serves as an Adjunct Professor at the Hellenic Open University. His research focuses on mobile/pervasive computing, extended reality, wireless networks, optimization algorithms, and transportation systems. He has authored over 200 publications, with an h-index of 37, and has led major research projects such as SMARTBUY (H2020) and iDeliver (Erasmus+). Education: PhD in Electronics Engineering (University of Essex, 2001), MSc in Telecommunications (University of Essex, 1997), BSc in Informatics (University of Athens, 1995). Research Interests: Mobile & Pervasive Computing Wireless Sensor Networks Immersive Technologies (AR/VR) Intelligent Transportation Systems Optimization Algorithms Awards: Recipient of Best Paper Awards, ranked top 2% in Networking & Telecommunications (Stanford, 2022). Advising & Grants: Supervised 3 completed PhDs and numerous MSc projects. Coordinated EU-funded projects including HoPE (EC/CIP7), MOVESMART (FP7), and eCOMPASS (FP7). Labs/Teams: Involved in interdisciplinary teams focusing on smart cities, cultural heritage technologies, and automotive systems.

• Operations Research
• Logistics and Supply Chain Management
• Optimization Algorithms
• +3 more

Seokcheon Lee is a Professor of Industrial Engineering at Purdue University's Edwardson School of Industrial Engineering. He is affiliated with the Global Engineering Program, contributing to interdisciplinary research and education. His work focuses on optimization, logistics, and emergency response systems, leveraging advanced methodologies such as reinforcement learning, clustering analysis, and simulation modeling. Key areas include drone-assisted delivery systems, supply chain resilience, and social welfare-based decision-making frameworks. His research bridges theoretical models with practical applications in transportation, healthcare, and sustainable energy systems. Research interests center on the intersection of operations research and real-world challenges, including multi-agent systems, vehicle routing, dynamic scheduling, and resource allocation. He explores solutions for emergency medical services (EMS) logistics, food accessibility, and renewable energy integration. Notable contributions include frameworks for flying warehouses, citizen responder networks, and energy-efficient routing protocols. His publications span over two decades, addressing topics like UAV task allocation, microgrid scheduling, and supply chain preparedness. While no specific awards are listed, his work has been supported by institutional recognition. Advising and grant details remain unspecified, though his extensive publication record reflects active research collaboration. He contributes to lab initiatives in drone logistics, healthcare systems optimization, and smart city infrastructure design.

Jonathan BardView profile

Professor

• Operations Research
• Industrial Engineering
• Logistics Optimization
• +6 more

Jonathan Bard is a Professor of Operations Research & Industrial Engineering at the University of Texas at Austin, holding the Industrial Properties Corporation Endowed Faculty Fellowship in Engineering. He serves as Associate Director of the Center for the Management of Operations Logistics and Assistant Graduate Advisor for the Manufacturing Systems Engineering Program. His academic journey includes a D.Sc. in Operations Research from George Washington University, an M.S. in Aeronautics & Astronautics from Stanford University, and a B.S. in Aeronautical Engineering from Rensselaer Polytechnic Institute. Dr. Bard's research focuses on algorithm development for airline operations, vehicle routing, machine scheduling, and large-scale optimization. He is internationally recognized for expertise in bilevel programming and postal operations. His work integrates decomposition techniques for hierarchical planning and multicriteria decision-making in socio-economic systems. Bard advises government agencies and corporations, and contributes to editorial boards of journals like International Journal of Production Research and IEEE Transactions on Engineering Management . His recent articles address challenges in freight rail scheduling, kidney exchange systems, waste management optimization, and healthcare logistics. Awards include Fellowships from INFORMS and IIE, and Senior Membership in IEEE. Bard’s consulting spans industries, emphasizing real-world applications of operations research principles.

• Machine Learning
• Optimization
• Control Systems
• +3 more

Bo Li is an Assistant Professor in the Department of Statistics at the University of California, Berkeley . His research bridges statistical theory with applications in machine learning, optimization, and autonomous systems. Education: PhD in Statistics (2006), advised by Peter Bickel Email: boliboli@gmail.com Bo Li's research focuses on machine learning , control systems , and energy-efficient optimization . His work addresses challenges in autonomous vehicle navigation, solar panel positioning, and stochastic energy routing problems. Key themes include predictive control frameworks, multi-agent coordination, and uncertainty handling in dynamic systems. Recent publications highlight 2025 and 2024 advancements in model predictive control (MPC) , generalized Nash equilibrium for autonomous racing, and stochastic optimization for energy systems. His work spans vehicle routing , solar energy tracking , and multi-agent control , emphasizing data-driven approaches and real-world validation.

• Optical Networking
• Wireless Communications
• Vehicular Networks
• +7 more

Dr. Wissam Fawaz is a professor-level academic with extensive contributions to optical networking, vehicular communication systems, and UAV-aided network solutions. His work spans over two decades, focusing on Quality of Service optimization , Free Space Optical (FSO) communications , and Mobile Edge Computing (MEC) . Key collaborations with Chadi Abou-Rjeily, Maurice Khabbaz, and Ken Chen Published in IEEE Transactions on Wireless Communications , IEEE Communications Magazine , and Computer Networks Research interests center on network reliability , resource allocation , and next-generation communication architectures . His work explores: UAV-based network repair mechanisms QoS differentiation in optical and vehicular networks Machine learning integration for MEC task offloading Buffer-aided cooperative FSO systems Recent publications (2022) demonstrate innovations in: Acoustic synchronization protocols D2D-enabled Het-MEC systems Lyapunov-optimized resource allocation

• Operations Research
• Vehicle Routing Problem
• Machine Learning Applications
• +7 more

Giusy Macrina is a Researcher at the Department of Mechanical, Energy and Management Engineering (University of Calabria, Italy). Her work focuses on Operations Research and Machine Learning applications to complex logistics and transportation problems. Specializes in Vehicle Routing Problems with drones and crowd-shipping Develops hybrid algorithms combining mathematical optimization and artificial intelligence Active in green logistics and sustainable transport systems Collaborates with international institutions like Amazon Her research spans smart mobility , energy-efficient delivery systems , and IoT localization challenges . Recent publications demonstrate her focus on integrating machine learning into traditional optimization problems . She teaches courses in Management Engineering , including Methods and Tools for Engineering and Production Management and Control at the graduate level. Her work addresses both static and dynamic optimization challenges in logistics and energy systems.