Find Academic Supervisors

• Cognitive Radio Networks
• Reinforcement Learning Applications
• Wireless Communications
• +7 more

Professor Kok-Lim Alvin Yau is an accomplished academic at Taylor's University in Malaysia, specializing in cognitive radio networks, reinforcement learning applications, and intelligent communications systems. With over 75 publications spanning from 2011 to 2025, he has established himself as a leading researcher in wireless networking and artificial intelligence applications in communications. His research interests encompass cognitive radio networks, reinforcement learning for networking applications, vehicular communications, edge computing, and the integration of AI in communication systems. Professor Yau's work demonstrates a consistent focus on applying machine learning techniques to solve complex networking challenges, particularly in dynamic spectrum access and resource allocation problems. Analysis of his recent publications (2023-2025) reveals a strategic shift toward more interdisciplinary research, combining reinforcement learning with generative AI techniques, exploring semantic communications, and addressing challenges in 6G-enabled vehicular networks. His work shows increasing emphasis on practical implementations and real-world applications of theoretical concepts, particularly in intelligent transportation systems and edge computing environments. Professor Yau has made significant contributions through numerous survey papers that have helped shape research directions in cognitive radio networks and reinforcement learning applications. His editorial work for special issues in IEEE Access and other journals demonstrates his standing in the academic community. He has successfully supervised multiple PhD students including Geong Sen Poh, Mee Hong Ling, and Aqeel Raza Syed, with whom he continues to collaborate on research projects. His international collaborations span institutions in China, Pakistan, and the UK, reflecting the global recognition of his research expertise.

Martin BehnkeView profile

Research Fellow

• Logistics
• Sustainable Transportation
• Operations Research
• +3 more

Dr. Martin Behnke serves as a Research Fellow at the Chair of Production and Logistics within the Faculty of Law, Economics and Business at Martin Luther University Halle-Wittenberg, where he has worked since March 2016 following his PhD completion in December 2020. His academic focus centers on sustainable transportation systems with emphasis on urban logistics. His educational foundation includes a Bachelor of Science (2010-2013) and Master of Science (2013-2016) in Business Mathematics, both earned at Martin Luther University Halle-Wittenberg. Behnke's research investigates emission-oriented transport planning through advanced vehicle routing methodologies, analyzing path selection impacts on greenhouse gas emissions and evaluating emerging technologies like electric vehicles in last-mile delivery. His work bridges operations research with environmental sustainability in freight transportation. Publication analysis reveals consistent focus on green logistics innovation, particularly urban freight optimization where environmental considerations are integrated into routing algorithms. Key themes include heterogeneous vehicle fleets, customer preference modeling for emission allocation, and multigraph-based routing solutions. No scientific awards or fellowships are documented in the available information. He actively supervises student theses and seminar papers while contributing to the DFG-funded project "Emission-oriented management of land-based freight transports," which develops emission-reduction frameworks for freight logistics. His teaching portfolio includes Mathematics I and II courses across multiple semesters. As core faculty within the Chair of Production and Logistics, Behnke participates in research initiatives and academic events including the Logistics Management 2019 conference he co-organized in Halle.

Saeedeh Ahmadi BasirView profile

Researcher

• Vehicle Routing Problem
• Transportation and Logistics
• Scheduling Problems
• +4 more

Dr. Saeedeh Ahmadi Basir is a Postdoctoral Researcher at the Technical University of Munich (TUM) , Germany, since 2024. Her research focuses on operations research and optimization techniques in logistics and scheduling. Education PhD in Industrial Engineering (2019–2024), Sabanci University, Istanbul, Turkey M.Sc. in Management and Productivity (2014–2017), Iran University of Science and Technology, Tehran, Iran BSc in Applied Mathematics (2006–2012), Isfahan University, Isfahan, Iran Research Interests Vehicle Routing Problem Transportation and Logistics Scheduling Problems Operations Research Logic-based Benders Decomposition Column Generation Metaheuristic Algorithms

• Genetic Programming
• Optimization Algorithms
• Scheduling Theory
• +3 more

Marko Durasevic is affiliated with the University of Zagreb's Faculty of Electrical Engineering and Computing. His primary research focuses on optimization algorithms, scheduling theory, and applications of genetic programming in solving complex combinatorial problems such as container relocation and dynamic machine scheduling. He collaborates extensively with researchers like Domagoj Jakobovic and Stjepan Picek, producing influential work at venues like GECCO, EuroGP, and EvoApplications. His research interests span automated heuristic design, cryptographic Boolean function optimization, and metaheuristic-based solutions for real-world logistics challenges. Durasevic has contributed to the development of ensemble methods for dispatching rules and energy-efficient heuristic systems, with a strong emphasis on practical applications in operations research and artificial intelligence. Notable contributions include the use of genetic programming for designing relocation policies in container terminals and evolving Boolean functions with cryptographic properties. His work frequently intersects with evolutionary computation frameworks, emphasizing both theoretical advancements and real-world problem-solving.

Veronika LeschView profile

Professor

• Self-aware Computing Systems
• Cyber-physical Systems
• Industry 4.0
• +3 more

Veronika Lesch is a Professor and Head of the Applied Computer Science program at DHBW Mosbach. Previously, she served as a post-doctoral research group leader and doctoral researcher at the University of Würzburg's Chair of Computer Science II (Software Engineering Group). Her research focuses on self-aware computing systems, cyber-physical systems, and intelligent transportation logistics. She has supervised numerous theses on topics like platooning coordination and warehouse optimization. Notable awards include being a nominee for the 'Ingenieurinnen Preis Bayern' and recipient of the 'Würzburg Software Engineering Award'. Education: She holds a Bachelor's (2015) and Master's (2017) in Computer Science from the University of Würzburg, followed by a PhD and postdoc at the same institution. Professional roles include teaching coordination of MINT courses and supervision of software projects. Research interests span IoT, Industry 4.0 logistics, and autonomous systems optimization. Over 20 peer-reviewed publications since 2017 address challenges in adaptive systems, vehicle routing, and meta-optimization strategies. Active in reviewing for top journals/conferences like IEEE Transactions on ITS and ICPE. Supervised 15+ student theses (2018-2021) Developed FADE framework for adaptive distance estimation Contributed to Combench communication protocol benchmarking Labs/Teams: Previously part of the Software Engineering Group at Würzburg, now leading applied CS initiatives at DHBW Mosbach.

Sebastian SchiffelsView profile

Professor

• Operations Research in Healthcare
• Behavioral Research in Healthcare
• Artificial Intelligence in Healthcare
• +4 more

Prof. Dr. Sebastian Schiffels is Professor of Digital Health & Medical Decision Making at the University of Augsburg's Faculty of Economics and Business. He leads the Chair of Digital Health & Medical Decision Making within the Institute of Business Administration, focusing on applying mathematical optimization, artificial intelligence, and behavioral science to healthcare challenges through close collaboration with the Augsburg University Hospital. His research encompasses three interconnected areas: Operations Research in Healthcare: Developing optimization techniques (branch-and-bound, column generation, heuristics) for hospital planning problems including staff scheduling and logistics management Behavioral Research in Healthcare: Conducting behavioral experiments to understand medical decision-making processes with emphasis on human-AI interaction AI in Healthcare: Implementing machine learning models (random forests, neural networks) for forecasting nursing needs and supporting shift planning decisions Prof. Schiffels currently leads the KISPP project (2023-2026), funded by the Bavarian State Ministry, which develops AI-based nursing staff planning systems to improve both care quality and nursing profession attractiveness. His work addresses critical healthcare challenges including nursing shortages and demographic pressures on care systems. He teaches courses including Health Care Operations Management and Cases in Business Analytics at both undergraduate and graduate levels. His research team includes scientific associates, assistants, and lecturers working across digital health applications. The chair participates in the Center for Interdisciplinary Health Research (ZIG) and talent networks including auxHOT and HeaDS-Net that connect academic research with practical healthcare operations.

• Combinatorial Optimization
• VLSI Design
• Chip Design
• +12 more

Stephan Held is a Professor at the Research Institute for Discrete Mathematics, University of Bonn, where he conducts research and teaches in combinatorial optimization, VLSI design, and discrete algorithms. He holds a Ph.D. from the University of Bonn and has been actively publishing in top venues such as DAC, DATE, IPCO, and IEEE Transactions. His research spans theoretical and applied aspects of optimization in chip design and logistics. He leads significant academic initiatives, including serving as General Chair for ISPD 2026 and as an Associate Editor for Discrete Optimization . Research Interests: VLSI Design: Clock scheduling, gate sizing, threshold voltage optimization, timing-driven placement and routing, repeater trees. Graph Coloring: Development of exactcolors , a high-performance open-source solver for maximum-weight stable set and graph coloring. Vehicle Routing: Work on time-dependent travel times, real-world benchmark creation using OpenStreetMap and Uber data, and winning the Amazon Last Mile Routing Challenge in 2021 with William Cook and Keld Helsgaun. His recent publications focus on approximation algorithms for Steiner tree variants, cost-distance optimization in routing, and combinatorial methods in chip design. These works frequently appear in journals like Mathematical Programming , Discrete Optimization , and IEEE Transactions on CAD , as well as conferences including DAC, ISPD, and IPCO. The research combines theoretical rigor with practical applications in electronic design automation and transportation logistics. Scientific Awards: Best Paper Award, DAC 2018 Best Paper Award, DATE 2009 Prof. Held advises students and has contributed to the academic community through teaching and organizing conferences. He has taught courses on chip design, combinatorial optimization, integer programming, and algorithmic mathematics. He has organized major events such as the International Symposium on Physical Design (ISPD), the Hausdorff Summer School, and the IPCO conference. His lab and research group at the University of Bonn are deeply integrated into the Bonn International Graduate School in Mathematics (BIGS), fostering collaboration in computational combinatorial optimization.

• Graph Neural Networks
• Combinatorial Optimization
• Federated Learning
• +3 more

Shiqing Liu is a researcher at the University of Bielefeld, affiliated with the Faculty of Engineering and the Cognitronics & Sensor Technology Group within the Center for Cognitive Interaction Technology (CITEC). Based at office CITEC 3-204, Liu contributes to the university's research in the Socio-Technical World domain, focusing on technologies that enable agents to act and communicate in complex environments. Dr. Liu's research spans several cutting-edge areas in artificial intelligence and optimization. Their work primarily focuses on graph neural networks, combinatorial optimization, and federated learning systems. They have made significant contributions to applying machine learning techniques to solve complex optimization problems including vehicle routing, facility location, and neural architecture search. Their research bridges theoretical computer science with practical applications in distributed systems and privacy-preserving technologies. An analysis of Dr. Liu's recent publications reveals a strong trajectory in developing unified frameworks that combine graph-based learning with combinatorial optimization. Their work increasingly addresses challenges in federated settings where data privacy and distribution heterogeneity present significant obstacles. The research demonstrates a progression from single-objective optimization problems toward more complex multi-objective scenarios, with growing emphasis on practical implementation constraints and real-world applicability. Dr. Liu is actively involved in the Cognitronics & Sensor Technology research group at CITEC, which is part of Bielefeld University's strategic focus on the Socio-Technical World. This center investigates how humans, robots, and AI systems can effectively interact and collaborate in complex environments, aligning with the university's broader mission of "Transcending Boundaries" between disciplines and between science and society.

Maximilian SchifferView profile

Associate Professor

• Operations Research
• Management Science
• Machine Learning
• +7 more

Prof. Dr. Maximilian Schiffer is an Associate Professor at the Professorship of Business Analytics & Intelligent Systems , TUM School of Management , Technical University of Munich. He holds a Ph.D. in Operations Research from RWTH Aachen University (2017) and has previously served as a visiting postdoctoral scholar at Stanford University and a postdoctoral scholar at RWTH Aachen. Education: Ph.D. in Operations Research, RWTH Aachen University (2017) Maximilian’s research focuses on developing operations research and prescriptive analytics methods to address societal mobility and transportation challenges, including electric vehicles , autonomous mobility-on-demand systems , and multi-modal transportation networks . His interdisciplinary work bridges robotics , computer science , and transport engineering . Recent publications highlight advancements in autonomous fleet control , electric vehicle charging infrastructure , and collusion detection in dynamic pricing . Scientific Awards: INFORMS TSL Dissertation Prize GOR Doctoral Dissertation Prize Best Paper Award, IEEE Conference on Intelligent Transportation Systems Maximilian is an associate member of the GERAD research group and serves on the editorial board of OR Spectrum and the TSL Dissertation Prize committee . His lab, the Professorship of Business Analytics & Intelligent Systems , trains students in machine learning, optimization, and future mobility systems.

Pia AmmannView profile

Researcher

• Mathematical Modeling
• Optimization
• Transportation and Logistics
• +2 more

Pia Ammann, M.Sc., is a research associate and doctoral candidate at the Chair of Operations Management at Technische Universität München (TUM) since 2019. She is associated with the RTG 'Advanced Optimization in a Networked Economy' (AdONE) and holds the GOR Master’s Thesis Prize 2020 and the Heureka Student Award 2019. B.Sc. and M.Sc. in Management and Technology at TUM (2012–2019) Study abroad at National Taiwan University (2016) Scholar of the Academic Training Program (Dieter Schwarz Stiftung) since 2021 Her research focuses on mathematical modeling and optimization in transportation and logistics, particularly addressing vehicle routing and scheduling challenges. She has explored synchronization constraints, automated truck platooning, and real-world multi-attribute problems using advanced heuristics like Adaptive Large Neighborhood Search. Her publications span journals like Transportation Research Part B: Methodological, arXiv working papers, and conference contributions at events such as Operations Research (2023), VeRoLog (2022), and the International Conference on Operations Research (2021). She is currently on parental leave until March 2025. GOR Master’s Thesis Prize (2020) Heureka Student Award (2019)

Banu Ulusoy-DereliView profile

Research Fellow

• Vehicle Routing
• Urban Logistics
• Transportation Systems
• +3 more

Banu Ulusoy-Dereli serves as a Research Fellow at the Technical University of Munich (TUM) School of Management within the Professorship of Business Analytics & Intelligent Systems led by Prof. Dr. Maximilian Schiffer. Her research develops optimization algorithms for urban transportation systems with direct industry applications. Education: Bachelor of Science in Industrial Engineering, Bilkent University Master's in Industrial Engineering, Koç University Research Focus: Specializing in Vehicle Routing and Urban Logistics , Banu employs Mixed Integer Programming and Metaheuristics to solve complex transportation challenges. Her work bridges theoretical optimization with real-world city logistics implementation, emphasizing scalable algorithmic solutions for large-scale problems. Publication Trends: Recent work centers on two-echelon location routing systems, integrating mobile depots and time synchronization constraints. Her publications demonstrate evolving expertise in adaptive large neighborhood search techniques for city logistics, reflecting strong alignment between theoretical methodology development and practical transportation applications. Scientific Awards: No awards documented in available sources. Advising & Grants: Current information indicates no formal student advising roles or externally funded research grants. Research Environment: Actively contributes to TUM's Professorship of Business Analytics & Intelligent Systems team, collaborating on intelligent transportation systems research within Munich's academic ecosystem while maintaining industry connections from prior professional experience.

• Artificial Intelligence
• Planning and Scheduling
• Reinforcement Learning
• +5 more

Sheila A. McIlraith is a Professor in the Department of Computer Science at the University of Toronto, where she leads research at the intersection of artificial intelligence, knowledge representation, and formal methods. With an extensive publication record spanning over three decades, she has made significant contributions to planning, reinforcement learning, and epistemic reasoning in AI systems. Her research interests focus on developing formal frameworks for AI planning and decision-making, with particular emphasis on interpretable AI, reward specification in reinforcement learning, and multi-agent systems. She has pioneered work in reward machines for reinforcement learning, epistemic planning, and the application of formal methods to ensure safety and fairness in AI systems. Her recent work bridges symbolic AI with deep learning approaches to create more transparent and controllable intelligent agents. Analysis of her recent publications reveals a strong trend toward integrating formal specification languages with machine learning, particularly in reinforcement learning where she develops methods for specifying complex tasks using linear temporal logic and related formalisms. Her work increasingly addresses ethical considerations in AI, including fairness in sequential decision making and the impact of ethics education in computer science curricula. McIlraith has mentored numerous PhD students who have become prominent researchers in AI, including Rodrigo Toro Icarte, Toryn Q. Klassen, and Andrew C. Li. Her collaborative network spans major AI research institutions worldwide, with frequent collaborations with researchers at institutions including the Vector Institute and international universities. Her research group explores the theoretical foundations of AI planning while developing practical applications in areas including robotic assistance, ethics-aware AI, and interpretable decision systems. Current projects focus on language-guided reinforcement learning, multi-agent verification, and the development of tools for responsible AI development and deployment.

• Machine Learning
• Deep Learning
• Neural Networks
• +7 more

Sung-Bae Cho is a prolific researcher in computer science with a publication record spanning over three decades from 1990 to 2025, demonstrating sustained academic productivity and research leadership. Their work primarily focuses on machine learning, neural networks, and their applications across diverse domains including cybersecurity, healthcare, and intelligent systems. Cho's research interests center around advanced machine learning techniques including deep learning architectures, Bayesian networks, and context-aware systems. Their work shows a consistent evolution from traditional neural networks to modern deep learning approaches, with recent publications emphasizing graph neural networks, federated learning, and anomaly detection systems. The research demonstrates strong methodological rigor with applications in practical domains such as traffic prediction, medical diagnosis, and security systems. The publication pattern reveals significant research productivity with multiple high-impact papers annually in reputable venues including IEEE Access, Neurocomputing, and Expert Systems with Applications. Recent work shows particular emphasis on addressing contemporary challenges in machine learning including continual learning, few-shot learning, and privacy-preserving approaches in federated settings. The research demonstrates both theoretical contributions and practical implementations across various application domains. Cho has cultivated an extensive collaborative network with numerous co-authors including Kyung-Joong Kim, Satchidananda Dehuri, Jin-Hyuk Hong, and Seok-Jun Bu, suggesting leadership in research groups and projects. The collaborative pattern indicates supervision of junior researchers and students, though specific advisee relationships aren't explicitly documented in the publication metadata. The researcher maintains active contributions to laboratory and team-based research, with recent publications indicating involvement in projects related to healthcare AI, cybersecurity systems, and intelligent transportation. Current research directions appear focused on addressing limitations in deep learning including catastrophic forgetting, data scarcity, and privacy concerns through novel architectural and training approaches.

• Software Defined Networking
• Machine Learning Applications
• Network Security
• +4 more

Mutaz Hamed Hussien Khairi is an active academic researcher at Universiti Teknologi Malaysia's College of Engineering, Department of Electrical Engineering. His research focuses on the intersection of networking technologies and machine learning applications, with particular expertise in Software Defined Networking (SDN), network security, and wireless communications. He has maintained consistent publication output in reputable journals between 2021-2023. His research interests center on applying machine learning techniques to solve critical networking challenges. Khairi's work spans Software Defined Networking optimization, intrusion detection systems for wireless networks, traffic forecasting, and specialized applications like vehicle classification using radar technology and forest fire detection systems. His research demonstrates a strong practical orientation with clear applications in network security and performance optimization. Analysis of his publication record reveals a clear trajectory focusing on machine learning applications in networking infrastructure. His work shows increasing sophistication in applying neural networks and other ML techniques to solve specific problems in SDN, wireless security, and environmental monitoring systems. The research consistently addresses real-world networking challenges with practical implementations. Khairi collaborates extensively with researchers at Universiti Teknologi Malaysia, particularly with faculty members Sharifah Hafizah Syed Ariffin, Mohamed Khalafalla Hassan, and Mosab Hamdan. His collaborative network extends across multiple research projects focusing on different aspects of network security and performance optimization, indicating active participation in research teams addressing contemporary networking challenges.

Christian FikarView profile

Professor

• Food Supply Chain Management
• Operations Research
• Operations Management
• +6 more

Christian Fikar is a Professor of Food Supply Chain Management at the Faculty of Life Sciences: Food, Nutrition and Health at the University of Bayreuth, based at the Kulmbach campus. His research focuses on business management issues in food value chains, particularly time-critical logistics processes and computer-based decision support systems. Professor Fikar studied Supply Chain Management at the Vienna University of Economics and Business, completed his doctorate and habilitation at the University of Natural Resources and Life Sciences in Vienna, and has held academic positions at both institutions before joining the University of Bayreuth. He has extensive international experience from research periods in the USA, Taiwan, Spain, and Finland. His methodological expertise spans Operations Research, Operations Management, and Business Analytics , with applications focused on increasing the resilience, sustainability, and efficiency of food value chains. Professor Fikar's research particularly examines logistics decisions in the food mail order industry and short food supply chains (regional supply networks) in Upper Franconia. His work enables more efficient processes that can contribute to reducing food waste while maintaining high-quality food delivery systems. Key research areas include perishable food distribution, digital logistics platforms, and crowd logistics for local food systems. Analysis of his recent publications reveals a strong focus on computational approaches to food supply chain challenges, integrating simulation and optimization techniques to address problems in perishable food distribution, crowd logistics, and regional food systems. His work shows increasing attention to digital transformation in food supply chains, quality preservation in logistics, and sustainable delivery models. Professor Fikar has advised doctoral students including Florian Cramer, whose 2025 dissertation focused on retail access models. His research has been supported by various projects examining sustainable food distribution systems, e-grocery operations, and resilience in short food supply chains. He actively contributes to the sustainable development of regional food networks in Upper Franconia through both research and teaching initiatives at the University of Bayreuth.