Find Academic Supervisors

Saravanan KuppusamyView profile

Associate Professor

• Supply Chain Management
• Sustainable Operations
• Inventory Management
• +2 more

Dr. Saravanan Kuppusamy (SK) is an Associate Professor at Rowan University's William G. Rohrer College of Business. He holds a Ph.D. in Operations Management from the University of Cincinnati, along with an M.S. in Industrial Engineering from West Virginia University and a B.S. in Mechanical Engineering from Coimbatore Institute of Technology. His research focuses on sustainable operations, supply chain management, inventory control, transportation optimization, additive manufacturing, and food supply chains. Prior to academia, he accumulated extensive industry experience at Schneider Logistics, PepsiCo, Kimberly-Clark, and Case New Holland across logistics, manufacturing, and consumer goods sectors. Dr. Kuppusamy teaches courses in global supply chain management, supply chain analytics, and business logistics. His publications appear in leading journals including European Journal of Operational Research and Transportation Research Part E.

Melih OzlenView profile

Associate Professor

• Operations Research
• Combinatorial Optimisation
• Mathematical Programming
• +1 more

Associate Professor Melih Ozlen serves as Deputy Head of Department (Academic Operations) in the Department of Mathematical and Geospatial Science within RMIT University's School of Science at the City Campus in Australia. With a strong academic background in operations research and optimization, he has established himself as a leading researcher in combinatorial optimization, mathematical programming, and multi-objective optimization. His research interests focus on practical applications of optimization techniques across various domains including healthcare logistics, wildfire management, network optimization, and power systems. Ozlen's work bridges theoretical optimization methods with real-world challenges, particularly in resource allocation problems under uncertainty. The publication record reveals a consistent research trajectory with emphasis on decomposition methods, evolutionary algorithms, and matheuristic approaches for solving complex optimization problems. His recent work shows increasing focus on healthcare applications while maintaining strong contributions to wildfire management and network optimization problems. As an active reviewer for prestigious journals including European Journal of Operational Research and Computers and Operations Research, as well as the Australian Research Council, Ozlen contributes significantly to the academic community. His professional memberships span key organizations including INFORMS, Australian Mathematical Society, and Australian Society for Operations Research. Ozlen actively supervises graduate students with current projects focusing on home care optimization, multi-objective mixed integer programming, and vehicle routing problems with applications to wildfire management. His teaching interests align with his research expertise in operations research, combinatorial optimization, mathematical programming, and multi-objective optimization.

Stefan MinnerView profile

Professor

• Logistics
• Supply Chain Management
• Inventory Management
• +9 more

Prof. Stefan Minner is a Full Professor of Logistics and Supply Chain Management at the TUM School of Management, Technical University of Munich since 2012. His academic career includes professorships at the University of Vienna (2008–2012) and University of Mannheim (2004–2008), with prior roles at the University of Calgary and University of Magdeburg. He holds a Ph.D. (1999) and Habilitation (2003) from Magdeburg, focusing on supply chain optimization and inventory management. Research focuses on data-driven logistics network design, inventory management under uncertainty, and urban logistics. Key areas include blockchain in supply chains, commodity procurement strategies, and automation in retail operations. He serves as Editor-in-Chief for the International Journal of Production Economics and holds leadership roles in logistics societies like the German Logistics Association (BVL) and International Society for Inventory Research (ISIR). His honors include Fellow of ISIR (2016), Wirtschaftswoche Top Researcher (2020), and the M&SOM Practice-based Research Competition (2019). Research output spans 25+ years, with recent work addressing AI in logistics, sustainable supply chains, and pandemic-era ambulance dispatching systems. Teaching includes courses on inventory management, transportation analytics, and sustainable supply chains. His lab's work integrates optimization, simulation, and empirical methods to solve real-world logistics challenges in automotive, retail, and manufacturing sectors.

• 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.

• Networking
• Edge Computing
• 5G/6G Technologies
• +5 more

Dr. Giang T. Nguyen is an academic researcher affiliated with Dresden University of Technology (Germany), specializing in advanced networking technologies. His primary affiliation is with the College of Computer Science and the Department of Networking and Communication Systems. He collaborates extensively with Prof. Frank H. P. Fitzek and other researchers on cutting-edge projects. Research Interests: Giang's work focuses on Network Functions Virtualization (NFV) , Edge Computing , Time-Sensitive Networking (TSN) , 5G/6G Technologies , and In-Network Computing (COIN) . He explores applications in industrial automation, tactile internet, and immersive media delivery, with a strong emphasis on latency reduction and network reliability. Publications: His recent work includes advancements in programmable network coding, TSN testbed development, and latency-optimized architectures for XR and IoT systems. Over 100+ publications since 2013 reflect his contributions to edge computing frameworks, network simulation tools (e.g., ns-3, OMNeT++), and collaborative SLAM systems. Key Projects: He leads initiatives like the TSN-FlexTest measurement testbed and the NET Playground heterogeneous network lab. His research bridges theoretical networking concepts with practical implementations in industrial robotics and emergency response systems.

Laurie GarrowView profile

Professor

• Aviation
• Urban Air Mobility
• Transportation Systems
• +4 more

Dr. Laurie Garrow is a prominent aviation expert and researcher at Georgia Institute of Technology, directing the ATL@GT Center. Her work focuses on emerging air transportation systems, including electric vertical takeoff and landing (eVTOL) aircraft and urban air mobility (UAM). She leads research on demand modeling for air taxi services, competitive market dynamics, and the integration of new technologies like New Distribution Capability (NDC) in airlines. Her studies analyze commuter behavior, city-specific feasibility of air taxi operations, and policy implications for rural transit funding. Dr. Garrow’s research combines geospatial analysis, discrete choice modeling, and large-scale data to evaluate transportation systems. Notable projects include mapping potential commuter air taxi routes in 40 US cities, comparing UAM with autonomous ground vehicles, and assessing barriers to AAM implementation in Germany and the US. Her work also explores post-COVID-19 airline industry transformations and innovative teaching methods in flipped classrooms. Her publications span transportation engineering, policy, and operations research, with a focus on advancing sustainable mobility solutions. Collaborations with industry partners like Uber Elevate and NASA highlight her role in bridging academic research with real-world applications.

Carlo Augusto GraziaView profile

Assistant Professor

• Networking Protocols
• Vehicular Communications
• Wireless Networks
• +6 more

Carlo Augusto Grazia is a Tenure Track Assistant Professor at the Department of Engineering Enzo Ferrari, University of Modena and Reggio Emilia. He holds a M.S. in Theoretical Computer Science (2012) and a Ph.D. in ICT (2016). His research focuses on networking protocols, vehicular communications, and wireless systems optimization. Key projects include the SURPRISE satellite software project, the IGNITE 5.0 smart industrial networking initiative, and V2X safety-related applications with Alstom. He has collaborated on EU-funded projects like FP7 PPDR-TC and PRIN 2009 SFINGI. His work spans TCP/IP algorithms, QoS, SDN, and deadlock analysis in concurrent systems. Recent studies explore 5G energy efficiency, V2X field trials, and 5G MEC architectures for smart cities. He has developed tools like Flent, TEMPEST, and PINK AQM. His contributions are published in IEEE journals and conferences, emphasizing practical solutions for emergency networks and high-speed vehicular systems. Education: M.S. (2012, Bologna), Ph.D. (2016, Modena and Reggio Emilia). Research Interests: Linux-based TCP/IP optimization Vehicle-to-Everything (V2X) communication standards (IEEE 802.11p) SDN and resource pooling for public safety Low-latency wireless protocols Emergency network architectures Articles Trends: Focus on TCP performance in wireless environments, vehicular safety systems, and 5G energy footprint analysis. Recent work addresses latency in smart city applications and modular packet scheduling frameworks. Advising & Grants: Led projects on satellite-LTE integration, disaster recovery networks, and automotive smart areas. Collaborations include ESA, Alstom, and EU initiatives. Labs/Teams: Active in the Modena Automotive Smart Area (MASA) and develops open-source tools like C2ML middleware for satellite optimization.

• Operations Research
• Logistics Optimization
• Combinatorial Optimization
• +3 more

Matthias Prandtstetter is an External Lecturer in the Department of Logic and Computation at TU Wien. His research focuses on optimization in logistics, transportation systems, and combinatorial problems. He has contributed to projects such as Algorithmic Discrete Optimization (EU-funded 2004–2008) and MyITS (2011–2013). His work integrates metaheuristics, integer programming, and semantic technologies to address challenges in warehouse logistics, home healthcare scheduling, and document reconstruction. Education: PhD in Hybrid Optimization Methods, TU Wien (2009) Diploma in Exact and Heuristic Methods for Car Sequencing, TU Wien (2005) Research interests include: Logistics optimization Metaheuristics and hybrid algorithms Multi-modal routing systems Document reconstruction from shredded fragments Production scheduling and car sequencing Key projects: Algorithmic Discrete Optimization (EU, 2004–2008) MyITS: Semantically Enriched Multi-Modal Routing (2011–2013) ASP and HEX-Programme (2008–2015) His research has addressed practical applications like cooperative delivery systems, spare parts warehouse routing, and cross-cut document reconstruction. Advising & grants: Supervised 8 students in optimization-related theses Received EU funding for Algorithmic Discrete Optimization Labs/teams: Active in TU Wien's operations research and logistics research groups, collaborating on projects involving semantic technologies and combinatorial optimization.

Zakka Ugih RizqiView profile

Researcher

• Optimization
• Simulation-Optimization
• Data Science
• +4 more

Zakka Ugih Rizqi is a Researcher at Aalborg University's Department of Materials and Production within the Faculty of Engineering and Science. His research focuses on optimizing decision-making through the fusion of optimization techniques, multi-method simulation, and data science, with primary applications in supply chains, energy systems, and smart manufacturing. He currently contributes to the design of future aseptic production systems through the AP2030 project. He holds a Ph.D. in Industrial Engineering from National Taiwan University of Science and Technology (2022–2024). Research Interests: His work emphasizes realistic modeling of complex systems, including uncertainty and dynamic complexity. Key areas include simulation-based Digital Twin frameworks for smart warehouses, energy-efficient AS/RS configurations, and system dynamics modeling for policy analysis. He explores multi-objective optimization approaches to balance operational efficiency and environmental sustainability. Projects & Grants: He participates in the AP2030: BRD Aseptic Factory 2030 (2023–2027), focusing on aseptic production innovation. His research has been recognized with awards such as The Best Paper Award (2023) and The Best Presenter Award (2023). Collaborations: Recent visits include The University of Tokyo, Kyoto University, and Tohoku University (2024). He has presented at conferences like the National University of Singapore's Analytics for X 2023. Labs & Teams: Engaged in interdisciplinary teams within the Faculty of Engineering and Science, collaborating on projects blending automation, sustainability, and data-driven decision-making.

Frédéric PICHONView profile

Professor

• Uncertain Reasoning
• Information Fusion
• Machine Learning
• +3 more

Frédéric Pichon is a Full Professor at Artois University's Laboratory of Computer Engineering and Automation of Artois (LGI2A), part of the Faculty of Applied Sciences in Béthune, France. He holds a Ph.D. in Information and Communication Systems (2009) and an H.D.R. (2018) from Artois University. His research focuses on uncertain reasoning, information fusion (particularly belief functions theory), and machine learning applications in decision-making under uncertainty. Education: B.Eng. (Hons) in Computer Networks and Distributed Systems, Edinburgh Napier University, 2003 M.Sc. in Information Technology, Aalborg University Esbjerg, 2005 Ph.D., University of Technology of Compiègne, 2009 H.D.R., Artois University, 2018 Research Interests: Belief functions theory and applications Optimization under severe uncertainty Machine learning with uncertain data Information fusion for decision support Articles Trends: Recent work emphasizes belief function-based solutions for optimization problems (e.g., shortest path, vehicle routing) and deep learning integration with uncertainty quantification. Early-career contributions include foundational work on Dempster-Shafer theory and conflict analysis. Awards: Notable recognitions include the IJAR Early Career Researcher Award (2022), multiple best paper awards at LFA and BELIEF conferences, and distinctions for student research supervision. Advising & Grants: Supervised 9 PhD students (3 in progress). Active in organizing international conferences (BELIEF, LFA) and editorial roles (Area Editor for International Journal of Approximate Reasoning). Research spans theoretical advancements and applied projects in logistics, image processing, and simulation. Labs/Teams: Core member of LGI2A, collaborating with institutions like Heudiasyc and IBISC labs on uncertainty modeling and fusion systems.

• Robotics Engineering
• Soft Robotics
• Additive Manufacturing
• +4 more

Dr. Markus Nemitz is an Assistant Professor of Mechanical Engineering at Tufts University's School of Engineering, leading the Nemitz Robotics Group. He holds a Ph.D. from the University of Edinburgh (2018) and completed postdoctoral training at Harvard University until 2020. Before joining Tufts in 2024, he served as an Assistant Professor at Worcester Polytechnic Institute (WPI) from 2020–2024. His research focuses on soft robotics, additive manufacturing, and fluidic control systems, with notable work on 3D-printable robots and robot ecosystems. He has secured significant funding, including an NSF CAREER Award (2023) and grants from the U.S. Army and EPA. His recent projects include combatting algal blooms via 3D-printed floating photocatalysts. Education: Ph.D. in Mechanical Engineering, University of Edinburgh (2014–2018) M.S. in Mechanical Engineering, University of Edinburgh (2013–2014) B.Eng. in Mechanical Engineering, Bochum University of Applied Sciences (2009–2012) His research interests span soft robotics, adaptive systems, and environmental applications. Recent work emphasizes fluidic control mechanisms, non-electronic actuators, and bio-inspired designs. His publications in Science Robotics , PNAS , and IEEE Robotics and Automation Letters highlight contributions to soft robotics integration and additive manufacturing. Scientific Awards: NSF CAREER Award (2023) Grants & Funding: NSF-RoseHub (Robot Ecosystems) U.S. Army (Robotics) U.S. EPA (Algal Bloom Mitigation) His lab develops open-source tools like the Soft Compiler for modular pneumatic circuits and explores applications in extreme environments and environmental engineering.

Mladenovic NenadView profile

Professor

• Operational Research
• Combinatorial Optimization
• Global Optimization
• +9 more

Nenad Mladenovic is a Professor at the Faculty of Organizational Sciences, University of Belgrade, and holds concurrent roles including Reader at Brunel University-London (United Kingdom) and Research Professor at the Mathematical Institute of the Serbian Academy of Sciences. His academic journey includes senior positions in institutions across Serbia, Canada, and the UK, with extensive visiting professorships in France, Canada, Hong Kong, and Spain. Professional Background: 2007–Present: Professor, Faculty of Organizational Sciences, University of Belgrade 2005–Present: Reader, School of Mathematics, Brunel University-London 2002–Present: Research Professor (Scientific Chancellor), Serbian Academy of Sciences Research Focus: His work centers on metaheuristic methods (e.g., Variable Neighborhood Search, Tabu Search) in combinatorial and global optimization, numerical algorithms, and mathematical programming. Applications span operational research, facility location, clustering, and data mining. Key Achievements: A prolific contributor with over 69 citation index (G-index 69), he is Coeditor-in-Chief of an international journal and holds editorial roles in ~10 journals. Awards include Fellowships from the Academy of Nonlinear Sciences (2008) and Scientific Society of Serbia (2003). He led major research projects, including one funded by the Serbian Ministry of Science (2002–2005). Grants & Collaboration: Secured grants from EPSRC (UK), NSERC (Canada), and CNRS (France). Coordinated over 20 industrial projects, emphasizing applied mathematics and optimization solutions.

Hani MahmassaniView profile

Professor

• Multimodal transportation systems
• Dynamic network modeling
• Autonomous vehicles
• +6 more

Hani Mahmassani is a Professor of Civil and Environmental Engineering at Northwestern University, with courtesy appointments in Industrial Engineering and Management Sciences. He holds the William A. Patterson Distinguished Chair in Transportation and serves as a core faculty member at the Northwestern University Transportation Center. Ph.D. in Transportation Systems from MIT M.S. in Civil Engineering from Purdue University B.S.C.E. from the University of Houston His research spans multimodal transportation systems analysis, autonomous vehicle integration, smart cities, and real-time logistics optimization. Specific interests include dynamic network modeling, transit design, user behavior analysis, and telecommunication-transportation interactions in large-scale human infrastructure systems. Recent publications focus on autonomous mobility pricing, urban air mobility airspace modeling, and stochastic transportation networks. His work addresses critical challenges in mobility-on-demand regulation, freight demand forecasting, and network reliability estimation. William A. Patterson Distinguished Chair in Transportation At the Northwestern Transportation Center, Mahmassani leads research initiatives connecting civil engineering with intelligent transportation systems. His projects integrate machine learning and optimization techniques to solve complex mobility challenges in urban environments.

Tarique AnwarView profile

Lecturer

• Data Science
• Machine Learning
• Artificial Intelligence
• +3 more

Tarique Anwar is a Lecturer in the Department of Computer Science at the University of York, UK, since 2021. He holds a PhD in Data Science from Swinburne University of Technology, Australia. As Programme Leader for both MSc and BSc Data Science programs, he oversees admissions and curriculum development. His academic roles include Data Science Admissions Tutor and membership in the Artificial Intelligence Group. Research interests span Data Science , Machine Learning , Artificial Intelligence , and Social Media Analysis , with a focus on solving real-world problems using structured/unstructured data. Notable areas include mental health detection via social media, network analysis, and explainable AI. His work addresses challenges in: Depression severity assessment using attention mechanisms Rumor source detection in social networks Eating disorder classification through multimodal learning Geographic-social-temporal pattern mining He supervises MS/PhD students in Data Science and Analytics, emphasizing interdisciplinary research. His publications (2020–2025) demonstrate expertise in AI applications for health, social networks, and optimization. Labs/Teams: Artificial Intelligence Group at the University of York, collaborating with industry and academic partners globally.

• Mathematics Education
• Quantitative Skills Development
• Curriculum Design
• +3 more

Shaun Belward is a Senior Lecturer at James Cook University, with a focus on mathematics education and quantitative skills development across disciplines. His research spans applied mathematics, fluid dynamics, and pedagogical innovation. Key Research Areas: Mathematics education, curriculum design for STEM, interdisciplinary collaboration in quantitative skill development, and fluid dynamics modeling. Educational Impact: Led initiatives to improve graduate learning outcomes in mathematics, developed cross-institutional curricula for life sciences, and explored peer group learning strategies. Applied Mathematics: Contributed to solving complex fluid dynamics problems, including supercritical flow over topography, atmospheric interfacial waves, and oil spill containment models using series solutions. Notable Trends: Recent work emphasizes dynamic vehicle routing optimization (2024), while earlier studies focused on bridging vocational and tertiary education pathways (2005-2015) and foundational mathematical misconceptions (2011). Collaborations: Partnered with educators like Kelly Matthews, Leanne Rylands, and Carmel Coady to address challenges in undergraduate science education.