Find Academic Supervisors

• Healthcare Operations Management and Analytics
• Supply Chain Coordination
• Inventory Management
• +9 more

ZHENG Zhichao Daniel is a Full-time Faculty Associate Professor of Operations Management at the Lee Kong Chian School of Business, Singapore Management University. He also serves as the PGR Coordinator for Operations Management and Area Coordinator of Operations Management since 2020. Education: Ph.D. in Management, National University of Singapore (2013) B.S. in Mathematics, National University of Singapore (2009) Research interests focus on Healthcare Operations Management and Analytics , Supply Chain Coordination , and Robust/Distributionally Robust Optimization . His work integrates quantitative methods to address challenges in healthcare systems, inventory management, and stochastic resource allocation. Key trends in his 15 most recent articles (2011–2023) include: Applications of optimization under uncertainty in healthcare and logistics Development of scheduling and inventory allocation models for service systems Integration of robust optimization techniques with real-world problems Analysis of pandemic response strategies and healthcare resource allocation Scientific Awards: POMS-HK Best Student Paper Competition (2022) Lee Kong Chian Fellowship (2019, 2021) Dean's Impact Award (2017) Multiple academic recognitions for research excellence and student mentorship Advising & Grants: Currently supervising PhD student WANG Xiaodong and DBA candidate CHEN Ping. His research has been supported by grants from Singapore Ministry of Education and institutional fellowships. Labs/Teams: Active contributor to the Operations Management research group at Lee Kong Chian School of Business, focusing on cross-disciplinary projects in healthcare analytics and supply chain innovation.

• Operations Research
• Queueing Theory
• Traffic Management
• +3 more

Professor Neil Walton is a Professor and Deputy Head of the Department of Operations Management, Strategy, Innovation, Information Systems, and Entrepreneurship at the Business School. His research primarily focuses on operations research, queueing theory, stochastic processes, and their applications in transportation systems, networking, and optimization. He has contributed significantly to understanding scheduling algorithms, traffic signal control, and decentralized systems. His work bridges theoretical foundations with practical applications in urban mobility and resource allocation. Research interests include advanced scheduling techniques for quantum switches, stochastic approximation methods, and optimization frameworks for vehicular networks. He has explored topics such as regret analysis in multi-arm bandits and the convergence of stochastic processes. His studies often address real-world challenges like traffic congestion and healthcare resource management through rigorous mathematical modeling. Publications highlight a consistent focus on optimizing systems under uncertainty, with notable contributions to network stability, adaptive control mechanisms, and reinforcement learning applications. His work on platooning in connected vehicle networks and decentralized signal control systems underscores a commitment to cutting-edge solutions for modern transportation challenges. Professor Walton has advised students such as Yao Shao, contributing to the training of future researchers in operations management and systems optimization.

Stef LemmensView profile

Assistant Professor

• Sustainable Supply Chains
• Humanitarian Logistics
• Closed-Loop Supply Chains
• +5 more

Dr. Stef Lemmens is an Assistant Professor at the Department of Technology and Operations Management at Rotterdam School of Management (RSM), Erasmus University Rotterdam. His research focuses on sustainable supply chains, particularly humanitarian logistics and closed-loop systems, employing methods like queueing networks and dynamic programming. He collaborates with industries including consumer electronics and biopharmaceuticals. Education: PhD in Applied Economics from KU Leuven (Belgium), Postdoctoral Research at INSEAD (France) Research interests emphasize practical applications of operations research in supply chain optimization. Recent work includes cross-national medicine stockpiling strategies and closed-loop systems in consumer electronics. His methodologies bridge theoretical models with real-world industry challenges. No scientific awards explicitly mentioned, but publications appear in top journals like Manufacturing & Service Operations Management and European Journal of Operational Research. Advising and grants: No specific students/grants listed. Collaborations include projects with EU member states and biopharmaceutical companies. Labs/teams: Engaged in vaccine supply chain design initiatives and cross-industry partnerships through RSM's operations management network.

Xu YingView profile

Senior Lecturer

• Operations Management
• Data Science & Analytics
• Supply Chain Management
• +5 more

Xu Ying is a Senior Lecturer in the Engineering Systems and Design (ESD) department at Singapore University of Technology and Design (SUTD). Her research focuses on applying stochastic modeling and game theory to address challenges in service operations management, socially responsible operations, and renewable energy demand-side management. She holds a Ph.D. in Operations Management from Carnegie Mellon University (2015), an M.Phil. in Industrial Engineering from Hong Kong University of Science and Technology (2008), and a B.E. in Control Science from Zhejiang University (2006). Key research areas include optimizing service systems through variability introduction, energy consumption coordination in consumer cooperatives, and crowdfunding models for green energy investment. Her work integrates analytical frameworks with real-world applications in transportation, healthcare, and multi-robot systems. Xu has received notable awards including Honorable Mentions in POMS Student Paper Competitions and the William Larimer Mellon Doctoral Fellowship. Her research trends emphasize interdisciplinary approaches, combining operations research with emerging technologies like multi-agent systems and renewable energy. Advising and grant details are not explicitly listed in the provided materials. Xu's contributions span both theoretical advancements and practical solutions for sustainable and efficient operations management.

Duan LingjieView profile

Associate Professor

• Machine Learning & AI
• Network Economics
• Algorithmic Game Theory
• +5 more

Lingjie Duan is an Associate Professor (tenured) and Associate Head of Pillar (Research) in the Engineering Systems and Design School at Singapore University of Technology and Design (SUTD). He holds a PhD in Information Engineering from The Chinese University of Hong Kong (2012) and was a visiting scholar at UC Berkeley's EECS Department. His research focuses on network economics, algorithmic game theory, and AI-driven networking, with emphasis on optimizing wireless systems, UAV networks, and distributed learning mechanisms. He has received prestigious awards including the IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award (2015) and SUTD Excellence in Research Award (2017). His work spans over 150 publications in top-tier journals/conferences, including 6 ESI Highly Cited Papers. He leads the Network Economics & Optimization Lab (NEOL) and serves as Editor for IEEE Transactions on Networking and Mobile Computing. Key research areas include: Machine Learning & AI in Networking Game Theory-driven Resource Allocation UAV-assisted Communication Systems Cyber-Security & Privacy Energy-Efficient IoT Systems Recent grants include projects on privacy-protected AI methods (SGD$0.5M) and decarbonizing urban transportation via crowdsourcing (SG$0.3M). He actively collaborates with institutions like MIT, UC Berkeley, and NUS.

• Applied Probability
• Stochastic Processes
• Matrix-Analytic Methods
• +15 more

Dr. Angus Lewis is a Lecturer in applied probability within the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. His research focuses on stochastic processes, matrix-analytic methods, and their applications in tracking, optimization, and data science. He explores theoretical and applied aspects of matrix-exponential distributions, Markov-modulated processes, and hidden Markov models, with applications in signal processing and ecological time-series analysis (sclerochronology). His work also involves stochastic filtering, parameter estimation for continuous-time Markov chains, and Markov decision processes. Research interests include the probabilistic interpretation of stochastic systems, near-real-time tracking using diverse data sources, and approximations of complex stochastic processes (e.g., multi-type branching processes, fluid queues). Dr. Lewis’s methods bridge theory and practice, aiming to solve optimization and modeling challenges in dynamic environments. No specific articles, awards, or grants are listed in the provided text. He is eligible to supervise postgraduate research given his academic role.

• Software Engineering
• Reverse Engineering
• Web Applications
• +3 more

Giuseppe Antonio Di Lucca is an Associate Professor at the University of Sannio, Department of Engineering. His research focuses on software engineering, reverse engineering, web applications, and aspect-oriented programming. He teaches courses including Databases and Elements of Computer Science for engineering students. Research Interests: His work spans model-driven engineering, design pattern detection, aspect mining, software maintenance, and web application testing. Key areas include: Reverse engineering methods for aspect mining in object-oriented systems Model-driven approaches for rapid prototyping of web applications Queue theory applications in software maintenance staffing Aspect-oriented frameworks for measurement systems Publication Trends: Di Lucca's 136+ publications emphasize software engineering innovations, with recent works focusing on model-driven design pattern detection (2013), aspect-oriented fault detection (2010), and web application testing techniques (2006). His research demonstrates consistent contributions to improving software quality through reverse engineering and aspect-oriented paradigms.

Matteo SavinoView profile

Professor

• Production Management
• Supply Chain Management
• Maintenance Optimization
• +7 more

Matteo Savino is a Full Professor at the Department of Engineering, Università degli Studi del Sannio . His academic career spans over 25 years with extensive contributions to production management, supply chain optimization, and quality systems. Research focuses on integrating Quality/Safety/Environment management systems Specializes in predictive maintenance and logistics optimization Active in multi-agent system applications for dynamic production environments Research trends show consistent work in: Maintenance cost optimization (1998-2024) Supply chain modeling (2005-2019) Industry 4.0 implementation (2018-2019) Risk analysis frameworks (2017) His publications demonstrate deep engagement with: Fuzzy logic applications Linear scheduling methods Machine learning for maintenance Resource allocation models

Ismail AbdulrashidView profile

Assistant Professor

• Healthcare Analytics
• Population Health
• Transportation and Logistics
• +5 more

Ismail Abdulrashid is an Assistant Professor of Data Analytics in the School of Finance and Operations Management at The University of Tulsa, affiliated with the College of Engineering & Natural Sciences. He contributes to interdisciplinary research at the intersection of operations research, healthcare, and artificial intelligence. Education: Ph.D. in Operations Research, Auburn University M.S. in Data Science, Auburn University M.S. in Mathematics, African University of Science and Technology B.A. in Mathematics, Bayero University, Nigeria Dr. Abdulrashid's research focuses on Healthcare Analytics, Population Health, Transportation and Logistics, and Artificial Intelligence . His work applies advanced mathematical modeling and data science techniques to solve complex real-world problems in healthcare delivery, supply chain resilience, and urban logistics. He leverages operations research and machine learning to optimize systems under uncertainty. His publications span high-impact journals such as the European Journal of Operational Research and Healthcare Analytics , reflecting a strong trend in applying optimization and AI to healthcare operations, chemotherapy modeling, and sustainable logistics. The research demonstrates a consistent focus on dynamic systems, time delays, and stochastic environments. Scientific Awards and Honors: Nominated for the Mayo Teaching Excellence Award, The University of Tulsa, Feb 2024 Outstanding International Student Award, Auburn University, Apr 2019 Teaching Citations Award in Applied Statistics, Auburn University, Nov. 2018 Kevin Doheny Fellowship, Auburn University, December 2017 Best Graduate Student Award in Mathematics, African University, May 2013 Best Undergraduate Student Award, Bayero University-Nigeria, March 2011 Dr. Abdulrashid has received research and teaching recognition, including competitive fellowships and teaching awards. While specific grant details are not listed, his fellowship support and publication record suggest active engagement in funded research. He advises students in data analytics and operations research, though specific advisees are not named. His work bridges theoretical modeling with practical applications in health and logistics systems.

Louis HauseuxView profile

Researcher

• Machine Learning
• Distributed Computing
• Network Theory
• +3 more

Louis Hauseux is a permanent researcher at Inria Sophia Antipolis, actively contributing to the NEO team through publications and conference presentations. His work spans machine learning, distributed systems, and mathematical modeling. Affiliation: Inria Sophia Antipolis Team: NEO Research Interests include federated learning, network optimization, and topological data analysis. Recent work explores Bayesian ensemble methods for one-shot federated learning Stochastic geometry in RIS-assisted cellular networks Hypergraphs for hierarchical clustering Graph-based approaches to galaxy filament extraction Article Trends show a focus on distributed learning frameworks, geometric/topological modeling in machine learning, and applications to both telecommunications and astrophysics. His 2025 publications emphasize sustainability and optimal control solutions. Labs & Teams : Affiliated with the NEO (Networks and Optimization) team at Inria Sophia Antipolis, focusing on algorithm design and system-level optimizations.

Przemysław RybaView profile

Researcher

• Computer Science
• Networking
• Algorithms
• +2 more

Przemysław Ryba is a researcher at the Faculty of Information and Communication Technology , Department of Systems and Computer Networks, Wrocław University of Science and Technology. He focuses on network engineering, anomaly detection, and medical telemetry systems. Current faculty member with active research and publications Specializes in wide area network optimization and biosignal processing Collaborates on software-defined networking and telemetry applications His research spans network resource allocation, heuristic algorithms for WANs, and biomedical signal processing systems. Recent work includes anomaly detection in server metrics and SDN experimental topologies. Publications since 2008 reflect expertise in hierarchical network design, medical telemetry, and computational methods. No formal scientific awards mentioned.

Wael AbughresView profile

Assistant Professor

• Modelling of Communication Networks
• Performance and reliability engineering
• Networking & Wireless Communications
• +5 more

Wael Abughres is an Assistant Professor of Communication Engineering at the University of Tripoli, Faculty of Engineering. He holds a PhD in Communication Engineering from the University of Bradford, UK (2009), and an M.Sc. in Electrical & Electronic Engineering from the University of Tripoli (2000). His academic career spans teaching and research in telecommunications, performance modeling, and wireless networks. Research interests include Communication Network Modeling, Performance and Reliability Engineering using MOSEL language, Cloud Computing, Fiber Optic Systems, and Open Education Resources. He has published 12 journal and conference papers and served as Director of the Information & Communication Technology Centre at University of Tripoli for four years.

Agnieszka JanickaView profile

Researcher

• Stochastic Networks
• Cascading Failures
• Heavy-Tailed Processes
• +3 more

Agnieszka Janicka is a Doctoral Candidate at Eindhoven University of Technology (TU/e) since 2022, affiliated with the Stochastic Resource Sharing Networks group. Her research focuses on cascading failures in networks, heavy-tailed processes in queueing models, and stochastic optimization techniques like Markov Decision Processes and reinforcement learning. Education: Bachelor's in Applied Mathematics (2020), TU/e Master's in Industrial and Applied Mathematics (2022), TU/e Certificate in Energy-related Refurbishments (2017), University College of Northern Denmark Her work contributes to understanding scale-free failures in power transmission and highway networks, combining theoretical analysis with mathematical simulations. She is supervised by Professor Maria Vlasiou and Dr. Fiona Sloothaak and collaborates with EURANDOM, LNMB, and NETWORKS. Scientific Awards: Gold KWG Poster Prize (2023) Teaching Involvement: She teaches courses including Advanced Simulation, Stochastic Simulation, and Stochastic Simulation and Modeling.

Marie-Ange RemicheView profile

Researcher

• Fluid Dynamics
• Queueing Theory
• Markov Processes
• +3 more

Marie-Ange Remiche is a researcher at the Faculty of Computer Science, Université Libre de Bruxelles. Her work spans stochastic modeling , queueing theory , and educational technology , with a focus on fluid queues, Markov processes, and user experience in digital learning tools. Active in network performance analysis and pedagogical innovation , including projects like Conception d'une Application Numérique en Ligne d'Aide à l'Apprentissage and Cartographie et Mutualisation des ressources numériques en Mathématiques . Key collaborations with researchers like Marie De Vleeschouwer-Dieudonne and contributions to conferences such as EPEW, ASMTA, and QPES. Her recent publications address fluid queue dynamics, web configurator usability, and fairness in probabilistic systems. She supervises PhD students and participates in conference committees and peer review activities.

Line Lervik-OlsenView profile

Professor

• Service Marketing
• Strategic Marketing
• Service Innovation
• +3 more

Line Lervik-Olsen is a Professor of Marketing at BI Norwegian Business School and Head of the Department of Marketing. She holds a Ph.D. in Marketing from BI Norwegian Business School and completed a visiting Ph.D. at the University of Michigan Business School. Her expertise spans service marketing, strategic marketing, and innovation, with a focus on consumer behavior, customer satisfaction, and complaint management. She leads the Norwegian Customer Satisfaction Barometer and serves as Research Director for the Norwegian Innovation Index at NHH’s Center for Service Innovation. Her work emphasizes customer-centric innovation and the intersection of technology, sustainability, and service quality. Lervik-Olsen teaches service marketing and strategic marketing at BI, blending academic rigor with practical industry insights. Her research has addressed topics like compulsive social media use, emotional dynamics in service encounters, and balancing digital/social innovation strategies. She has authored books on service and innovation, and her work appears in journals such as Journal of Service Research and European Journal of Marketing . Her contributions extend to media commentary on consumer trends, digital transformation, and corporate responsibility. Lervik-Olsen’s research methodology emphasizes actionable insights for businesses, particularly in leveraging customer feedback to drive innovation and improve service experiences.