جستجوی استاد راهنمای دانشگاهی

• Supply Chain Management
• Logistics
• Operations Management
• +۷ مورد دیگر

Henk Zijm is a full Professor in the Department of Industrial Engineering & Business Information Systems at the Faculty of Engineering Technology, University of Twente, and is affiliated with the Digital Society Institute. His research spans operations management, logistics, supply chain systems, and stochastic modeling, with a strong focus on practical industrial applications. His research interests include Supply Chain Management , Logistics , Inventory and Spare Parts Control , Multi-Agent Systems , and Digital Transformation in Manufacturing . He has made significant contributions to modeling complex systems under uncertainty, particularly in manufacturing and distribution networks. The 15 most recent articles reflect a consistent focus on supply chain resilience, digital coordination, and sustainable logistics. Key trends include the integration of additive manufacturing in spare parts supply, interactive tools for circular supply chains, and advanced modeling of traffic and production systems. His work bridges theoretical operations research with real-world industrial challenges. Henk Zijm has supervised 24 academic works, indicating active mentorship and PhD guidance. While no specific grants are listed, his extensive publication record and leadership in research areas suggest successful grant acquisition and project leadership. His work has strong interdisciplinary reach, particularly in digital society and systems engineering. He is associated with the Digital Society Institute at the University of Twente, which fosters interdisciplinary research on the societal impact of digital technologies. This affiliation underscores his engagement with broader technological and societal transformations.

Tom Johnstenمشاهده پروفایل

استاد

• Data Mining Algorithms
• Computational Biology
• Machine Learning Applications

Dr. Tom Johnsten is a Professor in the Computer Science Department at the University of South Alabama, part of the School of Computing. He holds a Ph.D. in Computer Science from the University of Louisiana at Lafayette (1998), an M.S. in Computer Science from Arizona State University (1992), and a B.S. in Mathematics from Oklahoma State University (1987). His research focuses on developing data mining algorithms, computational biology, and applying machine learning technologies to interdisciplinary challenges. His work bridges computational methods with biological systems, cybersecurity, and environmental data analysis. Notable contributions include advancements in DNA repair mechanisms, anomaly detection in data streams, and prescriptive maintenance frameworks. He has taught courses such as Digital Logic and Computer Architecture, Operating Systems, Programming Language Theory, and Data Mining. His office is located in Shelby Hall, Room 1119, with regular office hours on Tuesdays and Thursdays. Dr. Johnsten’s publications reflect a strong emphasis on cybersecurity, bioinformatics, and data-driven solutions. He has explored topics like mitochondrial DNA repair, robotic system security, and synthetic signal peptide design. While no scientific awards are explicitly listed, his extensive publication record highlights sustained academic contribution. Teaching and advising roles include mentoring students through courses like Research Methodologies and Database Design. His work integrates theoretical computer science with practical applications in diverse fields.

Xing Liمشاهده پروفایل

استاد

• International Relations
• Globalization and Social Change
• China and International Relations
• +۵ مورد دیگر

Xing Li is a Professor in the Department of Politics and Society at Aalborg University, affiliated with the Faculty of Social Sciences and Humanities. Their research focuses on global political dynamics, particularly China's role in international relations, multi-order world governance, and Arctic peace initiatives. Key projects include the 'Anticipating Governance in the Coming Multi-Order World' (2023-2026) and 'Preserving the Arctic as an International Zone of Peace' (2021-2023). Education details are not explicitly provided in the text, but Li's expertise spans international relations theory, political economy, and development studies. Their research interests integrate themes like globalization, social change, and East Asian regional integration, often addressing contemporary challenges in global governance. Recent research trends include interdisciplinary collaboration between political science and technical fields (e.g., energy systems and materials science), as evidenced by publications on fuel cell diagnostics and battery technologies. These studies highlight innovative applications of data-driven methods in both social and engineering contexts. Li has received the 'Teacher of the Year' award (2020) and has supervised one PhD student, J. Pang. They actively engage in academic activities, including organizing conferences on global south dynamics and multi-order world narratives. Li's work is supported by grants from institutions like the Asia Foundation and has been widely disseminated through media outlets like China Daily and South China Morning Post .

Frauke Liersمشاهده پروفایل

استاد

• Optimization under Uncertainty
• Distributionally Robust Optimization
• Quantum Computing
• +۵ مورد دیگر

Prof. Dr. Frauke Liers holds the Professorship of Optimization under Uncertainty & Data Analysis at the Department of Data Science (DDS), Friedrich-Alexander-University Erlangen-Nürnberg. Her research focuses on robust and distributionally robust optimization, mathematical programming, and applications in energy systems, healthcare logistics, and quantum computing. Email: frauke.liers@fau.de ResearchGate: Frauke Liers Research Interests span optimization under uncertainty, data-driven mathematical programming, and interdisciplinary applications. Key areas include: Distributionally robust optimization with scenario reduction and chance constraints Quantum computing optimization for gate routing and noise suppression Energy system modeling (photovoltaics, gas networks, electricity networks) Healthcare logistics (patient transport scheduling under uncertainty) Nanoparticle technology and chemical process optimization Recent Publications emphasize: Advancements in quantum circuit optimization (2025) Explainable optimization methods (2024) Robust approaches for particle precipitation control (2024) Dynamic trajectory optimization (2023) Time-expanded models for network flows (2022)

Bernhard Heinlothمشاهده پروفایل

پژوهشگر

• Operating Systems
• Kernel Optimization
• Compiler-Based Tailoring
• +۴ مورد دیگر

Bernhard Heinloth, M.Sc., is affiliated with the Department of Computer Science (INF) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), specifically within Chair of Computer Science 4 (System Software). His research focuses on system software optimization, kernel tailoring for embedded systems, and dynamic software updates. He has contributed to projects like Luci (dynamic shared object updates), Cocoon (on-demand kernel compilation), and Honey (binary library reduction). Teaching activities include leading exercise sessions for courses such as Betriebsysteme (Operating Systems), Systemnahe Programmierung in C (Low-Level Programming), and Praktikum angewandte Systemsoftwaretechnik (Systems Software Lab). He has advised numerous master's theses on topics ranging from interrupt latency analysis to compiler-based OS optimization. His publications span academic venues like USENIX ATC, PLOS, and SBESC, with a strong emphasis on practical system software engineering. Current work continues exploring compiler-driven system optimizations and real-time kernel performance.

• Data Science
• Blockchain Technology
• Transportation Systems
• +۷ مورد دیگر

John Easton is a Professor in the School of Social Sciences at Heriot-Watt University, United Kingdom. His research bridges technology, data science, and transportation systems, with a strong focus on blockchain applications, data quality, and railway infrastructure. He actively contributes to advancements in smart cities and industrial information integration. Research Interests: His work centers on data-driven decision-making in industrial and transport contexts. Key areas include blockchain-based systems for public transit ticketing, data quality in industry, railway track monitoring, and digital transformation in transportation. His research supports UN Sustainable Development Goals through innovations in sustainable infrastructure and smart mobility. The recent publications indicate a strong trend in applying blockchain and data analytics to transportation and industrial systems. Topics such as decision-making frameworks for blockchain adoption, integrated data platforms for railway maintenance, and secure data sharing in smart cities reflect a cohesive research agenda focused on technological innovation for societal benefit. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: While no direct information on students or grants is available, his senior academic rank and extensive publication record suggest active supervision and research leadership. Collaborations with researchers like J.D. Preece and Q. Fu indicate team-based research projects likely funded by external grants. Labs and Teams: Although no specific lab or research group is named, his collaborative work and focus areas suggest involvement in interdisciplinary research teams working on smart transportation, blockchain applications, and industrial data systems at Heriot-Watt University.

Melody Khadem Sameniمشاهده پروفایل

دانشیار

• Railway Operations
• Transportation Planning
• High Speed Railway
• +۴ مورد دیگر

Melody Khadem Sameni is an Associate Professor of Rail Transportation Engineering and Programme Director of the MSc Railway Systems Engineering and Integration at the University of Birmingham Dubai Campus. She previously served as Assistant Professor and Head of the Railway Transportation Group at Iran University of Science and Technology. Her expertise spans railway operations, management, systems engineering, and sustainability. Education: PhD in Civil Engineering, University of Southampton, 2012 MSc in Socio-Economic Systems Engineering, Tarbiat Modares University, 2008 BSc in Railway Transportation Engineering, Iran University of Science and Technology, 2003 Her research focuses on optimizing railway operations through systems engineering approaches, addressing capacity, demand analysis, safety, and sustainability. Notable contributions include work on railway noise impacts, high-speed rail demand forecasting, and gender differences in transportation preferences. She actively collaborates with global organizations like the International Union of Railways (UIC) and International Association of Public Transport (UITP). Awards include the ICE Best Innovative Poster Award and a University of Southampton personal development award. She has chaired sessions at major conferences such as WCRR and TRB, and served as a panellist for UITP Women in Public Transport 2024. Her publications span topics like railway capacity optimization, environmental impact assessments, and post-pandemic transport behavior. She holds leadership roles in academic committees, including Co-Chair of Special Interest Group A3 (Rail Transport) at the World Conference on Transport Research Society.

• Wind Energy Systems
• Renewable Energy
• Energy Systems Engineering
• +۴ مورد دیگر

Matti Juhani Koivisto is a Senior Researcher at the Department of Wind and Energy Systems at the Technical University of Denmark (DTU). His work focuses on renewable energy systems integration, particularly wind power modeling and analysis within large-scale energy systems. His research expertise spans several critical domains in renewable energy: Wind Energy Systems Engineering (100%) Wind Power Engineering (89%) Variable Renewable Energy Integration (80%) Power Engineering (56%) Time Series Analysis in Energy Systems (37%) Energy Engineering (35%) Dr. Koivisto's research addresses fundamental challenges in renewable energy integration, with particular emphasis on wind power forecasting, variability analysis, and the impacts of climate change on renewable resources. His work bridges theoretical modeling with practical energy system applications, contributing to more reliable and efficient integration of renewable energy sources into power grids. His recent publications demonstrate a strong focus on offshore wind energy, hybrid power plants, and advanced modeling techniques for wind farm generation. His work on increasing the resolution of solar and wind time series has been particularly influential in energy system modeling, addressing critical data requirements for high renewable penetration scenarios. Dr. Koivisto contributes significantly to UN Sustainable Development Goals related to affordable and clean energy (SDG 7) and climate action (SDG 13), with research directly addressing the technical challenges of transitioning to sustainable energy systems. He is actively involved in several major international research projects including AWETRAIN (Airborne Wind Energy Training), DTWO (Federated Digital Twins for Wind-Offshore), and GREAT (GRid Enhancement for Ancillaries in Tomorrow's power systems), where he serves as both project participant and PhD supervisor.

Masoud H. Nazariمشاهده پروفایل

استادیار

• Power Systems
• Grid Optimization
• Resilience Engineering
• +۶ مورد دیگر

Dr. Masoud H. Nazari is an Assistant Professor in the Department of Electrical and Computer Engineering at Wayne State University's College of Engineering. His research focuses on resilience and optimization of modern power systems through applications of stochastic hybrid systems, machine learning, and control theory. He holds leadership positions including Chair of IEEE Smart Buildings, Loads, and Consumer Systems Architecture Subcommittee and serves on IEEE Technology Conferences editorial boards. Education: Ph.D. in Electrical & Computer Engineering/Engineering & Public Policy from Carnegie Mellon University (2012), with visiting research at Massachusetts Institute of Technology. M.S. in Engineering & Public Policy from Carnegie Mellon University (2010). Research Focus: Primary interests include modern power systems resilience, stochastic hybrid systems, large-scale grid optimization, AI-enabled community resilience assessment, cyber-physical systems, and IoT applications in energy management. His work bridges theoretical control systems with practical energy infrastructure challenges. Publication Trends: Recent articles demonstrate strong focus on distributed control systems, resilience engineering in power grids, AI/ML applications for anomaly detection and optimization, and microgrid management. Work frequently addresses communication failures, stochastic variations, and cyber-physical security challenges in energy systems. Awards and Honors: WSU Excellence in Teaching Award (2024) Top 10 Team - ARPA-E Grid Optimization Challenge (2022-23) DTE E-Challenge Winner (2022) IEEE Senior Member (2019) Best Paper Award - North American Power Symposium (2017) Research Leadership: Principal Investigator for NSF AMPS ($200K): Stochastic contingency detection in power systems Co-PI for DOE RACER ($1M): Energy-water nexus resilience planning PI for ARPA-E Grid Optimization Challenge ($400K) Led DTE E-Challenge project developing AI-controlled HVAC systems Research Group: Leads the Modern Power Systems research team with current PhD students Hamid Varmazyari and Antar Kumar Biswas. Former advisees include Dr. Siyu Xie (now Assistant Professor in China) and multiple graduate/undergraduate researchers. The group focuses on control algorithms, resilience analytics, and optimization frameworks for power infrastructure.

• Information Visualization
• Software Systems
• Cybersecurity
• +۳ مورد دیگر

Bill Buchanan is a Professor at the School of Computing Engineering and the Built Environment, Edinburgh Napier University, and a member of the Centre for Algorithms, Visualisation and Evolving Systems. His work spans Information Visualization , Software Systems , Cybersecurity , and AI , focusing on innovative solutions for complex data challenges. Research Interests: Information visualization for hierarchical data, cybersecurity frameworks, machine learning in industrial IoT, and post-quantum cryptographic systems. Projects: Supervised PhD projects on visualizing overlapping classification hierarchies and biological network inference. Currently leads research in AI-driven human-robot interaction and secure Dockerized architectures. Publications: 15+ recent works on quantum agents, GANs for anomaly detection, homomorphic encryption, and advanced cryptographic techniques. Collaborations: Engages with biologists, AI researchers, and cybersecurity experts across institutions.

• Aviation Safety
• Fatigue Management
• Air Transport Policy
• +۳ مورد دیگر

Maria Papanikou is a Senior Lecturer in Aviation Engineering at the Aviation Academy, Amsterdam University of Applied Sciences. She holds a PhD from the University of Greenwich (2017), focusing on 'The philosophy of safety: system changes and the re-organisation of the aviation industry'. Her research emphasizes aviation safety, fatigue management, and policy-making in air transport. Key areas include operator variability in advanced systems, neuroscientific tools for fatigue monitoring, and safety metrics for aviation organizations. Recent work explores crisis-driven HR practices' impact on aviation safety and the application of big data in risk assessment. She has supervised student theses on MRO incident taxonomies and just culture in aviation. Collaborations include EUROSIM Congress and Rijkswaterstaat. A notable dataset, 'raak pro SMS Aviation', examines safety management systems in aviation. Her research outputs span peer-reviewed journals like Aircraft Engineering and Aerospace Technology, and she actively participates in academic conferences. Her contributions bridge engineering, cognitive science, and policy to enhance aviation safety frameworks.

Izabela Michalska-Dudekمشاهده پروفایل

استاد

• Customer Loyalty
• Customer Relationship Management
• Consumer Behavior
• +۳ مورد دیگر

Dr. Izabela Michalska-Dudek is a Professor and Head of the Department of Marketing and Tourism Management at Wrocław University of Economics’ Jelenia Góra Branch. She specializes in customer loyalty mechanisms, consumer behavior in tourism, and digital transformation impacts on travel agencies. Her work emphasizes empirical studies on CRM systems, post-pandemic travel decision-making, and predictive modeling for loyalty programs. Research Interests: Customer Relationship Management (CRM), Virtualization in Tourism, Pandemic Impact Analysis, Retail Tourism (ROPO), and Machine Learning applications in loyalty analytics. She actively publishes in peer-reviewed journals and organizes international conferences like the 'Current Trends in Spa, Hotel and Tourism' series. Publications focus on analyzing socio-economic factors influencing travel behavior, crisis management strategies, and technological adoption in the tourism sector. Recent work explores how machine learning can uncover consumer loyalty motives and quantify pandemic-related behavioral shifts. No scientific awards listed. She supervises doctoral students and is open to socio-economic collaborations. Consultations available via MS Teams on Fridays.

Stephen K. Robinsonمشاهده پروفایل

استاد

• Turbulence flow physics and modeling
• Experimental techniques
• Instrumentation
• +۷ مورد دیگر

Stephen K. Robinson is a Professor in the Department of Mechanical and Aerospace Engineering at the University of California Davis. His work focuses on turbulence flow physics, experimental techniques, and aerospace applications, with recent emphasis on human-robot interaction, space systems, and autonomous technologies. Research interests include turbulence modeling, advanced instrumentation, 3D visualization, and safety engineering. His work integrates applied aerodynamics with cognitive psychology to address challenges in space exploration, such as haptic interfaces, VR training, and human performance under extreme conditions. Recent publications highlight innovations in spacecraft maintenance, robotic systems, and autonomous glider design. His projects often combine hardware development with simulation tools, such as digital twins and real-time performance metrics for manual spacecraft operations. Robinson collaborates on NASA-related initiatives including EVA training systems, habitat technology, and lunar construction dynamics. He has pioneered solutions for CO2 removal systems, spacesuit glove ergonomics, and radiation-resistant materials for extraterrestrial missions.

Steven A. Velinskyمشاهده پروفایل

استاد

• Mechanical Systems Design
• Automated Highway Maintenance Technology
• Robotics in Construction
• +۳ مورد دیگر

Professor Steven A. Velinsky is a faculty member in the Department of Mechanical and Aerospace Engineering at the UC Davis College of Engineering. His research focuses on mechanical systems analysis, design, and automation, particularly in the context of highway maintenance and construction technologies. He directs the Advanced Highway Maintenance & Construction Technology (AHMCT) Research Center, which develops advanced automation and robotics solutions to enhance safety and efficiency in highway operations. His work spans diverse applications including wire ropes, vehicles, air bearings, ballscrew mechanisms, and automated machinery for tasks like snowplowing, pothole repair, and debris removal. The AHMCT Center emphasizes interdisciplinary innovation to address challenges in public infrastructure maintenance. Professor Velinsky collaborates with agencies like Caltrans to evaluate and implement new technologies such as the TowPlow, Sealzall Machine, and robotic systems for automated tasks. Recent research trends include optimizing traction control in winter conditions, improving deicing methods, and advancing fuel cell-powered lighting systems. His projects highlight the integration of robotics, control systems, and mechanical engineering principles to solve real-world infrastructure challenges. No notable scientific awards are listed, though his contributions to highway automation are significant. He has advised numerous projects but no specific student names are provided in the text. Key lab affiliations include the AHMCT Center, which serves as a hub for applied research in transportation infrastructure. Projects focus on both mechanical design and system-level automation, with applications ranging from pavement maintenance to roadside vegetation control.

Kjell Gunnar Robbersmyrمشاهده پروفایل

استاد

• Mechatronics
• Machine Design
• Condition Monitoring
• +۵ مورد دیگر

Kjell Gunnar Robbersmyr is a Professor at the University of Agder's Department of Engineering Sciences and director of the Top Research Center in Mechatronics. With a Ph.D. in mechanical engineering from NTNU (1992), his career spans academic leadership, research management at Agder Research, and active contributions to IEEE. His work focuses on mechatronics, machine design, and condition monitoring, with special emphasis on fault diagnosis in electric motors and vehicle crash modeling. Senior Member of IEEE Member of Norwegian Academy of Technical Sciences Member of Agder Academy of Sciences Research interests include: Advanced fault diagnosis in electric drives using AI and signal processing Vehicle crashworthiness modeling with lumped parameter and finite element methods Optical measurement technology for machine monitoring Digital twin applications for infrastructure and wind energy systems Condition monitoring of low-speed bearings and rotating machinery Recent publications demonstrate expertise in: Deep learning for imbalanced motor fault datasets Transformer networks in power electronics diagnostics 3D reconstruction techniques for mechanical systems Multi-classifier decision fusion in power systems Dynamic operations modeling for electric vehicles Scientific contributions include: Over 50 peer-reviewed articles Leadership in the Intelligent Monitoring research group Development of novel inverter topologies Innovations in wind turbine condition monitoring Advancements in laser-based mechanical diagnostics