جست‌وجوی استاد و پژوهشگر

Murat Yildirimمشاهده پروفایل

دانشیار

• Industrial Engineering
• Operations Research
• Mathematical Programming
• +۵ مورد دیگر

Murat Yildirim is an Associate Professor in the Department of Industrial and Systems Engineering at Wayne State University and Director of the Cyber Physical Systems Laboratory. He holds a PhD in Industrial and Systems Engineering from the Georgia Institute of Technology, where he also completed his M.S. in Operations Research and dual B.S. degrees in Industrial/Systems Engineering and Electrical/Computer Engineering. His research integrates mathematical programming and data analytics to optimize networked systems, with applications in renewable energy, predictive maintenance, and industrial IoT. Key focus areas include: Real-time inference integration for large-scale optimization Sensor-driven asset management and maintenance planning Decentralized optimization frameworks for energy systems Machine learning for failure prognostics His publications (2019–2024) predominantly explore renewable energy optimization (wind/solar maintenance, grid resilience), industrial IoT applications (inventory-maintenance coordination), and decentralized computing methods. Recent works emphasize deep learning for equipment prognostics and privacy-preserving optimization. Awards & Honors: INFORMS Energy, Natural Resources and Environment Best Paper Award (2018) Finalist for INFORMS Data Mining/Quality Statistics Awards (2015–2016) Doctoral Colloquium Poster Competition Winner (2015) Research Funding & Projects: Secured $4.54M total ($1.15M personal share) from NSF, DoE, Ford, and MTRAC. Major projects include DoE-supported PV degradation analytics ($2.8M), NSF-CPS grants for decentralized vehicle fleet optimization, and wind farm digitization initiatives. Laboratory: Leads the Cyber Physical Systems Laboratory (cyphysystemslab.com), focusing on scalable inference and optimization for industrial and energy systems. Advises 7 PhD students and has placed graduates in roles at JP Morgan, Walmart, and BLEND360.

Paul M.J. Van den Hofمشاهده پروفایل

استاد

• Data-driven modeling
• Control systems
• Dynamic networks
• +۶ مورد دیگر

Paul M.J. Van den Hof is a Full Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e), specializing in data-driven modeling and control of dynamic systems. He leads the Control Systems Group and is affiliated with multiple research centers, including EAISI High Tech Systems, Mobility, and Foundational, as well as the EIRES Research initiative. Research Interests: His work focuses on developing fundamental techniques for dynamic network modeling, closed-loop identification, data analytics, experimental design, and model-based control. Applications span industrial process control, oil reservoir engineering, high-tech mechatronic systems, and cyber-physical systems. Recent projects include SYSDYNET (dynamic network identification), NAPAS (nanometer-accurate actuation), and CADUSY (data-driven modeling using symbolic methods). Scientific Contributions: Van den Hof has authored over 250 research outputs, including foundational work on identifiability in dynamic networks, frequency domain identification, and distributed control algorithms. His 2024-2025 publications address challenges in fault detection, Bayesian network topology identification, and multi-step least squares methods. He emphasizes practical implementation through tools like the SYSDYNET MATLAB toolbox. ERC Advanced Research Grant (2015) Honorary Member, Hungarian Academy of Sciences (2016) IEEE Fellow (2007) and IEEE Life Fellow (2023) IFAC Advisor (2020)

Wangda Zuoمشاهده پروفایل

دانشیار

• Building Energy Modeling
• Renewable Energy Systems
• Machine Learning for Energy Systems
• +۳ مورد دیگر

Wangda Zuo is an Associate Professor in the Department of Civil, Environmental and Architectural Engineering at the University of Colorado Boulder. His research focuses on building energy systems, renewable energy integration, and machine learning applications for energy optimization across buildings and power grids. Dr. Zuo's expertise spans building energy modeling and forecasting , renewable resource allocation for resilient communities , and data center energy efficiency . He develops advanced computational frameworks using deep learning and optimization algorithms to solve complex energy challenges, with emphasis on practical implementations for grid stability and energy conservation. His work bridges theoretical innovation with real-world applications through industry and agency collaborations. Analysis of his 2020 publications reveals dominant themes in machine learning-driven building energy forecasting and renewable energy deployment optimization. His research consistently addresses critical intersections between power systems and built environments, particularly focusing on data center grid services and electricity pricing impacts on commercial building efficiency. Dr. Zuo's distinguished recognition includes: Research Development Award from CU Boulder's Civil Engineering Department (2020) ASHRAE Distinguished Service Award (2017) Eliahu I. Jury Early Career Research Award from University of Miami Engineering College (2016) Four consecutive SEEDS Leadership Awards from University of Miami (2013-2016) IBPSA-USA Emerging Professional Award for energy modeling excellence (2016) University of Miami Provost Research Award (2014) IBPSA-USA Best Poster Award (2009) He currently mentors two mechanical engineering PhD students and actively recruits additional graduate researchers through his laboratory. While specific grant details aren't provided in the source material, his award portfolio demonstrates sustained success in securing competitive research funding. His laboratory functions as an interdisciplinary hub for energy systems innovation, with ongoing projects focused on building-grid integration and machine learning applications.

• Transport and Logistics
• Mathematical Economics
• Econometrics
• +۵ مورد دیگر

De Lapparent Matthieu is a Full Professor at HES and Director of the Interdisciplinary Institute for Business Development (IIDE) at the Haute école d'Ingénierie et de Gestion du Canton de Vaud. His expertise spans Transport and Logistics, Mathematical Economics, Econometrics, Industrial Organization, and Behavior Modeling . He leads research projects focusing on urban development, energy systems, and transportation economics. Affiliations: IIDE, HES-SO Key roles: Director, Principal Investigator (4 completed projects funded by Société du Grand Paris, Planair SA, etc.) Research interests include: - Discrete choice modeling for mobility and infrastructure decisions - Energy network optimization (gas distribution, microgrids) - Urban sustainability and brownfield rehabilitation - Risk analysis in transportation and decision-making Recent studies explore EV charging dynamics, blockchain in energy markets, and spatial distribution of employment in Île-de-France. His work integrates quantitative methods with policy applications, emphasizing practical tools for urban planners and industry. Grants and projects total over CHF 100k in funding. Collaborations include institutions like Société du Grand Paris, Mobil'Homme Sàrl, and academic partners within HES-SO.

Georgios Konstantinouمشاهده پروفایل

دانشیار

• Power Electronics
• HVDC Transmission Systems
• Multilevel Converters
• +۷ مورد دیگر

Georgios Konstantinou is an Associate Professor in Energy Systems at the School of Electrical Engineering and Telecommunications, University of New South Wales (UNSW) Sydney. He leads the Real-Time Simulations Laboratory (RTS@UNSW), which hosts the largest Real-time Digital Simulation Laboratory in Australia. Dr. Konstantinou is also an ARC Future Fellow (FT240100038, 2025-2029) for the project "Integration and Stability of Power Electronics Defined Low Inertia Grids," and previously held an ARC Early Career Research Fellowship (DE170100370) focused on High-voltage DC grids. Dr. Konstantinou's educational background includes: PhD in Electrical Engineering from the University of New South Wales (UNSW), Sydney, Australia (2012) Thesis: "Harmonic Elimination Pulse Width Modulation of Modular and Hybrid Multilevel Converter Topologies" Diploma of Electrical Engineering (5-year degree, equivalent to Masters) from Aristotle University of Thessaloniki, Greece (2007) Graduate Diploma in University Learning and Teaching from UNSW (2015) His research focuses on power electronics and energy systems, with particular expertise in HVDC transmission systems, multilevel converters, real-time digital simulations, and hardware-in-the-loop testing. Dr. Konstantinou's work addresses critical challenges in grid integration of renewable energy and energy storage systems, with an emphasis on stability and control of power electronics-defined grids. His research bridges theoretical advancements with practical applications through close collaboration with industry partners including CSIRO and AGL. Analysis of Dr. Konstantinou's recent publications reveals a strong focus on grid-forming converters, digital twin technologies, and advanced control strategies for power systems with high renewable penetration. His work increasingly integrates artificial intelligence and machine learning techniques with traditional power system engineering to address stability challenges in low-inertia grids. Key themes include real-time simulation methodologies, fault analysis in HVDC systems, and innovative control approaches for power electronics interfaces. Dr. Konstantinou's scientific recognition includes: ARC Future Fellow (FT240100038, 2025-2029) ARC Early Career Research Fellow (DE170100370) Australia-China Young Scientist Exchange Program participant (2015) Next Steps Initiative Grant awardee (2016) Associate Editor for IEEE Transactions on Power Electronics Dr. Konstantinou actively supervises research students in areas including multilevel power electronics converters, HVDC systems, modular multilevel converters, and grid integration of large-scale renewable energy systems. His current research is supported by multiple grants totaling over $1.5 million, including an ARC Future Fellowship ($1,066,000), CSIRO Global Power System Transformation Initiative grants ($365,000 each), and various international collaboration grants. These projects focus on real-time simulation, grid integration of renewables, and stability of power electronics-defined grids. As the leader of the Real-Time Simulations Laboratory (RTS@UNSW), Dr. Konstantinou oversees Australia's largest Real-time Digital Simulation Laboratory with extensive capabilities in HVDC networks, multiterminal DC grids, power system protection relay testing, renewable energy systems, and smart grids. The laboratory serves as a critical resource for both academic research and industry collaboration, providing hardware-in-the-loop testing capabilities for next-generation power system technologies.

Adam Abdinمشاهده پروفایل

پژوهشگر

• Urban Mobility
• Critical Infrastructure Resilience
• Energy Systems Optimization
• +۳ مورد دیگر

Adam Abdin is a researcher specializing in interdisciplinary systems optimization, with a focus on urban mobility, critical infrastructure resilience, and space engineering. His work bridges operations research, AI applications, and climate adaptation strategies. He collaborates extensively with institutions on projects involving pandemic response, energy systems, and autonomous technologies. Key research areas include predictive maintenance frameworks, climate change exposure modeling, and on-orbit servicing mission optimization. Developed methodologies for coordinating traffic-power systems and enhancing AI-driven risk management in space missions. Recent research emphasizes strategic planning for pandemic control and optimizing testing strategies, as well as designing resilient energy systems against extreme weather events. His publications highlight contributions to electric vehicle rate design, cyber-risk mitigation in space AI, and comprehensive frameworks for critical infrastructure interdependency.

• Renewable Energy
• Battery Storage
• Electric Vehicles
• +۵ مورد دیگر

Bjorn Sturmberg is a Senior Research Fellow at the School of Engineering, Australian National University (ANU), specializing in renewable energy systems and grid integration. He holds a PhD from the University of Sydney and focuses on battery storage, electric vehicles, photovoltaics, and energy policy. His research addresses sustainability challenges in Australia and global contexts. Key research interests include renewable energy deployment impacts, hydrogen production optimization, grid reliability, and prosumer-driven energy systems. He has collaborated on projects like the Southcoast-grid Reliability Feasibility (SRF) and Next Generation Electric Bus Depot initiatives. Sturmberg has authored influential papers on offshore renewable energy feasibility, socio-economic impacts of energy transitions, and hydrogen production variability. His work integrates technical innovation with policy advocacy, evidenced by contributions to Australia’s Climate Change Authority submissions. He leads or co-investigates seven projects funded between 2019-2024, focusing on microgrids, decarbonization finance, and energy equity for renters.

Mohammad Farhan Khanمشاهده پروفایل

مدرس

• Data Analytics
• Applied Artificial Intelligence
• Mathematical Modelling
• +۴ مورد دیگر

Mohammad Farhan Khan is a Lecturer in Data Science at the University of Roehampton, affiliated with the School of Arts, Humanities, and Social Sciences. His research bridges mathematical modeling, AI, and sustainable development goals.

Ramazan Çağlarمشاهده پروفایل

دانشیار

• Electric Power Distribution
• Reliability Assessment
• System Reliability
• +۱۱ مورد دیگر

Ramazan Çağlar is an Associate Professor in the Department of Electrical Engineering at Istanbul Technical University, College of Engineering. His research focuses on modern power systems, including microgrids, renewable energy integration, reliability assessment, and intelligent control of distributed energy resources. He actively publishes in high-impact journals and leads research initiatives in smart grid technologies. Research Interests: His work spans electric power distribution, system reliability, microgrid dynamics, induction motors, and power transmission. He integrates advanced computational methods such as machine learning, Bayesian inference, and optimization algorithms to solve complex problems in energy systems. His recent focus includes fault prediction using drones, energy forecasting with neural networks, and optimal allocation of distributed generators. Recent Research Trends: Analysis of his latest publications (2022–2024) reveals a strong trend toward data-driven and AI-enhanced modeling in microgrids and renewable integration. He combines physical models with machine learning (e.g., neural ODEs, autoencoders, LSTMs) and applies multi-objective optimization techniques like multiverse optimization. His work increasingly emphasizes uncertainty quantification, real-time control, and sustainable energy solutions. Scientific Projects: Completed: Reliability Evaluation of Power Station Designed for DC-Fed Traction Systems in Light Rail Transit (2011–2021). Advising and Grants: He is currently supervising 14 theses in progress, indicating an active role in mentoring graduate students. While specific grant details are limited, his long-running project funded by ITU's BAP (Scientific Research Projects) suggests sustained research funding. His collaborations include international researchers, particularly in Africa and the Middle East. Labs and Research Teams: Though not explicitly named, his research activities suggest leadership in a power systems and smart grid laboratory at ITU, focusing on reliability, optimization, and AI applications in energy infrastructure.

Alireza Olamaمشاهده پروفایل

پژوهشگر

• Parallel Computing
• Distributed Computing
• Machine Learning
• +۳ مورد دیگر

Alireza Olama is a Postdoctoral Researcher in the Department of Information Technology at Åbo Akademi University's Faculty of Science and Engineering. His work bridges parallel and distributed computing with machine learning and numerical optimization, contributing to the UN Sustainable Development Goals through algorithmic advancements. Doctoral research: Distributed framework for sparse convex optimization (2019, Universidade Federal de Santa Catarina) Master's thesis: Lyapunov Based Hybrid Model Predictive Control (2017) Research spans: Developing novel algorithms for distributed convex optimization (e.g., ADMM variants, Augmented Lagrangian methods) Creating software tools for sparse convex programming Applying optimization techniques to energy management systems Advancing GPU-accelerated machine learning frameworks Recent publications focus on ℓ0 sparsity, distributed consensus optimization, and hybrid control systems. Active in the academic community through conference presentations and collaborations with institutions like Norwegian University of Science and Technology (NTNU) and KTH Royal Institute of Technology.

• Automation
• Robotics
• Energy-Efficient Technologies
• +۵ مورد دیگر

Aníbal T. de Almeida is a Full Professor at the University of Coimbra, Portugal, and Director of the Institute for Systems and Robotics (UC), an interdisciplinary research institute with over 150 researchers. His work spans automation, robotics, and energy-efficient technologies, particularly focusing on advanced electric motors and drives. PhD in Electrical Engineering from Imperial College, University of London Over 50 funded national and international projects in industrial automation and energy efficiency General Chair of IEEE EPQU 2011 and IEEE IROS 2012 (the largest robotics conference globally) His research interests include: Smart microgrid architecture and resiliency Life cycle assessment of electric motor technologies Decarbonization strategies for urban and industrial sectors Recyclable and repairable electronic components Wildfire risk mitigation through robotic systems Sustainable energy access in developing countries Recent publications demonstrate expertise in voltage unbalance analysis, multi-criteria optimization for decarbonization, and 3R (resilient/reparable/recyclable) battery design. Awards include the 2015 IEEE CEMRA Award for educational materials on energy harvesting in mobile robots, IEEE IROS Fellow since 2020, and IEEE Robotics and Automation Society Distinguished Lecturer since 2018. As an international consultant, he advises the European Commission, US Department of Energy, World Bank, UNDP, and CLASP, while serving on the Board of Directors of CLASP (Washington, USA) since 2014. His work bridges cutting-edge robotics with sustainable energy systems, influencing global policy and technological standards.

Ning Wangمشاهده پروفایل

پژوهشگر

• Control Strategy Engineering
• Parameter Design Engineering
• DC Microgrid Engineering
• +۴ مورد دیگر

Ning Wang is a Postdoctoral Researcher at Aalborg University's Faculty of Engineering and Science, working within the Applied Power Electronic Systems department at AAU Energy. Wang serves as Principal Investigator for the PhD project 'Advanced optimized and control strategy of DCDC converter in Renewable DC microgrid' (2021-2024) under the supervision of Chen Z. Wang's research focuses on advanced control strategies for power electronic systems, particularly in DC microgrid applications. Key research areas include parameter design methods for DAB converters, DC-bias current suppression strategies, and operation schemes for retired batteries in secondary usage scenarios. The research employs artificial intelligence techniques including ANN and PSO optimization to address challenges in power converter design and control. Analysis of Wang's recent publications reveals a strong emphasis on practical applications of power electronics in renewable energy systems, with particular attention to DC microgrids, electric vehicles, and power semiconductor device monitoring. The work demonstrates integration of machine learning approaches with traditional power electronics design to solve complex engineering problems in renewable energy integration. As Principal Investigator on a significant PhD project related to DC-DC converters in renewable DC microgrids, Wang directs research activities in this specialized domain, collaborating with multiple researchers including Wang Y., Jiang Y., and Chen Z. The project addresses optimization strategies and control methodologies for power electronic systems in renewable energy applications.

Yifan Zhouمشاهده پروفایل

استادیار

• AI-Driven Smart Grid
• Quantum Computing
• Power System Dynamics and Stability
• +۴ مورد دیگر

Yifan Zhou is an Assistant Professor in the Department of Electrical and Computer Engineering at Stony Brook University. He holds a PhD (2019) and B.E. (2014) in Electrical Engineering from Tsinghua University. His research focuses on AI-driven smart grids, quantum computing, and formal analysis of power systems, emphasizing resilience and renewable energy integration. He teaches courses on electromechanical energy converters, electric machinery, and AI-driven smart grids. He advises PhD students in power system dynamics and quantum computing applications. Education: Ph.D. in Electrical Engineering, Tsinghua University, 2019 B.E. in Electrical Engineering, Tsinghua University, 2014 Research Interests: Integrates machine learning and quantum computing for power system stability, microgrid operations, and verifiable smart grids. Highlights include quantum ML for stability assessment and neural dynamic equivalence modeling. Awards: 2023 NAI Young Academic Inventor’s Award (for quantum computing in power systems) 2021 Outstanding Reviewer for IEEE Transactions on Power Systems 2014 Tsinghua University Outstanding Thesis Award Grants & Mentoring: Supervises PhD students (e.g., Xuguo Fu, Sijia Yu) and leads projects on quantum-enabled power analytics and learning-based verification. Advises on federal grants for smart grid resilience. Labs/Teams: Leads research in quantum power flow analysis and AI-driven control systems within the Electrical and Computer Engineering department.

Marco Jungمشاهده پروفایل

استاد

• Power Electronics
• Electromobility
• Electrical Infrastructure
• +۹ مورد دیگر

Prof. Dr. Marco Jung is a Professor at the Bonn-Rhein-Sieg University of Applied Sciences , affiliated with the School of Engineering and Communication and the Institute for Technology, Resource Conservation and Energy Efficiency (TREE) . He leads the Power Electronics and Power Systems Laboratory (PEPS-Lab) and serves as Director of the International Center for Sustainable Development , while also heading the Power Converters and Electric Drives Department at Fraunhofer IEE. Research Interests: His work focuses on Bidirectional battery chargers (AC, inductive, DC) SiC/GaN semiconductors for power electronics Grid-forming power converters for hydrogen electrolysis and microgrids Power Hardware-in-the-Loop testing Smart grid integration of renewable energy Projects: He manages key initiatives like F-HIL RELOADED (grid-forming converters), GREATER (Rwanda energy transition), HyLeiT (electrolysis converters), and Curriculum 4.0.nrw (digital teaching). His IEEE leadership as German Chapter Chair (IAS/PELS/IES) underscores his industry influence.

• Control Systems
• Electrification of Railway Systems
• Thermoelectricity
• +۷ مورد دیگر

Ramón Rodríguez Pecharromán is an Associate Professor in the Department of Electronics, Automation, and Communications at the Higher Technical School of Engineering of Comillas Pontifical University. He has been affiliated with the university since 1992 and is also a researcher at the Technological Research Institute (IIT). He served as the Director of his department from 2012 to 2021. Research Interests: His primary research areas include Control Systems, Electrification of Railway Systems, and Thermoelectricity. His work focuses on enhancing energy efficiency in DC-electrified mass transit systems through advanced modeling, optimization algorithms, and energy storage integration. He investigates regenerative braking, demand charge reduction, and smart traffic simulation to improve railway electrical infrastructure. Publication Trends: His recent publications emphasize energy storage systems in railways, optimization using nature-inspired algorithms (e.g., coral reefs, swarm intelligence), and simulation-based assessment of reversible substations. There is a strong focus on sustainable urban transit, economic impact analysis, and high-impact applications in real-world metro systems. Scientific Awards: K. F. Alcock RIA Memorial Prize, Institution of Mechanical Engineers, August 2013 Distinción Honorífica a la mejor Tesis Doctoral, Universidad Pontificia Comillas, January 2021 Premio Cátedra de Industria Conectada al mejor Proyecto Fin de Carrera, April 2023 Advising and Grants: He has supervised PhD students, including D. Roch Dupré and A.J. López López. His research has been funded by major institutions such as ADIF, Metro de Madrid, the Ministry of Science and Innovation, and the European Regional Development Fund. Projects include energy management in rail systems, smart microgrids, and regenerative energy utilization for electric vehicle charging. Labs and Teams: He is a key member of the railway systems research group at IIT, contributing to applied projects in railway electrification and control. His team collaborates with national and international transportation authorities and industry partners to implement energy-efficient solutions.