جست‌وجوی استاد و پژوهشگر

• Multi-Objective Optimization
• Control Systems
• Robotics
• +۵ مورد دیگر

Ying Qu is a Research Assistant at the Institute of Mechanical and Electrical Engineering , University of Southern Denmark , affiliated with the Faculty of Engineering . Their work bridges robotics, control systems, and multi-objective optimization (MOO) in industrial and marine contexts. Research Focus: Multi-objective optimization, underwater robotics, digital twin frameworks, and sustainable agricultural technology. Key Projects: Development of an innovative propeller polishing robot for the shipping industry, focusing on fuel savings and emission reductions. Ying Qu's publications emphasize MOO applications in robotics (e.g., underwater end-effector control), industrial systems (greenhouse climate management), and power electronics. Their work integrates machine learning, evolutionary algorithms, and simulation techniques to address complex engineering challenges.

Melika Sadat Taslimiمشاهده پروفایل

پژوهشگر ارشد

• Power Engineering
• Chemical Engineering
• Renewable Energy Systems
• +۳ مورد دیگر

Melika Sadat Taslimi is a Salaried Ph.D. Research Fellow at the SDU Mechatronics (CIM) within the Institute of Mechanical and Electrical Engineering at the University of Southern Denmark. Her research focuses on power-to-methanol systems, renewable energy optimization, and carbon dioxide conversion technologies. Institute of Mechanical and Electrical Engineering, University of Southern Denmark SDU Mechatronics (CIM) Her work addresses critical aspects of energy efficiency, hydrogen production, and technological advancements in sustainable energy systems through analytical modeling, case studies, and collaborative research projects. Recent publications highlight her expertise in integrating solar/wind energy with storage solutions, optimizing off-grid systems, and advancing Power-to-X technologies. Collaborations span Denmark and Australia, focusing on green hydrogen value chains and sector coupling. As a Research Fellow, she contributes to interdisciplinary projects involving dynamic simulation, mathematical optimization, and techno-economic analysis of energy systems across diverse applications including data centers, industrial plants, and portable containerized units.

Peter Axel Nielsenمشاهده پروفایل

استاد

• Computer Science
• IT Project Management
• Software Development
• +۵ مورد دیگر

Peter Axel Nielsen is a Professor and Head of the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. His research spans computer science, IT project management, software development, and big data analytics, with a focus on agile methodologies, environmental assessment, and sustainable development goals. Institutional Affiliation: Aalborg University, Denmark Research Interests: Nielsen's work addresses the intersection of technology and sustainability. Key areas include risk management in agile software teams, big data analytics for societal benefits, and digital transformation in energy systems like district heating. Recent projects explore UAV routing, predictive maintenance for offshore wind farms, and AI integration in multimodal service systems. Trends in Publications: His 2024–2025 articles emphasize Digital transformation for sustainability UAV operations and routing algorithms Big data analytics in healthcare and energy Ageism in labor markets Maintenance optimization for renewable energy Scientific Awards: Best Paper Award at SCIS 2021 (Scandinavian Conference on Information Systems) Collaboration & Projects: Active in interdisciplinary collaborations, Nielsen leads initiatives like DREAMS (digitally supported environmental assessment) and SB3D (security by design in Digital Denmark). He has been a principal investigator in over 26 projects since 1995, including district heating research and digital educational material development.

Jan Martin Zepterمشاهده پروفایل

پژوهشگر ارشد

• Distributed Energy Resources
• Electric Vehicles
• Battery Energy Storage
• +۶ مورد دیگر

Jan Martin Zepter is a Postdoctoral Researcher at DTU's Department of Wind and Energy Systems, specializing in distributed energy resource modeling with emphasis on electric vehicle-grid integration and renewable energy systems. His work directly supports European decarbonization initiatives through projects like EV4EU and INSULAE. His academic journey includes: Ph.D. in Wind and Energy Systems, Technical University of Denmark (2019-2023) M.Sc. in Sustainable Energy Systems, Norwegian University of Science and Technology (2016-2019) Dual M.Sc. in Industrial Engineering (Electrical Engineering focus), Technical University of Berlin (2016-2019) B.Sc. in Industrial Engineering, Technical University of Berlin (2013-2017) Dr. Zepter's research bridges technical and economic domains, developing advanced models for virtual power plants , degradation-aware battery operation , and local flexibility markets . His fingerprint reveals dominant expertise in Electric Vehicle Engineering (100%), Energy Engineering (52%), and Battery Systems (46%), with growing contributions to DC Microgrids and Photovoltaics integration. He pioneers methodologies combining receding horizon optimization with real-world market constraints to enable high renewable penetration. His 15 most recent publications (2024-2025) demonstrate three critical trends: (1) Techno-economic analysis of PV-powered EV charging infrastructure, (2) Degradation-cost integration in battery market participation, and (3) Local flexibility market designs for distribution networks. These works establish novel frameworks for consumer-centric energy markets while addressing grid stability challenges through distributed resources. As an active supervisor, Dr. Zepter guides four PhD candidates—D. M. Santos (degradation-aware V2G), M. Ledro (battery-hybrid systems), A. Malkova (EV-powered plants), and X. Cao (EV management)—across European projects with combined funding exceeding €15M. His research directly contributes to UN Sustainable Development Goals 7 (Affordable Clean Energy) and 13 (Climate Action) through practical solutions for island energy systems and transport electrification. Based at DTU's Wind and Energy Systems department, he collaborates with industry partners on real-world demonstrations including Bornholm Island's virtual power plant and Madeira's replication initiatives, focusing on scalable solutions for renewable integration in constrained grids.

• Statistics
• Applied Statistics
• Precision Agriculture
• +۳ مورد دیگر

Signe Marie Jensen is an Associate Professor in the Department of Plant and Environmental Sciences at the University of Copenhagen's Faculty of Science. Her research bridges statistical methodology with agricultural applications, focusing on precision agriculture and plant phenotyping. She leads innovative work applying computer vision and machine learning to solve critical agricultural challenges. Education: MSc in Mathematical Economics, University of Copenhagen (2007) PhD in Applied Statistics, University of Copenhagen (2015) Dr. Jensen's research interests center on statistical methods for simultaneous inference and analysis of phenotyping data. Her work has increasingly focused on applying advanced machine learning techniques, particularly YOLO algorithms, to agricultural challenges including pest detection, weed management, and plant phenotyping. She develops statistical frameworks that enable more accurate and efficient analysis of complex agricultural data. Her recent publications reveal a strong trend toward interdisciplinary research combining statistics, computer vision, and plant science. The majority of her 2024-2025 publications focus on applying YOLO-based object detection systems to agricultural problems, particularly in precision farming applications. These works demonstrate her ability to translate statistical theory into practical agricultural technologies that address food security and sustainable farming challenges. Dr. Jensen's research output shows strong international collaboration, with co-authors from multiple countries working on agricultural technology development. Her work on the PhenoLab platform represents a significant contribution to high-throughput plant phenotyping infrastructure. She maintains active research in both theoretical statistics (simultaneous inference methods) and applied agricultural technology development, demonstrating remarkable versatility across these domains. Her current work on deep learning applications for crop monitoring represents the cutting edge of precision agriculture research.

• Hydrogeology
• Groundwater Modeling
• Environmental Hydrology
• +۳ مورد دیگر

Torben Obel Sonnenborg serves as an Adjunct Professor in the Department of Geosciences and Natural Resource Management (Geology section) at the University of Copenhagen, with concurrent affiliation at the Geological Survey of Denmark and Greenland (GEUS). Contact details include phone +4591333635 and emails tso@ign.ku.dk , tso@geus.dk , located at Øster Voldgade 10, 1350 København K. His research expertise spans: Hydrogeology Groundwater Modeling Environmental Hydrology Water Resources Management Geological Modeling Urban Hydrology Specializing in numerical modeling and uncertainty analysis, his work integrates machine learning with hydrostratigraphic data to address urban groundwater challenges and agricultural contamination risks. Analysis of his 2022-2024 publications reveals a methodological emphasis on quantifying interpretation uncertainties in 3D geological models, developing data-driven approaches for urban groundwater prediction, and assessing climate impacts on coastal aquifers. His collaborative work frequently appears in leading hydrology journals including Hydrology and Earth System Sciences and Water Resources Research , demonstrating cross-disciplinary integration of field measurements with simulation frameworks. No scientific awards or student mentoring activities are documented in available sources. Research funding details remain unspecified despite active project involvement evidenced by recent field-based studies. He contributes to integrated research teams at the University of Copenhagen and GEUS, focusing on Denmark's groundwater sustainability challenges including saltwater intrusion mapping, urban hydrogeology, and land-atmosphere interaction modeling through initiatives like cosmic-ray neutron soil moisture monitoring.

Shuting Liمشاهده پروفایل

پژوهشگر

• Microgrids
• Energy Engineering
• Virtual Synchronous Generators
• +۵ مورد دیگر

Dr. Shuting Li is a Postdoctoral Researcher at Aalborg University's Faculty of Engineering and Science within the Applied Power Electronic Systems group. Her research focuses on advanced control strategies for renewable energy integration, particularly in microgrid applications and virtual synchronous generator technologies. Her research interests span Microgrids , Virtual Synchronous Generators , Harmonics Control , and Wind Power Forecasting . She develops innovative control architectures addressing harmonic mitigation, stability enhancement, and energy management in systems with high renewable penetration. Her work bridges theoretical control design with practical implementation challenges in modern power systems. Dr. Li's publication record demonstrates consistent output with 15 research items between 2020-2024, including 7 publications in 2024 alone. Her work shows strong focus on harmonic control ( 46% fingerprint match ), virtual synchronous generators ( 46% ), and energy management systems ( 46% ), with significant contributions to wind power integration ( 30% ) and regenerative braking applications ( 30% ). She has secured research funding through the Harmonic Control Architectures for Virtual Synchronous Generator-based Distributed Generation Systems project (2021-2024), supervised by Professors Guerrero and Vasquez. This PhD project established her expertise in harmonic mitigation for distributed generation systems. Her laboratory work centers on the Applied Power Electronic Systems facility at Pontoppidanstræde 111, where she develops and tests control algorithms for microgrid applications using advanced simulation and hardware-in-loop platforms.

Irfan Ahmed Halepotoمشاهده پروفایل

پژوهشگر

• Electric Vehicle Engineering
• Autonomous Vehicles
• Optimization
• +۳ مورد دیگر

Irfan Ahmed Halepoto serves as a Guest Researcher at Aalborg University's Faculty of Engineering and Science within the Department of Power Electronics System Integration and Materials, Denmark. His work focuses on advancing electric vehicle (EV) technology through energy consumption modeling and drive range optimization. Research interests center on Electric Vehicle Engineering , Autonomous Systems , and Eco-driving Optimization . He employs black-box modeling techniques and AI-driven predictive frameworks to integrate real-time parameters like traffic patterns and weather data, developing novel strategies for energy-efficient route planning in EVs. His fingerprint analysis reveals dominant expertise in Key Parameters Engineering (100%), Black Box methodologies (100%), and Driving Route optimization (100%). Recent publications demonstrate a clear trend toward multi-objective optimization of EV performance using NSGA-II algorithms and real-time data integration. His research bridges theoretical modeling with practical applications in sustainable transportation, specifically targeting traffic avoidance systems and dynamic range extension for electric vehicles through eco-driving strategies. Based at Pontoppidanstraede 111, Aalborg East, Halepoto operates within AAU's Power Electronics research ecosystem, contributing to cutting-edge system integration projects focused on sustainable energy solutions for next-generation electric mobility.

Panagiotis Karrasمشاهده پروفایل

استاد

• Data Mining
• Database Systems
• Graph Data Management
• +۷ مورد دیگر

Panagiotis Karras is a Professor of Computer Science at the University of Copenhagen and a faculty affiliate at Aarhus University. His research centers on robust and versatile methods for data access, mining, and representation. He has held academic positions at Aalborg University, Skoltech, Rutgers, NUS, the University of Zurich, and DTU, reflecting a broad international academic footprint. His research interests lie at the intersection of database systems, data mining, and machine learning, with a focus on graph data management, data privacy, indexing, influence maximization, and clustering. He aims to develop scalable and efficient algorithms that maintain high utility under privacy and accuracy constraints. His recent publications reveal a strong trend in graph-based methods, including graph alignment (GRASP, FREDE), influence and diffusion modeling, data anonymization, and adaptive indexing. These works appear in premier venues such as VLDB, SIGMOD, KDD, ICDE, and AAAI, indicating sustained high-impact contributions. He has been recognized with the 2008 Young Scientist Award in Physical/Mathematical Science from HKIS. Karras is actively seeking motivated PhD students and has supervised numerous collaborative projects. While specific grant details are not listed, his continuous publication output and affiliations suggest active research funding. His work often involves interdisciplinary collaboration, particularly in graph analysis and privacy-preserving technologies. He has contributed significantly to the development of efficient data synopses, anonymization frameworks (e.g., SABRE, L-opacity), and scalable graph processing systems, often in collaboration with leading researchers in the database and data mining communities.

Rahul Misraمشاهده پروفایل

پژوهشگر ارشد

• Control Systems
• Decentralized Control
• Water Distribution Networks
• +۳ مورد دیگر

Rahul Misra is a Postdoctoral Researcher at the Department of Electronic Systems within The Technical Faculty of IT and Design at Aalborg University, Denmark. He is affiliated with the Learning and Decisions Lab and holds an ORCID identifier (0000-0003-2540-0206). He completed his PhD in 2024 with a thesis on "Decentralized Control of Complex Systems: Managing Uncertainties and Multi-objective Optimization with Multiple Controllers". His research spans control theory applications in critical infrastructure, with emphasis on decentralized control systems for water distribution and electric power networks. He integrates reinforcement learning and game theory to address challenges in system safety, multi-objective optimization, and uncertainty management, contributing to control frameworks for leakage diagnosis and contamination mitigation. Recent publications reveal a strong trend toward applying reinforcement learning and Markov Decision Processes to safety-critical infrastructure systems, with growing focus on theoretical foundations of safe control. His interdisciplinary work bridges control engineering, computer science, and civil engineering, particularly in smart grid and water network applications. His accolades include: EliteForsk Travel Grant 2022 (awarded by the Danish Ministry of Higher Education and Science) Dr. Misra has led research projects including SWIFT (2019-2024), focusing on decentralized control for water and electric power distribution systems. He maintains active collaborations with international researchers, evidenced by co-authorships across multiple publications and institutional partnerships. He is a core member of Aalborg University's Learning and Decisions Lab, which specializes in the intersection of learning theory, decision-making algorithms, and control systems for critical infrastructure resilience.

Nicolaj Søndberg-Jeppesenمشاهده پروفایل

مدرس پاره‌وقت

• Bayesian Networks
• Cyber-Physical Systems
• Healthcare Informatics
• +۴ مورد دیگر

Nicolaj Søndberg-Jeppesen is a Part-Time Lecturer at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on Bayesian networks, cyber-physical systems, and their applications in healthcare, industrial diagnostics, and decision support systems. Key research interests include: Development of Bayesian network models for predictive maintenance and health monitoring in industrial systems Integration of operational data into continuous improvement frameworks Application of probabilistic graphical models in healthcare logistics and emergency management Design of smart manufacturing architectures for diagnostics and predictive maintenance Notable projects include: Animated Computer Games (ongoing since 2010) Extending the scope of graphical models for decision problems (ongoing since 2010) His recent work emphasizes: Patient flow prediction in emergency departments Risk assessment in home care deliveries Object-oriented Bayesian networks for component-level diagnostics Publications span applications in healthcare informatics, industrial engineering, and game theory, with a focus on practical implementations of probabilistic models.

Jan-Otto Hooghoudtمشاهده پروفایل

مدرس پاره‌وقت

• Mathematical Statistics
• Bayesian Inference
• Optimization Algorithms
• +۴ مورد دیگر

Dr. Jan-Otto Hooghoudt is a Part-Time Lecturer in Mathematical Statistics at Aalborg University's Department of Mathematical Sciences, Faculty of Engineering and Science. His academic profile integrates statistical methodologies with industrial applications. Current affiliation: Aalborg University Email: janotto@math.aau.dk Research Focus: Combining mathematical statistics with engineering challenges, his work spans: Bayesian inference for protein spatial distribution analysis Evolutionary algorithms in oil & gas separation optimization Remaining useful life prediction models for industrial machinery Structural health monitoring of wind turbines Operational dynamics in data preparation systems Publication Trends: Recent works emphasize predictive maintenance (rolling element bearings, wind turbines), computational modeling, and interdisciplinary applications of statistics in industrial systems. Key collaborations include EU Mantis project contributions. Technical Expertise: Specializes in simulation models, condition monitoring, and Förster resonance energy transfer data analysis.

Ahmad Arabkoohsarمشاهده پروفایل

استاد

• Energy Engineering
• Thermal Energy Storage
• Renewable Energy Technologies
• +۳ مورد دیگر

Professor Ahmad Arabkoohsar is a leading academic in Energy Engineering and Thermal Systems at the Technical University of Denmark (DTU), specifically within the Department of Civil and Mechanical Engineering. His research focuses on sustainable energy technologies, including thermal energy storage, waste heat recovery, and thermochemical systems. He leads major projects such as COPILOT (enhancing deep-tech innovation in sustainable energy) and STOREDGE (long-duration thermochemical heat storage), aiming to advance renewable energy integration and decarbonization. His work contributes to UN SDGs, particularly SDG 7 (Affordable and Clean Energy). Recent projects emphasize optimizing multi-carrier energy systems, solar-thermochemical storage, and waste heat utilization. He has supervised students like Z. Sefidgar Shahkolaei and holds editorial awards for academic contributions. His research spans topics like heat pumps, energy hubs, and lifecycle assessments of advanced thermal technologies. Key achievements include over 94 publications and 24 active projects. Awards include 'Editor of Distinction' accolades in 2025. His labs and teams focus on cutting-edge innovations in energy systems, with collaborations across Europe and beyond.

Yu Jiangمشاهده پروفایل

دانشیار

• Transportation Science
• Transit Assignment
• Traffic Assignment
• +۱۳ مورد دیگر

Yu Jiang is an Associate Professor in the Department of Transportation Systems Modelling at the Technical University of Denmark (DTU), within the Department of Technology, Management and Economics. He is actively engaged in research and supervision, with a strong focus on transportation science and systems optimization. He is accepting PhD students and maintains an active research profile with numerous ongoing projects and publications. Education: PhD, The University of Hong Kong (2010–2014) His research interests include static and dynamic transit/traffic assignment, network design, infrastructure resilience and vulnerability, multi-objective optimization, and metaheuristics. These areas are central to improving urban mobility, public transport efficiency, and sustainable transportation systems. His work contributes to UN Sustainable Development Goals related to sustainable cities and responsible consumption. Recent publications (2025) reflect a strong trend in applying advanced optimization and machine learning techniques to urban transportation challenges, including traffic signal control, last-mile delivery, intermodal travel behaviour, and demand prediction across shared mobility systems. The research spans both theoretical modelling and practical case studies. Scientific Awards: No scientific awards listed in the provided text. Advising and Grants: Yu Jiang is the main supervisor for several PhD projects, including 'Development of an integrated optimisation model for operating urban transit system (SmoothTrip)', 'Crowdsourced Delivery as an Activity for Sustainable Cities', and 'Network Optimisation for Connected, Cooperative and Automated Mobility'. He is also a co-investigator in the NEMESYS project funded by the European Commission, focusing on dynamic pricing and tradable credits for emissions management. His academic activities include organizing conferences, presenting at international events, serving as a journal editor, and conducting peer reviews. Labs and Research Teams: He is part of the research network at DTU's Department of Technology, Management and Economics, collaborating on projects related to urban transit optimization, shared mobility, and sustainable freight. His work involves close collaboration with researchers from institutions such as Oxford University, NUS, and HKU, as well as international partners in ongoing research initiatives.

• Astrophysics
• Cosmology
• Gamma-ray bursts
• +۵ مورد دیگر

Joonas Kari Markku Viuho is a Research Fellow at the University of Copenhagen , affiliated with the Niels Bohr Institute and specifically the Cosmic Dawn Center (DAWN) . His research is centered on astrophysics, observational cosmology, and high-energy transient phenomena. University: University of Copenhagen School: Niels Bohr Institute Department: Cosmic Dawn Center (DAWN) Email: joonas.viuho@nbi.ku.dk His research interests include: Gamma-ray bursts and their environmental contexts Lyman-alpha absorber systems Near-infrared airglow modeling Multi-wavelength transient detection His publications over the last five years reveal a strong focus on: Gamma-ray burst follow-up observations Instrumentation for infrared spectroscopy Blazar flaring activity in gamma-ray sources Astrophysical false positives (e.g., dormant black hole misidentifications) He actively collaborates through networks in: Multi-messenger astronomy International GRB observation campaigns Cosmic Dawn-related research