جست‌وجوی استاد و پژوهشگر

• Policy and Administration
• Applied Economics
• Mechanical Engineering
• +۳ مورد دیگر

Professor Mark Howells is a Principal Research Fellow at the Centre for Environmental Policy within Imperial College London's Faculty of Natural Sciences. His work focuses on energy systems modelling and policy analysis for sustainable development in low and middle-income countries, with a particular emphasis on open-source frameworks and context-specific transition pathways. His research spans energy transition modelling, environmental policy, and sustainable development, utilizing tools like OSeMOSYS and CLEWs to address energy trilemma challenges across Africa, Asia, and South America. Key interests include integrating qualitative factors into quantitative models, green hydrogen planning, power sector reliability, and resource nexus management, with significant contributions to country-specific energy planning in Ghana, Nigeria, Kenya, Egypt, and Indonesia. Analysis of his 2023-2025 publications reveals a strong focus on open-source energy modelling applications for decarbonization, with recurring themes of data kit development, grid flexibility solutions, and water-energy-land nexus assessments. His work consistently bridges technical modelling with policy implementation needs, particularly in contexts of infrastructure constraints and climate vulnerability. No scientific awards were mentioned in the source materials. Professor Howells operates within Imperial College London's Centre for Environmental Policy, an interdisciplinary hub conducting research on global environmental challenges through integrated systems approaches that connect technical, economic, and policy dimensions of sustainability transitions.

Jann Weinandمشاهده پروفایل

استاد پژوهشی

• Energy System Optimization
• Renewable Energy Resource Assessment
• Complexity Management
• +۴ مورد دیگر

Dr. Jann Michael Weinand is the head of the Integrated Scenarios department at the Institute of Climate and Energy Systems (ICE-2) within Forschungszentrum Jülich GmbH. He leads a team of 30 scientists, PhD students, and master students focusing on energy system analysis, complexity management, and AI integration. His work addresses regional and international energy systems, emphasizing renewable energy resource assessment and techno-economic feasibility. Dr. Weinand holds a Dr.-Ing. from the Karlsruhe Institute of Technology (2020) and a Mechanical Engineering and Business Administration degree from RWTH Aachen University (2016). His research spans energy autonomy, renewable resource optimization, and the socio-technical challenges of energy transitions. Key research areas include energy system modeling, geothermal and wind energy potential, and data-driven methodologies. He coordinates interdisciplinary projects with academic and industrial partners, contributing to high-impact journals like Nature Energy and Joule. His team develops open-source tools (e.g., ETHOS workflows) for reproducible energy assessments and advocates for spatially disaggregated energy planning. Publications highlight trade-offs in energy system design, AI risks, and land-use conflicts for renewables. He emphasizes integrating social, technical, and environmental factors into energy policy frameworks.

Hamed Nabizadeh Rafsanjaniمشاهده پروفایل

مدرس

• IoT
• AI in Architecture, Engineering, and Construction (AEC)
• Digital Twin Technology
• +۵ مورد دیگر

Hamed Nabizadeh Rafsanjani, Ph.D., P.E., ENV SP is a Lecturer at the University of Georgia (UGA) College of Engineering since 2018. He holds a Ph.D. and master’s degrees in Construction Engineering and Management from the University of Nebraska-Lincoln (UNL). Prior to UGA, he served as an assistant professor and visiting lecturer at various universities, earning accolades such as the Best Teacher Award and Best Researcher Award. His research focuses on IoT, AI, and Digital Twin technologies applied to Architecture, Engineering, and Construction (AEC) industries. As director of iSC-LAB, he develops IoT-based platforms to analyze building occupants’ energy-use behaviors and improve learning environments. His research interests span smart building systems, non-intrusive energy monitoring, and occupant behavior analysis. Key projects include a global occupant behavior database and an IoT-based smartphone energy assistant (iSEA). He has secured grants from agencies like the National Science Foundation (NSF), contributing to studies on energy efficiency, construction project management, and sustainability in built environments. Rafsanjani’s publications (2018–2023) emphasize AI-driven solutions for AEC, IoT applications in energy management, and adaptive learning systems. His work bridges technology and human behavior to optimize building performance and educational outcomes. Awards reflect his dual excellence in pedagogy and research, with ongoing contributions to the Sustainable Human-Building Ecosystems field.

• Signal and Information Processing
• Energy Disaggregation
• Non-Intrusive Load Monitoring (NILM)
• +۱۵ مورد دیگر

Professor Vladimir Stankovic is a leading academic in the Department of Electronic and Electrical Engineering within the Faculty of Engineering at the University of Strathclyde, UK. His research focuses on signal and information processing to tackle global sustainability challenges such as energy efficiency, net-zero transition, electrification of transport, and landslide monitoring. He is actively involved in multiple high-impact research initiatives and holds leadership roles in major AI and engineering conferences. PhD (Dr-Ing), University of Leipzig, Germany, 2003 MEng (Dipl-Ing), Electrical Engineering, University of Belgrade, 2000 His research interests center on applying signal processing and AI to smart homes, micro-seismic analysis, health monitoring, remote sensing, and computer vision. He has developed innovative methods in energy disaggregation, graph signal processing, and interpretable machine learning. His work bridges engineering with sustainability, contributing to UN Sustainable Development Goals. He teaches core courses including image/video processing, communication systems, signals and systems, and information theory. The recent publications highlight a strong trend in applying AI and machine learning to energy analytics in buildings and agriculture (e.g., dairy farms), landslide monitoring using seismic data, and equitable EV charging planning. These works leverage advanced techniques such as graph neural networks, transformers, and human-in-the-loop deep learning, demonstrating a consistent focus on real-world impact, interpretability, and sustainability. He has received notable scientific awards: Finalist of the World first 10K Best paper Award (Top 3%) IEEE Multimedia Signal Processing Workshop (MMSP) top 10% best paper award 2010 Professor Stankovic has been a Principal or Co-Investigator on numerous research and knowledge exchange projects, including JED-AIs (Justice, Energy, Demand flexibility and AI for Sustainability), REMINDA, and multiple Digital Dairy Chain Innovation Vouchers. These projects are funded by EPSRC and other UKRI councils, focusing on AI-driven energy analytics, community energy sharing, and sustainable farming. He serves in key editorial roles, including Area Editor for IEEE Transactions on Communications and Associate Editor for Nature Scientific Data, and has organized major conferences such as IEEE MMSP 2017 and IJCNN 2025. He is actively involved in research teams and collaborative networks focused on AI for sustainability, energy systems, and environmental monitoring. His lab works on datasets such as the Plegma Dataset and REFIT, supporting open science in energy and seismic analytics.

Wim Casteelsمشاهده پروفایل

مدرس

• Data Science
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Wim Casteels is a Lecturer and researcher at AP University College, specializing in AI and data science. He transitioned from quantum physics research at the University of Antwerp and Paris to roles in data science at Argenta's Data Analytics Office and imec's IDLab research group. His work focuses on applying AI to education, transportation, and environmental monitoring. He leads projects like AI4UX (user pattern detection), LAP! (learning analytics dashboards), and AI4Care (AI for youth care support). Education: PhD in Physics (University of Antwerp), followed by postdoctoral research on quantum systems in Antwerp and Paris. Transitioned to data science roles in industry and academia. Research Interests: Machine Learning, Deep Learning, Learning Analytics, and AI applications in education and transportation. Projects include developing AI-driven road weather models using vehicle sensor data, predictive student success models, and energy-efficient building control systems. His work emphasizes ethical AI implementation and data-driven decision-making. Key Projects: AI4UX (2023–2025), AHUMAIN (2023–2025), LAP! (2019–2023), and AI4Care (2024–2026). His research is supported by grants like the Industrial Research Fund. Labs/Teams: Part of AP University College's Media, Design and IT Knowledge Center, previously affiliated with imec's IDLab group and Argenta's Data Analytics Office.

Danny Harveyمشاهده پروفایل

استاد

• Climate Science
• Energy Resources
• Sustainable Design
• +۴ مورد دیگر

Danny Harvey is a Full Professor in the Department of Geography and Planning at the University of Toronto, where he teaches courses on climate science, global warming, and energy systems. He holds a PhD in Climate Modelling from the University of Toronto (1985) and has conducted research at institutions including the National Center for Atmospheric Research (USA) and Laboratoire de Météorologie Dynamique (France). His research focuses on decarbonizing energy systems, with emphasis on: Energy efficiency in buildings and district energy systems Climate change mitigation pathways for transportation and industry Integration of renewable energy sources Global climate and carbon cycle modeling His recent publications demonstrate strong interdisciplinary work spanning energy policy, building technology, and climate economics, with consistent focus on practical solutions for carbon neutrality by mid-century. Professor Harvey supervises graduate students researching energy use in buildings and renewable energy integration. He serves as Deputy Editor for Climatic Change and has contributed to IPCC reports as lead author for buildings-related climate mitigation strategies. He advises Environmental Defense and Scope Four Capital on climate solutions.

• Building Stock Analysis
• Energy Consumption in Non-domestic Buildings
• Geospatial Modeling
• +۵ مورد دیگر

Rob Liddiard is a Research Associate at University College London's Bartlett School Env, Energy & Resources. His work focuses on identifying and classifying energy use patterns in non-domestic buildings through the 3DStock model , a geospatial representation of the UK building stock. He contributes to dynamic energy models like SimStock and collaborates with organizations such as the Valuation Office Agency (VOA) and Ordnance Survey AddressBase Premium.

Ursula Eickerمشاهده پروفایل

استاد

• Urban energy systems
• 3D City modeling
• Urban data and simulation platform development
• +۷ مورد دیگر

Prof. Ursula Eicker is the Canada Excellence Research Chair in Smart, Sustainable and Resilient Cities and Communities at Concordia University , leading cutting-edge research in urban energy systems. Her work integrates 3D city modeling, renewable energy systems, and sustainable transport to develop zero-carbon city strategies. PhD in Solid State Physics (Heriot-Watt University) Habilitation in Renewable Energy Systems (Technische Universitat Berlin) Research Interests focus on urban simulation platforms, district energy networks, and climate-resilient infrastructure. The INSEL4Cities platform enables holistic urban modeling for building demands, transportation, and greenery. Her Residential Densification studies demonstrate 65% energy reduction through retrofits and solar integration. Recent publications explore urban solar shading , transactive energy systems , green infrastructure equity , and decentralized hydrogen production . Awards include the German-African Innovation Incentive and recognition for photovoltaic research in Egypt. Over 50 graduate students in her lab examine zero-carbon pathways. Teaches ENCS 691 on urban energy systems. Secured 10M CAD for the CERC chair and multiple grants. Co-Director of Concordia's Next Generation Cities Institute .

Jacques de Chalendarمشاهده پروفایل

استاد مدعو

• Energy Systems
• Climate Change Mitigation
• Optimization
• +۳ مورد دیگر

Jacques de Chalendar is an Adjunct Professor in the Energy Science & Engineering department at Stanford University. He is an Independent Research Consultant for TotalEnergies and affiliated with the Benson Lab. His research focuses on low-carbon energy systems, energy efficiency, and optimization of infrastructure through data-driven approaches. De Chalendar holds a PhD in Energy Resources Engineering from Stanford (2020), an MSc from Stanford (2016), and a BSc/MSc from École Polytechnique (2014). Key research areas include emissions tracking in power systems, flexible urban energy systems, and building energy flexibility experiments. His work emphasizes integrating mathematical modeling, software engineering, and real-world prototyping to reduce energy consumption and carbon footprints. Notable projects include the COOLER Research Program for commercial building efficiency and real-time grid emissions monitoring at energy.stanford.edu/gridemissions. De Chalendar teaches ENERGY 104/204, exploring global energy transitions through computational modeling. He collaborates across disciplines with engineers, mathematicians, and economists to address large-scale decarbonization challenges. His experiments demonstrate scalable solutions like thermal storage optimization and demand response mechanisms in multi-zone buildings.

• Modeling and Simulation
• Energy System Analysis
• Vehicle Grid Integration
• +۳ مورد دیگر

Prof. Dr.-Ing. Marco Pruckner leads the Chair of Communication Networks at the University of Würzburg since 2022, following his assistant professorship in Energy Informatics at Friedrich-Alexander-University Erlangen-Nürnberg (2016–2022). His research focuses on energy system analysis, vehicle grid integration, and future mobility systems using modeling and simulation methodologies. University of Würzburg (2022–present) Friedrich-Alexander-University Erlangen-Nürnberg (2016–2022) UC Berkeley (Visiting Scholar, 2020) Key research areas: Modeling and simulation of integrated energy systems Digital twin development for local energy systems Optimization of electric vehicle charging strategies Water-energy nexus analysis and sustainability metrics Recent publications (2022–2025) demonstrate expertise in: Smart charging algorithms for vehicle grid integration Heat pump electrification and seasonal performance modeling Battery state-of-health estimation techniques Multi-objective optimization for data centers and energy systems His team at University of Würzburg includes Daniel Bayer, Paul Benz, Jonas Schiller, and Leo Strobel. Methodologies combine system dynamics, discrete-event simulation, and machine learning for energy and mobility system analysis.

Nizar Bouguilaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Clustering
• +۷ مورد دیگر

Prof. Nizar Bouguila is a Professor at the Concordia Institute for Information Systems Engineering (CIISE), Concordia University, and holds the Concordia University Research Chair in Applied Artificial Intelligence. His research focuses on machine learning, data clustering, smart building systems, and energy management. He actively supervises graduate students in Computer Science, Electrical and Computer Engineering, and Information Systems Security programs. Dr. Bouguila's work integrates probabilistic models, deep learning, and domain adaptation techniques. He has pioneered methods in energy disaggregation, load forecasting, and anomaly detection in smart infrastructure. His recent projects address challenges in occupant behavior prediction, speech emotion recognition, and fake news detection using Arabic datasets. Key research themes include: Probabilistic clustering with Bayesian nonparametric mixtures Smart building analytics via IoT and sensor data Domain adaptation for cross-domain learning Explainable AI in energy systems His 2023-2025 publications emphasize: Advanced transformer networks for load forecasting Graph neural networks in transportation Multimodal data fusion for anomaly detection Topic modeling for short texts Dr. Bouguila's advising spans multiple engineering and computer science disciplines, reflecting his interdisciplinary research profile.

• Renewable Energy Integration
• Electrical Power Systems
• Energy Storage Technologies
• +۴ مورد دیگر

Murray Thomson is a Senior Lecturer in Electrical Networks and Systems at CREST (Centre for Renewable Energy Systems Technology), part of the School of Electronic, Electrical & Systems Engineering at Loughborough University. His research focuses on integrating renewable energy into power systems, including low-voltage distribution networks and flexible demand management for grid balancing in low-carbon systems. He holds a BSc and PhD in relevant fields. His work emphasizes high penetrations of wind, marine, and solar power, alongside energy storage solutions and smart grid technologies. Notable contributions include techno-economic analyses of offshore wind and solar systems, evaluations of energy storage for decarbonization, and studies on heat pumps and insulation in UK decarbonization efforts. Recent research highlights include investigating battolyser technology for hydrogen production, analyzing non-technical electricity losses, and exploring microgrid design for energy access in rural areas. His work intersects technical feasibility with policy implications, particularly in achieving net-zero targets. Dr. Thomson's publications span over two decades, addressing demand modeling, grid integration challenges, and the evolving role of smart meter data. Despite no listed awards, his contributions are recognized through extensive peer-reviewed outputs. He leads research at CREST, focusing on innovative solutions for sustainable energy systems without explicitly noted grants or advising roles.

Manar Amayriمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Science
• Explainable AI
• +۵ مورد دیگر

Dr. Manar Amayri is a researcher affiliated with the CIISE (College/Institution name not explicitly provided), focusing on machine learning, data science, and their applications in energy management systems. Her work emphasizes smart buildings, smart cities, and human-in-the-loop AI systems. She explores advanced clustering techniques, domain adaptation, and anomaly detection in smart environments. Research Interests: Machine Learning, Explainable AI, Energy Management Systems, Smart Buildings, Data Clustering, Domain Adaptation, and Anomaly Detection. Her studies address challenges in load forecasting, occupancy estimation, and energy disaggregation using deep learning, Bayesian methods, and probabilistic models. Publications highlight innovations in hybrid models (e.g., Transformer-BiLSTM networks), data imputation for electric vehicle charging, and unsupervised learning techniques for smart grid applications. Recent work emphasizes interpretability and robustness in AI systems. No scientific awards or grants were explicitly mentioned in the provided texts. No student advisees or lab affiliations were listed.

Ning Luمشاهده پروفایل

استاد

• Power Systems
• Renewable Energy Integration
• Smart Grids
• +۳ مورد دیگر

Ning Lu is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University (NC State). She holds an IEEE Fellow distinction and specializes in power systems, renewable integration, and smart grid resilience. Her research focuses on load modeling, energy storage systems, and climate impacts on grids. She has authored over 60 publications and led projects on grid modeling, energy storage, and renewable integration. Education: Ph.D., Electric Power Engineering, Rensselaer Polytechnic Institute (2002) Master's, Electric Power Engineering, Rensselaer Polytechnic Institute (1999) Bachelor's, Electrical Engineering, Harbin Institute of Technology (1993) Research Interests: Her work spans load modeling/control, wide-area energy storage, renewable integration, smart grid predictive defense models, and climate change impacts on power systems. She develops advanced machine learning methods for grid analysis (e.g., vision transformers for load profile analysis) and designs resilient microgrid frameworks for disaster recovery. Recent Achievements: Recipient of 2023 Blessis Outstanding Undergraduate Advisor Award Elevated to IEEE Fellow (2020) Author of Demand Response in Smart Grids (2019) Grants & Labs: Leads projects funded by U.S. Department of Energy and industry partners. Her lab focuses on co-simulation platforms, hardware-in-loop testing, and grid-edge innovation. Active in developing tools like FeederGAN and the PARS solar resiliency platform.

Anna Marszal-Pomianowskaمشاهده پروفایل

دانشیار

• Energy Engineering
• Residential Building Engineering
• District Heating
• +۶ مورد دیگر

Anna Marszal-Pomianowska is an Associate Professor and Research Group Leader at the Department of Construction, Urban and Environmental Engineering, Aalborg University. She leads the Research Group for Energy in Buildings and holds academic affiliations within the Faculty of Engineering and Science. Her research focuses on energy systems, particularly district heating, residential building performance, and occupant behavior analysis. PhD in Energy Engineering (2012) Completed University Pedagogy course for Assistant Professors (2012–2015) Her research interests include fault detection in HVAC systems, energy flexibility of buildings, and the integration of smart meter data for optimizing energy use. She has led projects like IEA EBC Annex 84 and FOREFRONT, addressing demand management and smart heat data utilization. Over 30 MSc and 6 PhD students have been supervised under her guidance. Key publications include analyses of occupant behavior in residential buildings (2025), fault detection methodologies (2025), and smart meter data-driven frameworks (2022–2024). She contributes to datasets like the Residential Household Dataset (2025) and collaborates with institutions globally.