جست‌وجوی استاد و پژوهشگر

Zhuang Zhengمشاهده پروفایل

مدرس

• Smart Grids
• Building Energy Management
• Renewable Energy Systems
• +۳ مورد دیگر

Dr. Zhuang Zheng is a Lecturer (Assistant Professor) at the School of Computing, Engineering & Digital Technologies, Teesside University since April 2024. Previously, he served as a Postdoc Fellow at the Hong Kong Polytechnic University’s Department of Building Environment and Energy Engineering. He holds a PhD in Architecture and Civil Engineering from City University of Hong Kong (2021). His research focuses on smart energy systems, particularly next-generation residential energy management, grid-building interactions, urban-scale energy modeling, and cyber-physical-social frameworks for low-carbon systems. Education: PhD (2021) from City University of Hong Kong; Postdoc (2021–2024) at Hong Kong Polytechnic University. Key Research Interests: Smart grids, building energy management, renewable energy integration, and decarbonization technologies. Notable contributions include novel energy modeling techniques for peak shaving, voltage regulation, and multi-scale urban energy systems. Articles Trends: His work spans smart building controls, IoT-enabled energy systems, stochastic optimization, and safety risk evaluation. Recent focus areas include distributed control strategies for HVAC clusters and voltage regulation in smart grids. Collaborations: Collaborated with Hong Kong Sun Hung Kai and AECOM on commercial building energy flexibility projects. Actively contributes to international journals and conferences, including chairing sessions at the International Conference on Applied Energy (ICAE). Future Directions: Developing intelligent digitalization for building, power, and transportation sectors to advance smart low-carbon systems through socio-technical integration.

Fred Popowichمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Big Data
• +۴ مورد دیگر

Fred Popowich is a Professor in the School of Computing Science at Simon Fraser University (SFU), Canada. He holds adjunct positions at Dalhousie University's Faculty of Graduate Studies and is an Associate Member of SFU's Department of Linguistics and Cognitive Science Program. His academic career began post-PhD (Cognitive Science/Artificial Intelligence, University of Edinburgh, 1989) and has spanned over three decades at SFU. Education: PhD in Cognitive Science/Artificial Intelligence (University of Edinburgh, 1989); M.Sc. and B.Sc. in Computing Science (Simon Fraser University and University of Alberta, 1985/1982). Research focuses on natural language processing (NLP), machine translation, intelligent systems, and big data applications. He directs SFU’s Big Data Initiative and leads the Natural Language Laboratory, supervising MSc/PhD students in computing science. His work includes developing systems for smart homes, toxic language detection in social media, and energy grid analysis. Industry roles include co-founding Axonwave Software (as CTO/President) and contributing to technology commercialization. Current projects address EV charging impacts, personalized learning systems, and real-time load monitoring. Publications span machine translation, sentiment analysis, and NLP applications in education and energy systems. His work bridges theoretical computer science with practical applications in healthcare, smart cities, and education.

• Decision Support Systems
• Energy Management
• Sustainable Energy Planning
• +۸ مورد دیگر

Dr. Vangelis Marinakis is an Assistant Professor at the School of Electrical and Computer Engineering (ECE) of the National Technical University of Athens (NTUA). His academic background includes an Electrical and Computer Engineering degree and a PhD in Decision Support Systems for Sustainable Energy Planning from NTUA. PhD in Decision Support Systems for Sustainable Energy Planning (NTUA) Electrical and Computer Engineer (NTUA) His research focuses on designing methodologies for intelligent energy management across Smart Homes, Buildings, Cities, and Districts, leveraging technologies like IoT, AI, and Big Data. He has contributed to over 25 European (Horizon Europe, H2020) and national projects, with more than 50 journal publications and book chapters. Key research areas include Decision Support Systems , Energy Efficiency , and Renewable Energy Integration . He has led research in AI-driven energy forecasting, federated learning for privacy-preserving data models, and blockchain applications in energy markets. His work explores the intersection of Smart Grids , Building Informatics , and Climate Resilience . Dr. Marinakis has developed frameworks for: Decarbonization-as-a-Service in building renovations Scalable Big Data architectures for smart buildings Multi-criteria optimization of EV charging stations Explainable AI in energy decision-making Climate resilience assessment for urban housing

• Power Systems
• Smart Grids
• Distribution System State Estimation
• +۴ مورد دیگر

Dr. Barry Hayes is a Senior Lecturer (Associate Professor) in Power Systems Engineering at University College Cork (UCC), Ireland. He leads a research team focusing on grid integration of sustainable energy technologies and future power system operation/planning. Previously held roles include Lecturer at University of Galway (2016-2018) and Marie Sklodowska-Curie Research Fellow at IMDEA Energy (2013-2016). Holds a PhD in Electrical Power Systems Engineering from University of Edinburgh (2013). Research interests include smart grids, distribution network management, demand-side flexibility, non-intrusive load monitoring, and energy communities. Active in IEEE standards development (P2030 smart grid interoperability) and serves as Associate Editor for IEEE Transactions on Energy Markets, Policy and Regulation. Has secured €1.03M SFI grant (2024-2029) for disruptive energy tech research, plus other grants including €506K from Disruptive Technologies Innovation Fund. Supervised PhD students in power systems and energy markets. Collaborates with Irish energy industry and international partners through MaREI Centre. Publications emphasize grid integration challenges, P2P energy trading, and smart meter applications. Notable recognition includes Elsevier's Most-Cited Article award (2022). Engages in public science communication via RTÉ and advises community-owned renewable energy projects.

Steven B. Leebمشاهده پروفایل

استاد

• Power Electronics
• Energy Conversion Systems
• Motor Drives
• +۵ مورد دیگر

Steven B. Leeb is a Professor of Electrical Engineering and Computer Science (EECS) and Mechanical Engineering at MIT. He holds the Carl Richard Soderberg Chair in Power Engineering and is a MacVicar Fellow, recognizing his excellence in teaching and research. His work focuses on power electronics, energy conversion systems, motor drives, and nonintrusive power monitoring. Leeb has contributed to advancements in smart grid technologies, building energy management, and fault detection systems. He leads interdisciplinary research integrating control systems with renewable energy applications. Education: B.Sc., M.Sc., M.Eng., and Ph.D. in Electrical Engineering from MIT (1987–1993). His career at MIT spans over three decades, progressing from Assistant Professor (1993) to Full Professor (2005). He has advised numerous projects in collaboration with industries and military applications, including naval propulsion systems and unmanned aerial vehicle (UAV) energy buffering. Research Interests: Power Electronics and Motor Drives Nonintrusive Load Monitoring (NILM) Building Energy Management Smart Grid Technologies Sensors and Sensor Networks Electromagnetic Systems Awards: Over 15 major awards, including the National Science Foundation CAREER Award (1996), IEEE Fellow (2007), and multiple R&D 100 Awards. His work has led to patents in nonintrusive monitoring systems, smart lighting, and energy-efficient technologies. Teaching: Courses include Mechanical Engineering Tools (2.670) , Power Electronics Laboratory (6.131) , and Microcomputer Project Laboratory (6.115) . He emphasizes hands-on learning through labs like his DC motor experiments, which are featured in freshman orientation programs. Labs/Teams: Active in MIT’s Energy Initiative, collaborating with industry partners on projects such as naval propulsion systems and UAV energy systems. His group develops prototypes for commercial applications in smart buildings and renewable energy integration.

Richard Collinsمشاهده پروفایل

مدرس ارشد

• Water Distribution Systems
• Hydraulic Transients
• Pipe Fatigue Analysis
• +۵ مورد دیگر

Dr Richard Collins is a Senior Lecturer in Water Engineering at the University of Sheffield , affiliated with the School of Mechanical, Aerospace and Civil Engineering. His research focuses on hydraulic transients , pipeline integrity , and smart water infrastructure . Graduated with an Aerospace Engineering degree (2005) and PhD in Materials and Mechanical Engineering (2009) Current research explores pressure transients , leak detection , and autonomous robotic systems for pipeline inspection Projects include fatigue analysis , biofilm mobilisation , and ultrasound-based pipe assessment His publications emphasize cast iron pipe fatigue , acoustic leak detection , and transient-induced contamination . Funded by RCUK and Datatecnics , his work bridges mechanical engineering and civil infrastructure challenges.

• Network Protocols Engineering
• Cybersecurity
• Distributed Systems
• +۳ مورد دیگر

Klaus Wehrle is a Professor of Computer Science and Head of the Communication and Distributed Systems Group (Informatik 4) at RWTH Aachen University. He holds a diploma (1999) and doctorate (2002) from the University of Karlsruhe (now KIT). His research focuses on network protocols, cybersecurity, and distributed systems, funded by DFG, ERC, EU, and industry. He leads the DFG Priority Program on Cyber-Physical Networking and holds ERC grants. Awards include Südwestmetall and FZI Dissertation Prizes. He serves on IEEE, ACM, and DFG committees. His work spans protocol engineering, network simulation, and reliable communication software. He advises the Department of Computer Science at RWTH and collaborates on projects like In-Network Computing and Industrial IoT security. His research addresses industrial systems, smart grids, and privacy-preserving data sharing.

Daniel Pizarro Pérezمشاهده پروفایل

استاد

• Computer Vision
• Medical Imaging
• Smart Grids
• +۳ مورد دیگر

Dr. Daniel Pizarro Pérez is a Professor in the Department of Electronics at the University of Alcalá, Spain. He is a member of the GEINTRA research group, which focuses on Electronic Engineering applied to Intelligent Spaces and Transport. He holds a doctoral degree from the University of Alcalá, completing his thesis on 'Localización de robots móviles en espacios inteligentes utilizando cámaras externas y marcas naturales' (2008), supervised by Dr. Manuel Ramón Mazo Quintas and Dr. Enrique Santiso Gómez. His research interests span computer vision, medical imaging, smart grid technologies, acoustic signal processing, and control systems. Notable projects include augmented reality applications in laparoscopic surgery, non-intrusive load monitoring using smart meters, and advanced control methodologies for power electronics. His work bridges theoretical advancements with practical applications in healthcare, energy systems, and robotics. Recent publications highlight contributions to neural radiance fields for minimally-invasive surgery, distributed acoustic sensing in submarine environments, and deep learning-based activity recognition via energy consumption. His research frequently integrates interdisciplinary approaches, such as combining computer vision with medical robotics and leveraging machine learning for real-time control systems. Dr. Pizarro’s work has been supported by grants and collaborations within the GEINTRA group, with applications in intelligent spaces, robotic navigation, and sensor fusion. He has also contributed to educational initiatives like the GEMS Erasmus+ project, emphasizing sensory module development for robotics education.

David Yauمشاهده پروفایل

استاد

• Network Protocol Design
• Cyber-Physical System Security
• Network Security and Privacy
• +۳ مورد دیگر

David Yau is a Professor in the Information Systems Technology and Design (ISTD) pillar at the Singapore University of Technology and Design (SUTD). He holds a B.Sc. from the Chinese University of Hong Kong and M.S./Ph.D. from the University of Texas at Austin, all in Computer Science. Previously, he was Distinguished Scientist at the Advanced Digital Sciences Center (ADSC) in Singapore and Qiushi Chaired Professor at Zhejiang University, China. His research focuses on network security, cyber-physical systems, and smart grid resilience. He has led over 15 research projects funded by agencies like Singapore's NRF, A*Star, and the U.S. NSF. Notable awards include the IBM Fellowship, NSF CAREER Award, and Best Paper awards at IPSN 2017 and IEEE MFI 2010. Yau has advised numerous PhD/Master’s students, many now in academia and industry. His work spans secure protocols, intrusion detection in critical infrastructures, and privacy-preserving smart grid technologies. He has authored over 100 peer-reviewed publications and served on editorial boards of ACM Transactions on Sensor Networks and IEEE journals.

Mingjun Zhongمشاهده پروفایل

مدرس

• Machine Learning
• Computational Statistics
• Non-intrusive Load Monitoring
• +۱۲ مورد دیگر

Dr. Mingjun Zhong is a Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen. His research focuses on machine learning and computational statistics with applications in healthcare, energy systems, and medical imaging. He is actively engaged in teaching and academic service, including editorial roles in prominent journals. Position: Lecturer Institution: University of Aberdeen School: School of Natural and Computing Sciences Email: mingjun.zhong@abdn.ac.uk Dr. Zhong's research interests center on probabilistic and statistical machine learning methodologies applied to real-world data. He works on healthcare data analysis, non-intrusive load monitoring (NILM), spectroscopy, EEG/fMRI, and energy disaggregation. His methodological expertise includes variational inference, Markov chain Monte Carlo, Bayesian matrix factorization, and deep learning. He has developed lightweight and efficient neural network models for applications in medical imaging and smart grids. The most recent publications reflect a strong trend in applying advanced machine learning techniques—particularly deep learning, self-supervised learning, and capsule networks—to diverse domains such as medical diagnostics, energy disaggregation, and clinical decision support. There is a clear emphasis on developing efficient, interpretable, and robust models for real-world deployment, often addressing challenges like class imbalance, domain adaptation, and data scarcity. Scientific recognition includes: Fellow of the Higher Education Academy (FHEA) Associate Editor, Neural Processing Letters Review Editor, Frontiers in Applied Mathematics and Statistics Regular reviewer for top-tier journals and conferences in AI and machine learning Grant reviewer for multiple funding bodies Dr. Zhong advises a number of students, as evidenced by co-authorships on numerous publications. His research is supported by academic collaborations and likely external grants, though specific funding details are not mentioned. He teaches courses in Robotics, Machine Learning, and Knowledge Representation and Reasoning, contributing significantly to the curriculum in computing sciences. He is involved in interdisciplinary research, particularly through projects like ARCHERY (Artificial intelligence to Revolutionise the patient Care pathway in Hip and knEe aRthroplastY), which integrates AI into orthopaedic care. His work bridges computer science, statistics, and domain-specific applications, demonstrating a strong commitment to impactful, application-driven research.

Wagner Ourique De Moraisمشاهده پروفایل

مدرس

• Ambient Assisted Living (AAL)
• Smart Home Systems
• Healthcare Technology
• +۳ مورد دیگر

Wagner Ourique De Morais is a Lecturer at the Academy of Information Technology , Halmstad University, with a focus on Smart Home Systems and Healthcare Technology for elderly populations. Doctoral Thesis : Architecting Smart Home Environments for Healthcare: A Database-Centric Approach (2015) Key Research Areas : Ambient Assisted Living (AAL), Graph Neural Networks for Clinical Prediction, IoT-Edge-Cloud Offloading, and Wearable Health Monitors His work spans 15+ years , including publications on Zero-Day Attack Detection in Vehicular Networks (2025), Graph Neural Networks for Clinical Risk Prediction (2024), and Smart Medication Organizers (2017). Research trends emphasize sensor integration , context-aware computing , and security in smart environments . He contributes to projects like the "Safe at Night" initiative and has developed middleware for heterogeneous sensor networks (2008). Current teaching and research at Halmstad University prioritize technology-enabled elderly care and cyber-physical system design .

Ioana Banicescuمشاهده پروفایل

استاد

• Parallel Algorithms
• Scientific Computing
• Performance Modeling
• +۳ مورد دیگر

Ioana Banicescu is a Professor at Mississippi State University's Department of Computer Science and Engineering. She holds a Ph.D. in Computer Science from New York University and previously directed the Center for Cloud and Autonomic Computing. Her research focuses on parallel algorithms, scientific computing, performance modeling, and autonomic systems. Her education includes: Ph.D. Computer Science - New York University M.S. Computer Science - New York University B.S./M.S. Electronics and Telecommunications - Polytechnic University of Bucharest Banicescu's research explores fundamental challenges in high-performance computing, including dynamic load balancing, performance optimization, and autonomic resource management. Her recent work emphasizes explainable AI for cybersecurity systems and robust resource allocation models for heterogeneous computing environments. Her publication record demonstrates consistent focus on performance optimization in parallel systems, with recent expansion into cybersecurity applications using machine learning approaches. She maintains collaborations with national laboratories and international research forums. Banicescu has received multiple National Science Foundation awards for research excellence and serves on numerous program committees for IEEE/ACM conferences. She contributes to the academic community through editorial roles for Cluster Computing and the International Journal on Computational Science and Engineering.

• 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.

Emanuele Principiمشاهده پروفایل

دانشیار

• Electrical Engineering
• Signal Processing
• Machine Learning
• +۴ مورد دیگر

Emanuele Principi serves as an Associate Professor in the Department of Information Engineering at the College of Engineering, Università Politecnica delle Marche (UNIVPM). His academic appointment falls under the scientific sector IIET-01/A - Elettrotecnica (Electrical Engineering). Based at Via Brecce Bianche 12 in Ancona, Italy, Professor Principi maintains regular office hours on Fridays from 15:00-18:00 via Microsoft Teams, and can be contacted at e.principi@univpm.it or by phone at 0712204626. Professor Principi's research spans electrical engineering with a strong focus on signal processing, machine learning applications, and energy systems. His work particularly emphasizes non-intrusive load monitoring (NILM), acoustic signal analysis, and renewable energy integration. He has developed innovative approaches combining deep learning with electrical engineering principles to address challenges in energy disaggregation, sound event classification, and smart grid technologies. His research bridges theoretical advancements with practical applications in sustainable energy systems and intelligent monitoring solutions. Analysis of Professor Principi's publication trends reveals a consistent research trajectory with increasing focus on machine learning applications for energy systems. His recent work (2023-2025) demonstrates growing sophistication in knowledge distillation techniques for non-intrusive load monitoring, with increasing attention to interpretability and edge computing implementations. He has also expanded his research into photovoltaic-thermal systems with thermoelectric generators, showing diversification into renewable energy conversion while maintaining his core expertise in electrical signal processing. Professor Principi has made significant contributions to the fields of non-intrusive load monitoring and acoustic signal processing through his extensive publication record. His work on knowledge distillation frameworks, weakly supervised learning approaches, and real-time signal processing systems has advanced methodologies in energy monitoring and sound event classification. Professor Principi actively advises students and researchers in electrical engineering, signal processing, and machine learning applications. His research projects likely involve collaborations with industry partners in energy technology and smart grid development, though specific grant details are not provided in the available information. He appears to lead or participate in research teams focused on developing practical implementations of his theoretical work, particularly in edge computing applications for energy monitoring. Professor Principi's laboratory or research group likely focuses on signal processing for energy systems and acoustic analysis, with equipment supporting electrical measurements, audio signal acquisition, and machine learning model development. His work suggests integration of hardware and software systems for real-world implementation of monitoring technologies.

Abouzar Estebsariمشاهده پروفایل

دانشیار

• Sustainable Energy Systems
• Smart Grids
• Power System Security
• +۴ مورد دیگر

Abouzar Estebsari is an Associate Professor in Civil and Building Services Engineering at London South Bank University (LSBU). He holds roles as Course Director of the MSc Building Services Engineering program and Timetabling Lead of the department. His expertise spans sustainable energy systems, smart grids, and power system security, with a focus on multi-vector energy systems and real-time simulations using tools like Opal-RT. Estebsari has contributed to EU-funded projects (FP7/H2020) and secured grants including £1.03M from Innovate UK as Co-Investigator. Educated at Politecnico di Torino (PhD, 2015), he held postdoctoral and faculty roles there before joining LSBU. He has also been a Visiting Scholar at RWTH-Aachen University (2016) and a Research Scholar at The George Washington University (2019). His research emphasizes low-carbon energy solutions, collaborating with BSRIA LSBU Net Zero Building Centre and the Centre for Civil and Building Services Engineering (CCiBSE). Estebsari's work addresses UN SDGs related to affordable and clean energy and sustainable cities. His 60+ peer-reviewed publications (H-index 21) cover topics like non-intrusive load monitoring (NILM), IoT-enabled smart grids, and energy policy analysis. He has received the 2020 Manby Prize for contributions to ICT/BIM in civil engineering. He advises PhD students and serves on international examination boards, reviews EPSRC proposals, and evaluates EU Marie Sklodowska Curie Actions. His current projects focus on nanogrids, seaport energy resilience, and decision tools for power systems in developing nations.