جست‌وجوی استاد و پژوهشگر

• Power Electronics
• Energy Conversion
• Microgrids
• +۴ مورد دیگر

Bjarte Hoff is an Associate Professor and Study Program Director for the Master of Electrical Engineering at UiT The Arctic University of Norway's Department of Electrical Technology. His core research focuses on power electronics, energy conversion systems, microgrids, and smart energy applications, particularly in Arctic environments. He leads multiple projects including RENEW (renewable energy systems), Smart Senja, and HyEkoTank (hydrogen fuel cells for maritime transport). His research interests span: Power electronics and control algorithms Low-voltage electrical installations and microgrids Distributed generation and smart grids Electric transport systems (maritime/vehicular) Renewable energy integration Capacitive wireless power transfer Publication analysis shows consistent focus on: energy conversion topologies (especially current source inverters), Arctic energy management using deep learning, capacitive wireless charging for maritime applications, and optimization of power transfer systems. His most recent works demonstrate increasing emphasis on AI-driven energy management and hydrogen fuel cell integration. He teaches courses including: ELE-3605 Electrical Engineering Project ELE-3607 Power Electronics ELE-3608 Advanced Electric Drives ETE-2801 Electrical Installations As principal investigator, he leads projects such as: RENEW - Renewable & Smart Rural Power Systems Smart Senja (Arctic microgrids) HyEkoTank (hydrogen PEM fuel cells for ships) Arctic Energy research initiative UNDERSEA capacitive charging systems He collaborates with the Electromechanical Systems research group and supervises PhD candidates in power electronics and energy systems.

Longbing Caoمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Professor Longbing Cao serves as a Professor at University of Technology Sydney (UTS) in the Faculty of Engineering and Information Technology, specifically within the Data Science Institute. He also holds the position of Distinguished Chair Professor in AI at Macquarie University and is an ARC Future Fellow (Level 3). As a pioneering researcher in AI, data science, and advanced analytics, Professor Cao founded the UTS Advanced Analytics Institute, the first data science initiative in Australia. Professor Cao's research interests span across data science fundamentals, machine/deep learning, behavior informatics, and enterprise data science applications. His work bridges the gap between theoretical research and practical implementation, focusing on non-IID learning, actionable knowledge discovery, and complex behavior modeling. His research has addressed real-world challenges in government services, finance, insurance, banking, telecommunications, transport, and e-commerce sectors. His 15 most recent publications reveal a strong trend toward addressing distributional challenges in machine learning, developing transdisciplinary AI approaches, modeling complex temporal dependencies, and advancing negative sequence analysis. His work consistently integrates theoretical foundations with practical applications, particularly in financial analytics, healthcare, and disaster resilience. Scientific Awards: Eureka Prize for Excellence in Data Science (2019) ACM Distinguished Scientist Professor Cao has made significant contributions to academic leadership and research supervision. He established world-first research degrees at UTS (Master of Analytics and PhD Thesis: Analytics), founded the IEEE Task Force on Data Science and Advanced Analytics, and served as general chair of the KDD conference in 2015 (the first edition in Australia). His funded research portfolio includes multiple ARC grants and industry collaborations focusing on complex data analytics, behavior informatics, and AI applications. He leads the Data Science Lab at UTS, which has become a prominent center for data science research in Australia. The lab focuses on developing fundamental theories and practical applications of data science, with strong industry partnerships that translate research into real-world impact. Professor Cao's leadership in establishing the annual Big Data Summit in 2012 created Australia's first platform for bridging academic research with industry and government applications.

• Electric Vehicle Engineering
• Battery System Design
• Autonomous Vehicle Optimization
• +۵ مورد دیگر

Jelena Andric is an Associate Professor in the Vehicle Engineering and Autonomous Systems department at Chalmers University of Technology, working within the Mechanics and Maritime Sciences school. Her research spans multiple aspects of vehicle engineering with a particular focus on electric and autonomous vehicle technologies. Her research interests primarily center around electric vehicle engineering, battery system design, autonomous vehicle optimization, thermal management systems, computational fluid dynamics, reinforcement learning applications, and hardware-in-the-loop simulation. She has developed expertise in applying advanced computational methods to solve complex vehicle engineering problems, particularly in the areas of battery management and energy optimization. Analysis of her recent publications reveals a strong trend toward optimizing electric vehicle performance, particularly focusing on battery degradation mitigation, thermal management, and energy efficiency. Her work increasingly incorporates artificial intelligence techniques, especially reinforcement learning, to develop sophisticated optimization approaches for electric trucks and autonomous vehicles. The research spans from fundamental fluid dynamics studies to applied engineering solutions for real-world transportation challenges. Dr. Andric has been actively involved in numerous research projects funded by organizations including VINNOVA, the Swedish Energy Agency, CHAIR, and ÅForsk. These projects cover diverse areas such as battery aging prediction, vehicle management strategies for electrified vehicles, electric public transit systems, and virtual engine calibration. Her current research portfolio includes leadership in projects related to simulation-based evaluation of connected vehicles, battery aging prediction for electric autonomous vehicles, AI-cloud-based vehicle management strategies, and transition to electric public transit systems. These projects demonstrate her commitment to addressing critical challenges in sustainable transportation through advanced engineering solutions.

Hsia-Ching Changمشاهده پروفایل

دانشیار

• Adoption/Diffusion of Social Media
• Data Analytics
• Knowledge/Science Mapping
• +۴ مورد دیگر

Dr. Hsia-Ching Chang is an Associate Professor at the University of North Texas , specializing in the intersection of information science, cybersecurity, and social media adoption. Her interdisciplinary research contributes to understanding how digital technologies shape human interaction and organizational practices. Ph.D. and M.S. in Information Science from State University of New York, Albany M.A. and B.A. in Library and Information Science from National Taipei University, Taiwan Her research focuses on cybersecurity education , social media adoption , data governance , information architecture , and knowledge mapping . She employs methodologies like science mapping , meta-synthesis , and information theoretical frameworks to analyze technological trends and information behavior. Recent publications highlight her work on Twitter data analysis , mobile technologies , and smart library systems . While no formal awards or advisees are listed, her contributions span education, research, and professional practice in information science.

Elham Shiraziمشاهده پروفایل

استادیار

• Artificial Intelligence
• Energy Management
• Sustainable Energy
• +۴ مورد دیگر

Elham Shirazi is an Assistant Professor specializing in Advanced Manufacturing, Sustainable Products & Energy Systems . Her research aligns with UN Sustainable Development Goals, focusing on Energy Management , Smart Grids , and Artificial Intelligence applications in energy systems. She has an h-index of 10 and 577 citations, primarily in Machine Learning and Power Systems . Her recent publications highlight trends in PV Power Forecasting , Energy Storage Optimization , and Grid Stability . Key methodologies include Long Short-Term Memory (LSTM) , Deep Reinforcement Learning , and Wavelet Packet Decomposition , applied to Building-Integrated Photovoltaics and Multi-agent Systems for grid resilience.

• Energy Systems
• Renewable Energy
• District Heating and Cooling
• +۵ مورد دیگر

Giuseppe Franchini is a Full Professor of Energy and Environmental Systems at the Department of Engineering and Applied Sciences at the University of Bergamo. He serves as both the Rector's Delegate for Energy Transition and Director of the Department of Engineering and Applied Sciences. His academic career is centered on energy systems research with significant contributions to renewable energy technologies. He teaches Energy Systems and Renewable Energy Technologies in the Master's Degree Program in Mechanical Engineering, along with other related courses including Smart Energy Systems and Technologies for Energy Sustainability. Professor Franchini's research focuses on complex energy systems with particular emphasis on renewable energy plants and experimental investigations of turbomachinery. His work spans across solar energy systems, district heating and cooling networks, hydrogen energy applications, and smart grid technologies. His research group actively investigates optimization strategies for renewable energy integration in various contexts from urban environments to remote communities. His publication record shows a strong trend toward practical applications of renewable energy systems, with recent work focusing on energy transition challenges in different geographical contexts including Latvia, Milan, and the Arabian Peninsula. His research increasingly incorporates machine learning techniques for energy forecasting and system optimization, while maintaining strong emphasis on techno-economic assessments of emerging energy technologies like green hydrogen production. As Director of the Department of Engineering and Applied Sciences, Professor Franchini oversees multiple research initiatives and educational programs. His leadership role as Rector's Delegate for Energy Transition positions him at the forefront of implementing sustainable energy practices at the university level and influencing regional energy policies.

• Industrial Digitalization
• Automation
• Human-Robot Collaboration
• +۷ مورد دیگر

Professor Johan Stahre is affiliated with Chalmers University of Technology, where he leads the Division of Production Systems and serves as Assistant Head of Department. His expertise spans industrial digitalization, automation, and the human role in future manufacturing systems. Codirector of Produktion2030 (2013–present) Central figure in EIT Manufacturing development (2016–present) Chalmers representative in European Factories of the Future Research Organisation His research focuses on: Manufacturing resilience and uncertainty navigation Human-Robot Collaboration in restricted environments 5G-enabled smart maintenance systems Sustainability through digital servitization Skill gaps in Industry 4.0/5.0 Computer vision applications in assembly systems Recent publications highlight trends in immersive technologies for manufacturing and resilience frameworks. While no formal awards are listed, his leadership in national and European innovation programs underscores his impact.

Amine Mhedhbiمشاهده پروفایل

استادیار

• Database Management
• Graph-Structured Databases
• Query Optimization
• +۴ مورد دیگر

Amine Mhedhbi is an Assistant Professor at Polytechnique Montréal in the Department of Computer Engineering and Software Engineering. He is affiliated with the Institute for Data Valorization (IVADO) and the Software Engineering for Machine Learning Applications (SEMLA) group. His research focuses on data management systems, particularly graph-structured databases, multimodal data engineering, and AI-driven query optimization. Ph.D. in Computer Science from University of Waterloo Former technical advisor to enterprise companies Prior applied research leadership at Distyl AI and internships at Microsoft Research His recent work explores integrating large language models (LLMs) into database systems, optimizing SQL generation, and advancing graph database architectures. Key projects include GraphflowDB and FlockMTL , addressing scalability and declarative semantic applications. Scientific awards include: NSERC Discovery Grant with Discovery Launch Supplement (2025) Cheriton School Distinguished Dissertation Award (2024) Microsoft Research Ph.D. Fellowship (2020) VLDB Best Paper Award (2018) He supervises graduate students in database systems and machine learning applications and serves on program committees for top-tier conferences like VLDB and SIGMOD.

Ahmed H. Elsheikhمشاهده پروفایل

استاد

• Subsurface Engineering
• Artificial Intelligence in Energy Systems
• Machine Learning Applications
• +۵ مورد دیگر

Professor Ahmed H. Elsheikh is a faculty member at Heriot-Watt University's School of Energy, Geoscience, Infrastructure and Society, within the Institute for GeoEnergy Engineering. He holds a Professor title since 2021, previously serving as Associate (2017-2021) and Assistant Professor (2013-2017). His educational background includes a Ph.D. (2010) and MASc (2002) from McMaster University, and a BASc from Al-Azhar University (1999). Research Interests: Fluid control via AI, predictive machine learning, generative modeling, data assimilation, Bayesian uncertainty quantification, and subsurface engineering. His work addresses challenges in reservoir modeling, CO2 sequestration, seismic inversion, and geophysical data analysis. Publications span 2004-2025 with a focus on machine learning applications in energy systems, stochastic field generation, and subsurface flow modeling. Notable contributions include AI-driven seismic inversion, deep learning for reactive transport, and ensemble-based history matching. Key Achievements: Developed novel methodologies for uncertainty quantification, GAN applications in geological modeling, and real-time reservoir monitoring systems. His work contributes to UN Sustainable Development Goals related to climate action (SDG 13) and affordable clean energy (SDG 7).

Jan Rossmeislمشاهده پروفایل

استاد

• Catalysis
• Green Chemistry
• Energy Conversion
• +۴ مورد دیگر

Jan Rossmeisl is a Professor in the Department of Chemistry at the University of Copenhagen, Denmark, conducting research at the intersection of catalysis, sustainable energy, and electrochemical processes. His work focuses on developing advanced materials for energy conversion applications including fuel cells and carbon dioxide reduction technologies. His research interests span catalysis , green chemistry , and energy conversion , with specific expertise in electrocatalytic reaction mechanisms, high-entropy alloy design, and sustainable pathways for chemical synthesis. Current investigations emphasize computational modeling of surface reactions and experimental validation of novel catalyst systems for renewable energy applications. Analysis of his 2024-2025 publications reveals a dominant focus on electrocatalytic $$\text{CO}_2$$ conversion and oxygen reduction processes. Key trends include the development of high-entropy alloys for catalytic coupling reactions, strain engineering in binary alloys, and non-aqueous electrolyte effects on $$\text{CO}_2$$ reduction. His work bridges theoretical computation with experimental validation to address challenges in sustainable energy conversion and chemical production.

• Optical Engineering
• Biomedical Engineering
• Machine Learning
• +۴ مورد دیگر

Demetri Psaltis is a **Professor honoraire** at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences (STI) and the Department of Physics (PH-STI). He holds roles as **Chargé de cours** (Lecturer) across multiple departments including Microengineering (SMT-ENS), Electrical and Electronics Engineering (SEL-ENS), and serves as **Professeur hôte** (Host Professor) at the Laboratoire d'hémodynamique et de technologie cardiovasculaire (LHTC). His research focuses on advanced optical systems, biomedical imaging, nonlinear optics, and the integration of machine learning with optical technologies. Key affiliations include the Institute of Bioengineering (IBI-STI) and administrative roles in the IBI-STI-GE management unit. He has advised over 20 PhD students at EPFL, contributing significantly to their thesis work. His laboratories develop cutting-edge tools for applications in medical diagnostics, energy systems, and optical computing. Research interests span computational optical imaging, optical computing architectures, 3D printing with light, and AI-driven wavefront shaping. Recent publications emphasize innovations in hybrid neural networks, optical diffusion models, and scalable optical circuit switching. His work bridges fundamental physics with practical applications in healthcare and renewable energy sectors. Labs: Laboratoire d'hémodynamique et de technologie cardiovasculaire (LHTC), IBI-STI Institute Teaching:** Courses include Computational Optical Imaging, Optical Computing, and 3D Printing with Light.

Jian Liuمشاهده پروفایل

عضو هیئت علمی

• Artificial Intelligence and Machine Learning
• Energy Economics and Sustainable Operations
• Operations Research and Supply Chain Optimization
• +۳ مورد دیگر

Dr. Jian Liu is an Assistant Research Professor at the Kummer Institute Center for Artificial Intelligence and Autonomous Systems and the Department of Electrical and Computer Engineering at Missouri University of Science and Technology. He holds a Ph.D. in Electrical Engineering from Missouri S&T and another Ph.D. in Management Science and Engineering from Nanjing University of Aeronautics and Astronautics. His research focuses on artificial intelligence, energy systems optimization, and operations research with applications in sustainable energy, queueing theory, and supply chain management. He has held visiting positions at prestigious institutions including Penn State University and Tsinghua University. Research Interests: Artificial Intelligence (Explainable AI, Reinforcement Learning) Energy Economics (Electricity Markets, Renewable Integration) Behavioral Operations (Queueing Systems, Fairness in Service) Complex System Optimization (Supply Chains, Manufacturing) Publications reflect expertise in AI applications for edge computing, energy storage optimization, and behavioral queueing models. Recent work addresses fairness in service systems, dynamic pricing strategies, and multi-period energy dispatch optimization. His research spans interdisciplinary areas including smart grids, sustainable materials, and industrial process optimization. Liu has advised multiple collaborative projects at the intersection of AI and energy systems. His work has been published in top journals like Applied Energy and IEEE Transactions on Power Systems .

Seif Haridiمشاهده پروفایل

استاد

• Parallel Computing
• Distributed Computing
• Cloud Computing
• +۴ مورد دیگر

Seif Haridi is a Professor at KTH Royal Institute of Technology in Stockholm, Sweden, specializing in parallel and distributed computing systems. He holds dual roles as Chair-Professor of Computer Systems and Chief Scientific Advisor at RISE SICS. His research integrates systems engineering with theoretical foundations, focusing on programming systems, distributed computing, and big data technologies. Key contributions include co-designing SICStus Prolog, the Mozart Programming System, and Apache Flink, as well as leading the development of HOPS, a European big data platform awarded the IEEE Scale Prize 2017. He has led major EU projects like EIT-Digital’s cloud computing initiative and co-founded startups such as LogicalClocks and HiveStreaming. His teaching includes courses on distributed algorithms and peer-to-peer computing at KTH. Notable awards include the European Data Science Technology Innovation 2019. His work spans systems like HOPS, Flink, and Kompics, emphasizing scalability and robustness in distributed environments. Current projects include CDA (Continuous Deep Analytics) and ExtremeEarth for geospatial data analysis. Research interests include distributed algorithms, consensus protocols, and cloud-native systems. His lab’s contributions to scalable storage (e.g., HopsFS) and stream processing (Apache Flink) highlight his impact on both academia and industry.

Florian Dosterمشاهده پروفایل

استاد

• Subsurface Flow Phenomena
• Geological Carbon Storage
• Porous Media Modeling
• +۳ مورد دیگر

Florian Doster is a Professor at Heriot-Watt University, affiliated with the School of Energy, Geoscience, Infrastructure and Society and the Institute for GeoEnergy Engineering. He leads the MuPhi Research Group, focusing on multi-scale modeling of subsurface flow phenomena, particularly in porous media and fractured reservoirs. His work addresses challenges in CO2 storage, hydrocarbon production, and groundwater management. He actively supervises PhD students, including Amanzhol Kubeyev, and collaborates with industry and research councils. Research interests include multi-scale simulation, reactive transport, and AI-driven uncertainty quantification, with applications to geological carbon sequestration and reservoir engineering. His group develops open-source tools, such as the Carbonate Reservoir Group HWU code. Notable projects include regional screening of saline aquifers in Malaysia and fracture mechanics studies. Publications highlight advancements in AI for subsurface modeling, CO2 leakage risk assessment, and pore-scale interactions. Funding sources include industry partnerships and research councils. The MuPhi Group’s activities span geomechanics, fracture network analysis, and environmental sustainability.

Jiangfeng Zhangمشاهده پروفایل

دانشیار

• Battery Management Systems
• Smart Grid Integration
• Renewable Energy
• +۲ مورد دیگر

Jiangfeng Zhang is an Associate Professor in the Department of Automotive Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research focuses on electric vehicle technologies, battery management, renewable energy integration, and smart grid solutions. Research Areas: His work spans battery optimization, grid resilience, autonomous vehicle control, and sustainable energy planning. Recent projects include solid-state battery advancements, V2G integration, and AI-driven energy management. Publication Trends: His articles predominantly address optimization challenges in electric mobility and grid stability, utilizing machine learning, game theory, and control systems. Key themes include decarbonization, cyber-physical security, and renewable integration under uncertainty.