جستجوی استاد راهنمای دانشگاهی

Gonzalo Guillén Gosálbezمشاهده پروفایل

استاد

• Process Systems Engineering
• Sustainable Chemistry
• Environmental Impact Assessment
• +۷ مورد دیگر

Prof. Dr. Gonzalo Guillén Gosálbez is a Full Professor at the Department of Chemistry and Applied Biosciences , ETH Zürich. He holds a PhD in Process Systems Engineering (UPC, 2005) and has held academic positions at Imperial College London (Reader), University of Manchester (Senior Lecturer), and Universitat Rovira i Virgili (Assistant/Associate Professor). His research focuses on Sustainable Chemical Processes , integrating life cycle assessment, optimization techniques, and planetary boundary analysis to evaluate and design low-carbon technologies. Current position: Full Professor, ETH Zürich (2019–present) Prior roles: Imperial College London (2016–2019), University of Manchester (2014–2016), URV Spain (2008–2014) Education: PhD (UPC, 2005), MEng/BEng (University of Murcia) His research explores CO2 valorization , green methanol synthesis , circular marine fuels , and planetary boundary compliance in energy and chemical systems. Recent work emphasizes machine learning for process modeling, single-atom catalysis , and decentralized ammonia production . Scientific contributions include 15+ peer-reviewed articles (2023–2025) in journals like Nature Chemical Engineering , ACS Sustainable Chemistry & Engineering , and Energy & Environmental Science . Key themes: Optimization of hybrid fossil/renewable carbon systems Environmental impacts of energy transitions Catalyst design for sustainable chemistry Life cycle assessment of emerging technologies Awarded UPC Top Doctoral Student Award and Top National Student Award , he combines process systems engineering with sustainability metrics to address global challenges in chemical and energy systems.

Mo Addaمشاهده پروفایل

مدرس ارشد

• Network Security
• Distributed Systems
• Wireless Networks
• +۵ مورد دیگر

Dr. Mo Adda is a Principal Lecturer at the University of Portsmouth's School of Computing, part of the Faculty of Technology. He holds a PhD in Distributed Systems and Parallel Processing from the University of Surrey. His research focuses on network security, distributed systems, wireless networks, and cybercrime prevention. He leads projects in the Centre for Cybercrime and Economic Crime, exploring fault management in networks, blockchain applications, and IoT forensics. With 16 supervised theses, he advises on topics like energy-efficient cloud systems and machine learning for environmental modeling. His work bridges academia and industry, addressing challenges in software-defined networks, traffic control, and secure data sharing in social networks. Education: PhD in Distributed Systems (University of Surrey) Affiliations: Centre of Excellence in Defence, Risk & Resilience; Portsmouth Centre for Advanced Materials and Manufacturing Research Interests Dr. Adda's research spans: Network security and fault detection mechanisms Blockchain applications in IoT and forensics Energy-efficient cloud data center optimization Machine learning for climate modeling Self-organizing network protocols Grants & Collaborations His projects include collaborations with industry partners on secure data leakage detection in cloud systems and resilient wireless protocols for harsh environments. He has pioneered fault classification systems using clustering algorithms and fuzzy logic. Labs & Teams He contributes to the Centre for Cybercrime and Economic Crime, focusing on digital forensics and network intrusion analysis. His team develops frameworks for privacy management in social networks and proactive routing in software-defined networks.

Frede Blaabjergمشاهده پروفایل

استاد

• Power Electronics
• Wind Turbines
• Photovoltaic (PV) Systems
• +۶ مورد دیگر

Frede Blaabjerg is a Professor at Aalborg University (AAU Energy) , affiliated with the Faculty of Engineering and Science . Since 1998, he has pioneered power electronics research in applications such as wind turbines , photovoltaic (PV) systems , reliability engineering , and Power-2-X technologies. Education : PhD in Electrical Engineering (1995, Aalborg University) Honorary Degrees : Honoris Causa at University Politehnica Timisoara (2017) and Tallinn Technical University (2018) His research focuses on power electronics control , system optimization , and reliability for renewable energy and electric mobility . Recent work includes grid-forming converters , virtual synchronous generators , and smart EV charging systems. Key publication trends span 15+ years , with over 3,733 peer-reviewed articles and 900+ journal papers in power electronics , renewables , and energy storage . Notable book series: Control of Power Electronic Converters and Systems (4 volumes, Elsevier). Scientific Awards : 46 IEEE Prize Paper Awards 2020 IEEE Edison Medal 2019 Global Energy Prize 2014 IEEE William E. Newell Power Electronics Award Leadership Roles : Editor-in-Chief, IEEE Transactions on Power Electronics (2006–2012) Chairman, Danish Council for Research and Innovation Policy (2020–) President, IEEE Power Electronics Society (2019–2020)

Qingguo Liمشاهده پروفایل

استاد

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +۴ مورد دیگر

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

Yiyu Yaoمشاهده پروفایل

استاد

• Three-way Decisions
• Granular Computing
• Rough Sets
• +۸ مورد دیگر

Yiyu Yao is a Professor in the Department of Computer Science at the University of Regina, Faculty of Science. He holds a B.Eng. from Xi'an Jiaotong University and earned both his M.Sc. and Ph.D. from the University of Regina. His office is located in College West 308.6, and he can be reached at Yiyu.Yao@uregina.ca or by phone at (306) 585-5226. Dr. Yao's research spans multiple interconnected domains in intelligent systems. His primary focus is on three-way decisions, which serves as a unifying framework for his work in granular computing, rough sets, and decision-theoretic models. He has developed significant theoretical contributions to decision-theoretic rough sets (DTRS) and probabilistic rough sets, creating bridges between uncertainty management and practical decision-making applications. His work extends to web intelligence, information retrieval systems, and multiview data analysis, where he applies his theoretical frameworks to real-world problems in data science and artificial intelligence. Analysis of Dr. Yao's recent publications reveals a strong continuing focus on three-way decision theory, with increasing applications across diverse domains. His work demonstrates evolution from foundational theoretical contributions to sophisticated applications in multi-criteria decision making, conflict analysis, and explainable AI. The research shows integration of granular computing principles with modern machine learning techniques, particularly in handling uncertainty and developing interpretable models. Recent publications indicate growing interest in the intersection of three-way decisions with fuzzy sets, shadowed sets, and cognitive approaches to data analysis. Dr. Yao has mentored numerous graduate students and has hosted many visiting scholars, primarily from Chinese institutions including Nanjing University Posts and Telecommunication, Harbin Normal University, Shaanxi Normal University, and others. He serves as Area Editor on Rough Sets for the International Journal of Approximate Reasoning and as Associate Editor for Information Sciences. He is also Associate Editor-in-Chief for the Journal of Emerging Technologies in Web Intelligence and serves on multiple editorial boards including LNCS Transactions on Rough Sets and Web Intelligence and Agent Systems. He has organized significant conferences including the International Joint Conference on Rough Sets (IJCRS 2017) and served on the Steering Committee for the International Symposium on Fuzzy and Rough Sets.

Swati Aggarwalمشاهده پروفایل

استاد

• Artificial Intelligence
• AI in Health and Medicine
• AI in Society
• +۶ مورد دیگر

Swati Aggarwal is a Professor in Artificial Intelligence at the Faculty of Logistics, Molde University College (HiMolde). Her research focuses on AI applications in healthcare, ethics, cognitive development, and neural networks. She holds a PhD in Neutrosophic Neural Networks, a Master's in Information Technology, and a Bachelor's in Computer Science and Engineering. Previously, she was a Marie Curie Postdoc Fellow at NTNU, working on AI models for cognitive assessment in infants (AIM_COACH project). Research Interests - AI in Health/Medicine - Ethics in AI and Societal Impact - EEG/BCI for Cognitive Assessment - Machine Learning and Deep Learning Publications Her recent work spans AI ethics, BCI applications, adversarial attacks, and healthcare diagnostics. Notable contributions include EEG-based infant perceptual monitoring (2025) and malaria detection via EfficientNet (2023). She also explores cross-lingual adversarial robustness and blockchain in hospitality systems. Labs/Teams - ABC-AI: Applied, Basic, and Conscientious AI Group - Virtual Technologies and Learning Research Group

Christophe Viavatteneمشاهده پروفایل

دانشیار

• Flood Risk Management
• Water and Environmental Sustainability
• Geographic Information Systems (GIS)
• +۴ مورد دیگر

Christophe Viavattene is an Associate Professor of Environmental Sustainability at Middlesex University , specializing in flood risk management and urban resilience. He has spent 17 years at the Flood Hazard Research Centre , leading interdisciplinary research across economic and physical sciences in water and flood risk. Research Focus: Flood vulnerability (coastal, urban, fluvial), GIS tools (INDRA model), Multi-Coloured Manual (MCM) methodology, mental health impacts of flooding, groundwater flood risk in arid regions, and air quality risk assessment. Teaching: Programme Leader for the MSc Sustainability and Environmental Management , teaching problem-based sustainability, GIS, and environmental monitoring. Key Collaborations: EU FP7 RISC-KIT, EU CONHAZ, Environment Agency UK projects. Recent Publications: Bayesian flood models, coastal hotspots analysis, and groundwater flood damage methodologies. His work bridges academic research with practical applications, including flood storage compensation frameworks and vulnerability indicator libraries.

Mingsheng Yingمشاهده پروفایل

استاد

• Quantum Computation
• Quantum Programming
• Model-checking Quantum Systems
• +۵ مورد دیگر

Mingsheng Ying is a Distinguished Professor and Research Director of the Centre for Quantum Computation and Intelligent Systems (QCIS) at the Faculty of Engineering and Information Technology, University of Technology Sydney, Australia. He also holds the position of Cheung Kong Professor at the State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, China. Professor Ying graduated from the Department of Mathematics, Fuzhou Teachers College, Jiangxi, China, in 1981. His primary research interests span quantum computation (particularly quantum programming and model-checking quantum systems), programming theory and formal methods, and the foundations of artificial intelligence (focusing on logic and uncertainty). As an author of the books "Foundations of Quantum Programming" (Elsevier - Morgan Kaufmann 2016) and "Topology in Process Calculus: Approximate Correctness and Infinite Evolution of Concurrent Programs" (Springer-Verlag, 2001), he has published over 100 papers in top international journals and conferences. His recent research publications demonstrate a strong focus on quantum programming languages, verification techniques for quantum systems, and the theoretical foundations of quantum computation. The trend in his work shows increasing emphasis on formal verification methods for quantum systems, particularly model-checking techniques for quantum Markov chains and quantum processes. His research bridges theoretical computer science with quantum information theory, creating frameworks for reliable quantum software development. Editorial Board, Artificial Intelligence, Elsevier, Amsterdam Editorial Board, Fuzzy Sets and Systems, Elsevier, Amsterdam Vice President, International Fuzzy Systems Association (elected in 2005) Program Chair, IFSA 2005, World Congress of International Fuzzy Systems Association Chairman, Chinese Association of Fuzzy Systems and Mathematics Professor Ying has secured significant research funding including multiple Australian Research Council Discovery Projects such as "Model-checking quantum Markov chains: towards verification techniques for quantum cryptographic systems" (2013-2015) and "Process algebra approach to distributed quantum computation and secure quantum communication" (2011-2013). His work has also received funding from the National Natural Science Foundation of China and Tsinghua University. At QCIS, he leads research in quantum software theory and methodology, with applications in quantum cryptography and secure communication.

Ying Caiمشاهده پروفایل

دانشیار

• AI
• Machine Learning
• Data Science
• +۴ مورد دیگر

Ying Cai is an Associate Professor in the Department of Computer Science at Iowa State University, joining in 2003 after earning his Ph.D. in Computer Science from the University of Central Florida (2002). His research focuses on AI, machine learning, data science, cybersecurity, privacy protection, and database systems. He leads projects funded by the Air Force Research Laboratory, including work on authentication data structures for rank-aware queries, requiring U.S. citizenship and expertise in linear algebra/cryptography. Dr. Cai’s work spans cybersecurity (e.g., adversarial example defense, secure secret sharing), spatio-temporal systems (e.g., traffic risk prediction, check-in time modeling), and healthcare AI (e.g., cervical spine diagnosis with transformers). His publications emphasize practical applications of ML in privacy, security, and distributed systems. Professional roles include Associate Editor for Multimedia Tools and Applications (since 2009), Co-chair for COMPSAC TAIN/NCIW symposium (2014–2017), and TPC Chair for Mobilware 2010. His service includes contributions to INFOCOM, ICDCS, and MDM conferences. Current research opportunities exist for graduate students with strong programming/math skills, particularly in cryptography and linear algebra. He emphasizes interdisciplinary work, such as bridging AI with social sciences via large language models.

• Robotics
• Transfer Learning
• Neuro-fuzzy Systems
• +۲ مورد دیگر

C. S. George Lee is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering, located in West Lafayette. His research focuses on Robotics, Transfer Learning, Neuro-fuzzy Systems, Automatic Controls, and Computer Engineering. He holds a BSEE (1973), MSEE (1974) from Washington State University, and a PhD (1978) from Purdue University. His work integrates computational intelligence with AI, robotics, and education technology, emphasizing human-machine co-learning models and bilingual systems. His contributions span domains like quantum computing, generative AI, and knowledge graph applications. He leads the Art Lab at Purdue and has published extensively on topics ranging from humanoid robotics to cross-cultural educational platforms. His research areas include developing intelligent agents for edutainment, robotic assistants for student learning, and advanced machine learning techniques. Notable trends in his publications involve computational intelligence applied to bilingual language models (e.g., Taiwanese/English co-learning), quantum-based AI systems, and human-centric robotics. He has explored applications in healthcare (e.g., blood donor analysis), autonomous navigation, and game AI (e.g., Go). His work often bridges theoretical advancements with real-world implementations, such as Java software tools for motor activity assessment (JKinect) and AI-driven platforms for skill evaluation. Lee's research emphasizes interdisciplinary collaboration, with contributions to IEEE conferences and cross-institutional projects. His lab develops tools for adaptive e-learning, robotic task performance evaluation, and human pose estimation using neural networks. Despite prolific publishing, no specific grants or awards are explicitly mentioned in the provided text. His work continues to explore the intersection of human intelligence and smart machines through platforms like Metaverse integration and BCI (Brain-Computer Interface) applications.

• Computer Learning
• Fuzzy Sets and Systems
• Decision Support Systems
• +۳ مورد دیگر

Prof. Sadettin Emre Alptekin is a full Professor of Industrial Engineering at Galatasaray University, Faculty of Engineering and Technology, where he also serves as Vice Dean. Since joining the university as a research assistant in 2000, he has steadily advanced through the academic ranks, becoming an Assistant Professor (2006–2010), Associate Professor (2010–2023), and finally Professor in 2023. Education: PhD (Dr), Industrial Engineering, Istanbul Technical University, Institute of Science and Technology, 2001–2006 MSc, Industrial Engineering, Galatasaray University, Faculty of Engineering and Technology, 1999–2001 BSc, Industrial Engineering, Istanbul Technical University, Faculty of Management, 1995–1999 Languages: Advanced English (C1), Upper-Intermediate French (B2), Advanced German (C1) Research Interests: Prof. Alptekin’s research focuses on Computer Learning , Fuzzy Sets and Systems , and Decision Support Systems . His work integrates artificial intelligence, machine learning, and soft-computing techniques to solve complex industrial and managerial problems in areas such as supply chain management, quality function deployment, blockchain adoption, and mental-health prediction. Publication Trends: Across more than 50 refereed publications, Prof. Alptekin has consistently explored hybrid intelligent models that combine fuzzy logic, machine learning, and multi-criteria decision-making. Recent articles emphasize deep-learning-based anomaly detection in industrial time-series data, blockchain adoption in supply chains, and machine-learning applications in subjective well-being and mental-health modeling. Scientific Awards & Honors: No specific awards or medals are listed in the provided documents. Research Leadership & Funding: Since 2008 he has been the principal investigator (executive) of 12 nationally funded projects, covering topics such as Industry 4.0 sub-system design, Internet of Things applications, artificial neural networks in organizational decision-making, big-data analytics, and strategic decision processes. Graduate Advising: He has formally supervised at least 8 master’s theses and numerous undergraduate projects. Representative thesis titles include Gaussian-process-regression-based man-hour prediction, machine-learning-driven human-behavior modeling, recommender-system design for e-commerce, thyroid-nodule diagnosis from scintigraphic images, software-effort estimation via neural networks, spreadsheet heuristics for joint-replenishment problems, cross-selling decision systems in insurance, and profitability analyses of Turkish banks under disinflation. Laboratories & Teams: While no dedicated laboratory name is disclosed, his continuous role as Vice Dean and principal investigator implies active leadership of the Industrial Engineering department’s research clusters in intelligent systems and decision support technologies.

Hyuck Jin Parkمشاهده پروفایل

استاد

• Landslide Hazard Assessment
• Geotechnical Engineering
• Machine Learning Applications
• +۵ مورد دیگر

Hyuck Jin Park is a Full Professor in the Department of Energy Resources and Geosystems Engineering at Sejong University, South Korea, where he has been teaching and conducting research since 2003. With a Ph.D. in Engineering Geology from Purdue University, his expertise spans geotechnical engineering, landslide analysis, and geospatial technologies. Professor Park has built a distinguished career in landslide hazard assessment, combining traditional geotechnical approaches with modern machine learning techniques to improve prediction accuracy and risk management. His educational background includes: B.S. in Geology from Yonsei University (1990) M.S. in Geophysics from Yonsei University (1993) Ph.D. in Engineering Geology from Purdue University (2011) Professor Park's research focuses on the spatial and temporal probability of landslide occurrence, utilizing fuzzy logic, probabilistic analysis, GIS, Monte Carlo simulation, and machine learning for landslide hazard assessment. His work integrates physically based models with statistical approaches to better understand landslide mechanisms and improve prediction capabilities. He has made significant contributions to the development of methodologies that account for geological uncertainties in hazard assessment, with applications ranging from rock slope stability to rainfall-induced shallow landslides. His recent publications demonstrate a clear trend toward integrating explainable artificial intelligence with traditional geotechnical approaches for natural hazard assessment. Professor Park's work increasingly focuses on making machine learning models transparent and interpretable while maintaining high predictive accuracy. The research spans multiple hazard types including landslides, earthquakes, and floods, with a growing emphasis on climate change impacts and data-scarce environments. With an h-index of 28 and over 3,421 citations, Professor Park has established himself as a leading researcher in his field. His work has been published in high-impact journals including Engineering Geology, Landslides, and Catena, reflecting the significance and quality of his contributions to geotechnical engineering and natural hazard assessment. Professor Park has mentored numerous researchers through collaborative projects and has secured funding for his innovative work in landslide prediction and hazard assessment. His research has involved significant international collaboration, particularly with researchers from Malaysia, Australia, and Yemen, addressing landslide and flood risks in diverse geographical contexts. He leads research activities within the Department of Geoinformation Engineering at Sejong University and has contributed to the development of specialized tools like DEWS (Distance, Elevation, Watershed, and Slope unit) for landslide early warning systems.

Chee Kiat Seowمشاهده پروفایل

دانشیار

• Cyber Physical Security
• Wireless Communication Localization
• Internet of Things (IoT) with AI/ML
• +۳ مورد دیگر

Dr. Chee Kiat Seow is an Associate Professor at the University of Glasgow's School of Computing Science. He holds a PhD from Nanyang Technological University (NTU) and an MSc from the National University of Singapore (NUS). His research focuses on cyber-physical security, wireless communication localization, and IoT systems leveraging AI/ML. He has led projects valued in the millions, winning awards like the IEEE Best Student Paper and National Instruments Engineering Impact Awards. Education: PhD (NTU), MSc (NUS) Research: Specializes in UWB positioning, spoofing detection, and IoT integration with 5G/GNSS. Teaching: Courses include Big Data, Software Engineering, and Data Analytics. His recent work addresses NLOS mitigation in indoor localization and cyber-physical security threats. Over 63 publications span journals like IEEE Transactions and conferences such as IPIN and WF-IoT. Supervised 6+ PhD/MSc students on topics like autonomous robotics and AI-driven localization. Grants: Includes $853K for 5G-X Smart Building projects and $797K for GNSS signal authentication. Awards: IEEE PIERS Best Student Paper (2019), NI Engineering Impact Awards (2015-2016). He advises on IoT and cybersecurity for organizations like ARTC and National Instruments. Active in IEEE Signal Processing and Computer Society.

• Artificial Intelligence
• Strategic Reasoning in Multi-Agent Systems
• Formal Verification
• +۶ مورد دیگر

Aniello Murano is a Professor of Computer Science at the Department of Electrical Engineering and Information Technologies , University of Naples Federico II. He serves as Scientific Director of the ASTREA (Automated Strategic Reasoning) Laboratory and leads cutting-edge research in Artificial Intelligence, Strategic Reasoning, Multi-Agent Systems , and Formal Verification . Research Interests : Strategic reasoning under perfect/imperfect information, specification/verification/synthesis of reactive systems, temporal/modal logics, automata theory, parity games, game theory, mechanism design, and formal languages. Notable Projects : PNRR Research Unit Coordinator (2023-2025) on Resilient AI, PRIN 2020 Unit Coordinator (RIPER: Resilient AI-Based Self-Programming and Strategic Reasoning), H2020-MSCA SEAL (Principal Coordinator). Awards & Honors : JPMorgan Faculty Research Award (2022), Royal Society Award (2016), Best Paper PRIMA (2015), INDAM Project Leader (2023), Italian Scientific Habilitation (2017-2018). Students & Postdocs : Supervised 6 PhD students (e.g., Silvia Stranieri, Vadim Malvone) and mentored postdocs such as Munyque Mittelmann and Bastien Maubert. Laboratory : Leads ASTREA Lab, focusing on automated strategic reasoning and resilient AI systems.

• Complex networks
• Industrial electronics
• AI and machine learning
• +۴ مورد دیگر

Associate Professor Fengling Han is affiliated with RMIT University's School of Computing Technologies in Melbourne, Australia. He holds the rank of Associate Professor since January 2022. His research focuses on complex networks, industrial electronics, AI/machine learning, and network security. Notable contributions include steganography frameworks for healthcare data, sliding mode control for energy systems, and blockchain applications in surveillance and voting systems. His work spans interdisciplinary areas such as renewable energy integration, battery management systems, and privacy-preserving recommendation systems. He has supervised numerous projects, including AI-driven chatbots, medical imaging watermarking, and peer-to-peer energy trading systems. Han's service roles include conference reviewing and committee memberships in international conferences like IEEE and ISMST. Research Interests: His expertise spans electrical engineering, control systems, and AI applications. Key areas include battery management, cybersecurity, and smart manufacturing. Recent projects emphasize Industry 5.0 technologies, blockchain for data integrity, and deep learning for steganalysis. Teaching and Supervision: Teaches network security, data communication, and IT infrastructure. Current supervision includes AI-powered business modeling, medical imaging tampering detection, and renewable energy sharing systems. Over 14 research projects are documented, reflecting his interdisciplinary impact. Awards and Recognition: While specific awards are not listed, his extensive publications (over 150 outputs) and high citation counts (e.g., 119 citations for the Industry 5.0 survey) highlight his scholarly contributions.