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Michael BaldeaView profile

Associate Professor

• Process Systems Engineering
• Energy Systems
• Nonlinear Optimization
• +3 more

Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.

• Civil Engineering
• Structural Engineering
• Computational Engineering
• +3 more

Professor Haijiang Li is a Chair in BIM for Smart Engineering at Cardiff University's School of Engineering. His roles include leading the Computational Mechanics and Engineering AI Research Group, directing the BIM for Smart Engineering Centre, and overseeing the BIM MSc programme. He holds editorial roles for journals like Construction Innovation and Automation in Construction , and chairs the European Group of Intelligent Computing in Engineering (EG-ICE). Research focuses on smart computational engineering platforms integrating BIM, AI, and big data for sustainable infrastructure. Key areas include digital twins, disaster management, and resilient urban systems. He has secured £40M in research funding, including £9M as PI, and led over 70 research staff and students. Prof. Li is a Standards Committee Technical Executive at buildingSMART, driving international BIM standards. His work includes co-authoring a book on BIM standards across China, the US, and the UK. Awards include Fellowships from the British Computer Society (FBCS) and the Higher Education Academy (FHEA). His research outputs span over 250 publications, covering topics like AI-driven bridge maintenance, ontology-based decision-making, and energy-efficient urban systems. Collaborations with industry and global partners emphasize practical applications of BIM and smart technologies.

Frede BlaabjergView profile

Professor

• Power Electronics
• Wind Turbines
• Photovoltaic (PV) Systems
• +6 more

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 LiView profile

Professor

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +4 more

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 YaoView profile

Professor

• Three-way Decisions
• Granular Computing
• Rough Sets
• +8 more

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 AggarwalView profile

Professor

• Artificial Intelligence
• AI in Health and Medicine
• AI in Society
• +6 more

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

Ying CaiView profile

Associate Professor

• AI
• Machine Learning
• Data Science
• +4 more

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
• +2 more

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.

• Decision Support Systems
• Energy Management
• Environmental Analysis
• +6 more

John Psarras is a Professor at the National Technical University of Athens (NTUA) in the School of Electrical and Computer Engineering, specifically within the Division of Industrial Electric Devices and Decision Systems. He serves as the Director of the Decision Support Systems Laboratory (DSSlab) and the University Research Institute of Communication and Computer Systems. He holds a Diploma in Mechanical Engineering (1982) and a Ph.D. in Electrical and Computer Engineering (1989), both from NTUA. His research specializes in decision support systems with applications in energy management, environmental analysis, and information systems. Key areas include: Multi-criteria analysis for energy policy and renewable integration AI-driven optimization of smart grids and building efficiency Sustainable finance mechanisms for green projects Blockchain applications in education and data security His recent publications (2023–2025) demonstrate a strong focus on AI-enhanced decision tools for energy transitions, smart infrastructure, healthcare diagnostics, and cross-border renewable cooperation, reflecting interdisciplinary innovation. He has supervised 22 PhD theses and coordinates EU-funded projects in energy policy, clean technology, and capacity building. No scientific awards are listed in available sources. He leads the Decision Support Systems Laboratory (DSSlab), advancing research in energy analytics, and directs the University Research Institute of Communication and Computer Systems, facilitating large-scale interdisciplinary collaborations.

• Computer Learning
• Fuzzy Sets and Systems
• Decision Support Systems
• +3 more

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 ParkView profile

Professor

• Landslide Hazard Assessment
• Geotechnical Engineering
• Machine Learning Applications
• +5 more

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.

Emine AyazView profile

Professor

• Electrical Engineering
• Mechanical Fault Detection
• Signal Processing
• +12 more

Emine Ayaz is a Professor at Istanbul Technical University's Department of Electrical Engineering. Her research spans fault detection in electric motors, signal processing, and nuclear power plant monitoring, with recent work integrating deep learning (e.g., dual RNN architectures) and medical applications (e.g., parasitology, plant-based wound healing). Key Collaborations : International partnerships in motor diagnostics and nuclear engineering. Projects : Led grants on high-voltage training and predictive maintenance for TEİAŞ and industrial processes. Research Trends : Recent publications emphasize neural networks for motor fault classification, coherence analysis for insulation diagnostics, and interdisciplinary work in plant biotechnology and parasitology. Labs & Teams : Involved in projects analyzing vibration signals, wavelet transforms, and sensor fusion for industrial and nuclear systems.

• Complex networks
• Industrial electronics
• AI and machine learning
• +4 more

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.

Richard GaultView profile

Lecturer

• Computer Vision
• Medical Image Analysis
• Artificial Intelligence
• +3 more

Dr. Richard Gault is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on computer vision and deep learning applied to microscopy data, particularly in medicine, health, and life sciences. He leads a team developing novel methods for medical image analysis, including histopathology and digital pathology, with applications in cancer diagnosis and environmental science. He is actively involved in teaching, having received Excellence in Teaching awards from Queen's University Belfast in 2019 and 2022. His work bridges computational intelligence and healthcare, with notable contributions to AI-driven diagnostics, stain normalization in histopathology, and multimodal data fusion. Dr. Gault's research interests include ensemble learning, fuzzy systems, and generative models like diffusion networks. He supervises multiple PhD students and has mentored graduates now working in machine learning engineering and postdoctoral research. His team’s achievements include awards such as the 2023 Best Oral Presentation at the Pan Ireland Ophthalmology Day and a 2021 Best Paper Award from his school. Key contributions include the LymphoSight AI application for detecting lymphoid structures and HistoClean , open-source software for improving CNN development in histopathology. He has been recognized as a Senior Member of IEEE and a Fellow of the Higher Education Academy. His work is supported by grants such as the R5131ECI project on 3D quantifier approximation via 2D video analysis (2019–2025). He actively engages in academic activities, including conference organization and PhD external examinations across Europe.

Dieu Tien BuiView profile

Professor

• Geospatial Artificial Intelligence
• Machine Learning
• GIS and Geomatics
• +6 more

Dieu Tien Bui is a Full Professor in the Department of Business and IT at the University of South-Eastern Norway (USN) School of Business. His research focuses on Geospatial Artificial Intelligence Machine Learning GIS and Remote Sensing Natural Hazard Modeling Environmental Problems (landslides, floods, soil salinity, biomass) . He has contributed to over 15 recent publications in journals like Science of the Total Environment , Remote Sensing , and Geomorphology , emphasizing hybrid AI models for landslide and flood susceptibility. His work spans Vietnam, India, China, and Iran with applications in climate change adaptation and disaster management. Scientific Awards: Global Highly Cited Researcher PhD Supervision: He has supervised 8 PhD students at institutions including USN, NTNU, and Vietnamese universities.