جستجوی استاد راهنمای دانشگاهی

Michio Sugeno is a distinguished Professor at Tokyo Institute of Technology's Graduate School of Information Science and Engineering, Department of Computational Intelligence. With a career spanning over four decades, he has established himself as a leading figure in fuzzy systems and computational intelligence. His research interests encompass Fuzzy Systems, Computational Intelligence, Nonlinear Control, Choquet Integral theory, Brain-Computer Interfaces, and Linguistic Computing. Sugeno's work has fundamentally shaped modern fuzzy control theory, particularly through his development of the Takagi-Sugeno fuzzy model which has become a standard approach in industrial applications. Analysis of his recent publications reveals a continued focus on piecewise nonlinear modeling, stability analysis of fuzzy systems, and the application of Choquet calculus to various computational problems. His work demonstrates a consistent trajectory from theoretical foundations to practical implementations in control systems and intelligent computing. IEEE Pioneer Award in Fuzzy Systems IFSA Fellow Emanuel R. Piore Award Sugeno has mentored numerous researchers who have become prominent in their own right, including Tadanari Taniguchi, Luka Eciolaza, and Anh-Tu Nguyen. His laboratory has been instrumental in developing novel approaches to nonlinear control systems using piecewise bilinear models and fuzzy logic. Current research directions include brain-computer interfaces using EEG analysis and the development of everyday language computing systems that enable more natural human-computer interaction.

• Fuzzy Systems
• Computational Intelligence
• Nonlinear Control
• +۴ مورد دیگر

Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۱۲ مورد دیگر

Dr. Ihab Hijazi is a researcher at the Chair of Geoinformatics , Technical University of Munich, specializing in 3D geospatial modeling and BIM-GIS integration . He teaches courses such as CAFM - Facility Management and GIS and contributes to advancing CityGML standards and urban digital twins . Research Focus : Hijazi’s work bridges Building Information Modeling (BIM) and Geographic Information Systems (GIS) , emphasizing interoperability , semantic transformation , and smart city infrastructures . His projects include the Smart Sustainable Districts (SSD) and Smart District Data Infrastructure , focusing on urban energy systems and data harmonization. Publications highlight his contributions to CityGML utility networks , urban growth simulations , and semantic modeling frameworks. He actively develops 3DCityDB and participates in standardization initiatives like the DIN Spec 91607 Digital Twin for Cities .

• Geoinformatics
• 3D Geospatial Modeling
• BIM-GIS Integration
• +۴ مورد دیگر

Guo Li is affiliated with the Beijing Institute of Technology, School of Management and Economics. Their research spans computer vision, optimization algorithms, signal processing, and machine learning. Collaborations include work on image super-resolution, sensor networks, and energy systems. Publications are distributed across journals like Comput. Electron. Agric. , IEEE Trans. Circuits Syst. , and Entropy . Research interests focus on computational methods for image processing, algorithm design, and interdisciplinary applications in agriculture and energy. Recent work emphasizes lightweight neural network architectures, sparrow search algorithms, and thermodynamic modeling in materials science. Notable contributions include advancements in citrus fruit detection, fatigue life assessment of superalloys, and load forecasting techniques. Active in international conferences such as CVPR, ICC, and NSDI, with a strong publication record since 1998.

• Computer Science
• Electrical Engineering
• Signal Processing
• +۳ مورد دیگر

Núria Agell Jané is a Full Professor at ESADE Business School , Universitat Ramon Llull, specializing in Artificial Intelligence and Decision-Making Systems. She leads the JUICE (Judgements and Decisions in the Market Place) research group and the ESADE D3 - Institute for Data-Driven Decisions . Doctorate in Applied Mathematics (Qualitative Reasoning Modelling), UPC-BarcelonaTech Bachelor's in Mathematics, University of Barcelona Her research focuses on Artificial Intelligence , Decision-Making Systems , and Fuzzy Logic , with applications in Business, Marketing, and Sustainability. Recent publications emphasize Hesitant Fuzzy Linguistic Term Sets , Consensus Modeling , and AI in Sustainable Development . She coordinates multiple publicly and privately funded projects applying AI to Business and Marketing challenges. As PhD Programme Director (2005-2013) and current Department Director of Operations, Innovation and Data Sciences , she has shaped academic and research strategies at ESADE. Her work spans collaborations with institutions like LAAS-CNRS (France) and University of Edinburgh Business School , with over 40 journal publications and 50 conference contributions. She has directly supervised 11 PhD students in AI and Decision Sciences.

• Artificial Intelligence
• Decision-Making Systems
• Fuzzy Logic
• +۵ مورد دیگر

Surajit Chaudhuri is a Researcher at Microsoft , with a career spanning decades in database systems and data management . He has received the prestigious SIGMOD Edgar F. Codd Innovations Award (2011) for his contributions to query optimization , index tuning , and data lakes . Research Interests : His work focuses on database tuning , approximate query processing , fuzzy similarity joins , automated data transformations , and machine learning integration for scalable data systems. Recent Publications : In 2025, his research includes Auto-Test for unsupervised error detection in tables, Esc for budget-aware index tuning, and MMTU for multi-task table understanding benchmarks. Earlier works in 2024–2023 address spreadsheet formula recommendation , low-overhead index filtering , and time-series pattern recognition . Scientific Impact : He has co-authored influential papers in SIGMOD , VLDB , and IEEE Transactions , shaping practices in cloud databases , query optimization , and self-service BI . His collaborations span institutions like Microsoft, MIT, and ETH Zurich.

• Database Systems
• Query Optimization
• Index Tuning
• +۷ مورد دیگر

Dr. Wolfgang Eppler is a Researcher at the Institute for Technology Assessment and Systems Analysis (ITAS) at the Karlsruhe Institute of Technology (KIT), where he has been working since 2023. His research focuses on the societal implications of digital technologies, particularly artificial intelligence and digital transformation. Prior to his current position, he served in various leadership roles related to staff representation at KIT and its predecessor institutions. Dr. Eppler received his education at the University of Stuttgart, where he completed his computer science studies from 1980 to 1986. He then worked as a research assistant at the University of Karlsruhe and the Research Center for Information Technology (FZI) from 1987 to 1993, during which time he earned his doctorate on the topic of "Pre-structuring of neural networks with fuzzy logic." Dr. Eppler's research spans multiple domains at the intersection of technology and society. His early work focused on neural networks, fuzzy logic, and their applications in areas such as electronic noses, medical imaging, and high-energy physics data processing. In recent years, his research has shifted toward technology assessment, particularly examining the societal impacts of artificial intelligence, digital transformation, and the governance of emerging technologies. His interdisciplinary approach combines technical expertise with social science perspectives to address complex questions about the role of technology in society. Analysis of Dr. Eppler's recent publications reveals a strong focus on the ethical, governance, and societal implications of artificial intelligence. His 2024-2025 work addresses critical issues such as EU AI regulation, generative AI for technology assessment, the grounding of large language models, and algorithmic bias. This represents an evolution from his earlier technical work on neural networks and data processing systems toward more policy-oriented research that bridges technical and social dimensions of technological change. Throughout his career, Dr. Eppler has been actively involved in institutional governance and staff representation. From 2009 to 2023, he served as Chairman of the Staff Council at KIT, and from 2005 to 2009 as Chairman of the Works Council at the Karlsruhe Research Center. His publications on university governance, particularly regarding the KIT merger and models for democratic science institutions, reflect his practical experience and theoretical interest in participatory decision-making in academic settings. Dr. Eppler is a member of the Research Group "Digital Technologies and Social Change" at ITAS, where he contributes to projects examining the societal dimensions of technological innovation. His interdisciplinary background enables him to bridge technical and social science perspectives in assessing emerging technologies.

• Artificial Intelligence
• Digital Transformation
• Technology Assessment
• +۵ مورد دیگر

Prof. Dr. rer. nat. habil. Detlef Hauke Mache is a full Professor of Mathematics and Applied Mathematics at the TH Georg Agricola University of Applied Sciences in Bochum, Germany, within the Faculty of Electrical Engineering, Information Technology, and Industrial Engineering. Since 2003, he has held the chair for Applied Mathematics with a focus on Constructive Approximation. Education: Diploma in Mathematics and Computer Science, University of Dortmund (1988) Doctoral degree (Dr. rer. nat.) in Mathematics, University of Dortmund (1991) Habilitation (Dr. habil.) in Mathematics, University of Dortmund (1997) Research Interests: Prof. Mache's research spans Constructive Approximation Theory , emphasizing the development and analysis of approximation methods. He explores Radial Basis Function (RBF) Networks and their applications in neural networks and fuzzy logic. His work integrates theoretical foundations with practical algorithms, particularly in numerical analysis and approximation techniques. Key areas include: Neural Networks and Fuzzy Logic Systems Quasi-Interpolation Methods Orthogonal Polynomial Expansions Integral Transforms and Convolution Structures Publications and Editorial Contributions: Prof. Mache has authored over 30 peer-reviewed papers and edited several volumes in approximation theory. His research trends indicate a focus on advancing approximation methods through theoretical insights and practical applications in neural networks and computational intelligence. Scientific Awards and Honors: While no specific awards are listed, his extensive editorial roles and habilitation qualification signify recognition in his field. Teaching and Advising: He teaches courses in Higher Mathematics, Applied Mathematics, Differential Equations, and Numerical Analysis. While no specific students are named, his long-standing academic positions suggest significant contributions to graduate education. Laboratories and Teams: As a professor in the Faculty of Electrical Engineering and Information Technology, he likely collaborates with interdisciplinary teams, though no specific labs are mentioned.

• Constructive Approximation
• Radial Basis Function Networks
• Neural Networks
• +۴ مورد دیگر

Muhammad Waqas is a researcher affiliated with COMSATS University Islamabad , where he holds a position in the Department of Meteorology under the School of Applied Sciences and Humanities . His academic collaborations span institutions like Bahria University, National University of Technology, and University of Bahrain, indicating a multidisciplinary approach. Research interests include Mechanisms for integrating fuzzy logic and machine learning in health monitoring Application of deep learning to medical imaging and clinical diagnostics Development of smart sensors for wearable technology in biomechanics Analysis of social media data for public health surveillance and sentiment analysis Investigation of digital citizenship and ICT leadership in educational contexts Trends in his 15 most recent publications (2025-2024) reveal a focus on medical diagnostics (e.g., monkeypox, breast cancer), smart infrastructure (e.g., sensor placement, structural health monitoring), and social media analytics for health and behavioral insights. These works leverage machine learning , fuzzy systems , and multi-objective optimization .

• Computer Science
• Machine Learning
• Health Monitoring Systems
• +۷ مورد دیگر

Mohit Kumar is an außerplanmäßiger Professor of Computational Intelligence in Automation at the Institute of Automation Technology, University of Rostock. He concurrently serves as a Key Researcher in Data Science at the Software Competence Center Hagenberg, Austria, and as a Visiting Professor at the Georg-August-Universität Göttingen. His research centers on Trustworthy Artificial Intelligence frameworks, specifically developing Explainable AI, Privacy-Preserving AI, and Transferrable AI methodologies. He pioneers fuzzy logic applications in machine intelligence and creates AI-driven analytical systems for complex data, signals, and image processing. This work is rigorously grounded in probability theory, statistical modeling, estimation theory, and robust adaptive filtering techniques. At the Software Competence Center Hagenberg, he leads digitalization solution development through theoretically sound approaches and extensive real-world experimentation to solve critical industrial and societal challenges.

• Trustworthy Artificial Intelligence
• Explainable AI
• Privacy-Preserving AI
• +۱۰ مورد دیگر

Dr. Peyman Badakhshan is a Researcher at the Chair of Information Systems and Business Process Management at the University of Münster. His expertise spans Business Process Management, Process Mining, and Industrial Engineering. Educational background includes a PhD in Business/Managerial Economics from Universität Liechtenstein, an MSc in Information Systems, and an M.Eng. in Industrial Engineering. Research focuses on operationalizing process analytics across domains like supply chain, healthcare, and manufacturing. Recent work develops frameworks for agile BPM and process mining tools, with applications in medical training, emergency services, and industrial process optimization. Publications demonstrate consistent innovation in mapping business process landscapes, developing diagnostic instruments, and creating large-scale datasets for academic use. Research integrates methodologies from decision analysis, fuzzy logic, and pattern recognition to solve complex operational challenges.

• Business Process Management
• Process Mining
• Industrial Engineering

James F. Peters is a faculty member in the Department of Electrical and Computer Engineering at the University of Manitoba, Winnipeg, Canada. His research lies at the intersection of computational topology, proximity theory, rough sets, and digital image analysis, with applications in computer vision, pattern recognition, and biologically-inspired computing. He has made foundational contributions to the theory of near sets and computational proximity, publishing extensively in journals and book series by Springer. His research interests include computational proximity, near sets, rough sets, digital image analysis, pattern recognition, and topological models of perception. These are evident from his numerous publications in theoretical and applied computer science, often in collaboration with researchers such as Andrzej Skowron, Sheela Ramanna, and Arturo Tozzi. His work spans mathematical foundations, computational models, and real-world applications in biomedical imaging and rehabilitation systems. The recent articles (2017–2025) show a strong trend toward integrating topology, physics, and neuroscience in the analysis of digital images and brain activity. Topics include proximal nerves, optical vortices, thermodynamics of emotions, and entropy in cosmology, indicating a broad interdisciplinary approach. His publications frequently appear in journals such as Entropy , Information Sciences , and Transactions on Rough Sets , as well as in Springer’s Lecture Notes in Computer Science and Intelligent Systems Reference Library series. He has authored or co-authored several books and special issues, notably in the Transactions on Rough Sets series, and has contributed to encyclopedic works on rough sets and computational intelligence. His editorial and collaborative roles highlight his leadership in the rough and near sets research community. Dr. Peters has advised or collaborated with several researchers, though specific student names are not listed in the provided text. He has been involved in projects related to adaptive learning, telerehabilitation gaming systems, and image classification using tolerance near sets. His work often involves grants and interdisciplinary teams, especially in computational intelligence and biomedical applications. He is associated with research groups and labs focused on computational intelligence, rough sets, and digital image analysis, often in collaboration with the University of Warsaw and other international institutions. His ongoing work continues to explore the mathematical foundations of perception and proximity in both artificial and biological systems.

• Computational Proximity
• Near Sets
• Rough Sets
• +۱۳ مورد دیگر

Khalil Esper is a Researcher at the Department of Computer Science, Faculty of Engineering, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), where he works at the Chair of Computer Science 12 (Hardware-Software Co-Design). His research focuses on verification, energy optimization, and runtime requirement enforcement in embedded systems and MPSoCs. His educational background includes: Informatics Engineering from Aleppo University, Syria (2010-2015) European Master in Embedded Computing Systems (EMECS) from Rhineland-Palatinate University of Technology Kaiserslautern-Landau (Germany) and Norwegian University of Science and Technology (Norway) (2017-2019) Esper's research interests center around verification and model checking, energy optimization on MPSoC, real-time systems and embedded systems, and autonomic computing. His work particularly focuses on runtime requirement enforcement mechanisms for non-functional properties in multi-processor systems-on-chip, with applications extending to medical devices and human-robot interaction systems. He has developed approaches using finite state machines, reinforcement learning, and evolutionary algorithms to ensure system properties are maintained during execution. His publication record shows a strong trend toward applying formal methods and runtime enforcement techniques to increasingly complex systems, with recent work expanding into safety-critical applications like orthoses and human-robot interaction. The interdisciplinary nature of his research bridges computer science, embedded systems engineering, and biomedical applications. Esper has supervised multiple theses including: Sascha H.: Runtime Requirement Enforcement of Non-Functional Requirements on MPSoCs Using Fuzzy Logic (2022) Iana S.: Feedback-Based Control of Non-functional Program Execution Properties on Linux (2023) Philipp L.: Runtime Requirement Enforcement of Functional and Non-Functional Requirements of a Knee Orthosis Based on a Digital Twin (2024) Avinash N.: Runtime Requirement Enforcement of Safety Properties of an Ankle Orthosis Based on a Digital Twin (2024) Zhiyi T.: Generation of Environment FSMs Using Machine Learning Techniques (2025) Moustafa A.: Runtime Requirement Enforcement of Safety Properties of Human-Robot Interaction Based on a Digital Twin (2025) Florian K.: Runtime Requirement Enforcement of Safety Properties of Human-Robot Interaction (2025) He has been actively teaching courses on Approximate Computing and Embedded Systems since the 2021/2022 academic year, demonstrating his commitment to academic instruction alongside his research activities. Esper is involved in the InvasIC research project, part of the DFG Transregional Collaborative Research Center 89 on Invasive Computing, which explores novel approaches to resource management in parallel computing systems.

• Verification and Model Checking
• Energy Optimization
• Real-time Systems
• +۴ مورد دیگر

Lakhmi C. Jain is a distinguished academic affiliated with the University of South Australia. As a Professor, she has made significant contributions to the fields of Artificial Intelligence, Computational Intelligence, and Fuzzy Systems. Her research spans neural networks, decision support systems, robotics, and data analysis, with a focus on interdisciplinary applications. Her career includes over 445 publications, including books like Complex Networks in Software, Knowledge, and Social Systems (2019) and E-Learning Systems - Intelligent Techniques for Personalization (2017). She has held editorial roles in journals such as the International Journal of Intelligent Decision Technologies (IDT) and the Journal of Intelligent & Fuzzy Systems. Jain's work emphasizes practical applications of computational intelligence, including efforts in software development, biomedical signal processing, and multi-agent systems. She has collaborated extensively with researchers globally, contributing to advancements in AI-driven technologies and decision-making frameworks.

• Artificial Intelligence
• Computational Intelligence
• Neural Networks
• +۵ مورد دیگر

Tim G. Schweisfurth is a Full Professor in Organizational Design and Collaboration Engineering at the School of Management Sciences and Technology, Hamburg University of Technology (TUHH), a position he has held since January 2023. Previously, he served as Associate Professor in High-Tech Business at the University of Twente (2021-2022) and Associate Professor of Technology and Innovation Management at the University of Southern Denmark (2018-2020). His academic credentials include: PhD from Hamburg University of Technology (TUHH) venia legendi (Habilitation) from Technical University of Munich (TUM) Professor Schweisfurth's research centers on three interconnected domains: (1) digital and technology-driven innovation, examining how emerging technologies reshape business models; (2) venture idea generation and evaluation, investigating cognitive and social mechanisms behind opportunity identification; and (3) distributed and collaborative innovation, studying cross-boundary knowledge flows. His work bridges theoretical rigor with practical application through partnerships with Siemens, Osram, Audi, and Panasonic, focusing on real-world innovation challenges in corporate settings. Analysis of his recent publications reveals a pronounced shift toward micro-foundational studies of innovation processes, with growing emphasis on digital tools (e.g., internal crowdfunding platforms), cognitive dynamics in idea evaluation, and sustainability implications. His 2023-2025 work increasingly integrates AI ethics, organizational hierarchy effects, and configurational approaches to innovation performance, while maintaining strong connections to user innovation theory. He currently serves as Editor-in-Chief of Creativity and Innovation Management and Advisory Editor for Research Policy , shaping discourse in top innovation journals. Industry collaborations span technology implementation studies with EWE and Mammut, plus Panasonic-funded research on distributed innovation ecosystems. Leading the Organizational Design and Collaboration Engineering research group at TUHH, he develops frameworks for engineering collaboration structures that enhance innovation capacity, utilizing mixed-method approaches including natural experiments and large-scale field studies in manufacturing and technology sectors.

• Innovation and Entrepreneurship
• Digital Innovation
• Technology-Driven Innovation
• +۴ مورد دیگر