جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۶ مورد دیگر

Haitao Wang is a prominent researcher affiliated with the Chinese Academy of Sciences, specifically with the Institute of Automation and State Key Laboratory of Management and Control for Complex Systems in Beijing. His research spans multiple disciplines including artificial intelligence, computer vision, engineering systems, and geospatial information processing. With extensive publications across top-tier journals and conferences, he demonstrates significant academic impact in both theoretical and applied research domains. Wang's research interests focus on the intersection of artificial intelligence and practical engineering applications. His work in machine learning includes developing novel algorithms for distance selection, corrosion prediction models for oil and gas infrastructure, and domain-specific large language models for geological applications. In computer vision, he has made contributions to building pattern recognition, image restoration techniques, and UAV-based environmental monitoring systems. His engineering research spans fault diagnosis systems, precision mechanical design, and human-robot interaction frameworks. Analysis of his recent publications reveals a strong trend toward integrating large language models with specialized domain knowledge, particularly in geological applications (GeoProspect) and robotic task reasoning (Double-Feedback). His work increasingly focuses on practical applications in high-risk industries, emergency response systems, and environmental monitoring, demonstrating a shift toward solving real-world problems with AI technologies. His scientific contributions demonstrate excellence across multiple domains, though specific awards are not documented in the provided information. The breadth of his collaborative work across different institutions and research areas highlights his interdisciplinary approach and academic leadership. Wang's research group appears to be involved in numerous projects related to AI-driven decision support systems, particularly in nuclear safety and emergency response scenarios. His recent work on human reliability analysis frameworks suggests active involvement in high-stakes application domains where human-machine collaboration is critical.

Guy De Tréمشاهده پروفایل

دانشیار

• Computational Intelligence
• Data Quality
• Decision Support Systems
• +۷ مورد دیگر

Guy De Tré is an Associate Professor at the Department of Telecommunications and Information Processing within Ghent University's Faculty of Engineering and Architecture. He leads the Database, Document and Content Management (DDCM) research group and focuses on computational intelligence in information systems, with expertise in bi-polarity handling, uncertainty modeling, and multi-valued logic systems. Primary Affiliation: Ghent University Research Focus: Data quality, fuzzy querying, spatio-temporal modeling Key Contributions: Foundational work in possibilistic databases and explainable AI His research combines theoretical and applied approaches to information management systems. Theoretical work includes: Bipolarity and uncertainty handling in databases Multi-valued logic frameworks Interval B-tree indexing for possibilistic data Applied research spans: NoSQL database optimization Decision support systems Contextualized machine learning 3D/4D modeling for geological resources Recent publications show increasing focus on explainable AI, with multiple works on contextualized support vector machine classification and orthographic similarity measures for graph-based data representations. His work bridges database theory with practical applications in data quality assessment, medical informatics, and cultural heritage projects like the Byzantine Book Epigrams database. Research Group: Leads the DDCM group at Ghent University, specializing in: Database management innovation Content modeling techniques Fuzzy logic implementations Temporal data indexing Intelligent information systems

Barteld Kooiمشاهده پروفایل

دانشیار

• Logic
• Dynamic Epistemic Logic
• Philosophy of Mind
• +۴ مورد دیگر

Dr. Barteld Kooi is an Associate Professor in Theoretical Philosophy at the University of Groningen. He holds a PhD in Computer Science and has held a full professorship in Logic and Argumentation from 2011–2021. His research focuses on dynamic epistemic logic, paradox analysis, and the formal modeling of intelligent interaction. Key projects include his NWO VIDI grant exploring logic expressivity and his VENI project on dynamic epistemic logic applied to classic paradoxes. Education: BSc and MSc in Philosophy (Groningen), PhD in Computer Science (Groningen, 2003). Supervised by Gerard Renardel and Rineke Verbrugge for his dissertation on probabilistic dynamic epistemic logic. Research interests include logical paradoxes (e.g., Monty Hall, liar paradox), formal epistemology, and multi-agent systems. Recent work examines semantic leakage in lie detection and cognitive biases in social contexts. He teaches courses like Advanced Logic and Multi-Agent Systems. Grants: NWO VENI (2006–2009), NWO VIDI (2010–present). No listed awards, but his grants reflect significant academic recognition. His work intersects philosophy with computer science and cognitive science. Labs/Teams: Active in the Theoretical Philosophy department, contributing to interdisciplinary projects on logic and AI. No dedicated lab mentioned but collaborates widely in formal philosophy domains.

• Computer Science
• Artificial Intelligence
• Medical Image Analysis
• +۳ مورد دیگر

Xi Wu is a faculty member at Chengdu University of Information Technology , affiliated with the School of Computer Science . He holds a PhD from Sichuan University (2012, College of Electronic and Information Engineering). Current Research Focus: Medical imaging, computer vision, and deep learning applications in healthcare Key Themes: PET image reconstruction, radiotherapy dose prediction, GANs, diffusion models, and facial expression recognition His recent work explores transformer architectures , semi-supervised learning , and uncertainty-aware models for tasks like tumor segmentation and multi-organ analysis. Publications emphasize cross-domain adaptation and multi-modal medical imaging .

• Constrained Clustering
• Explainable AI
• Constraint Programming
• +۴ مورد دیگر

Thi-Bich-Hanh DIEP-DAO serves as a Lecturer with Habilitation à Diriger des Recherches (HDR) at the University of Orleans, where she is affiliated with the LIFO laboratory (Laboratory of Informatics of Orléans). She holds significant administrative roles including THAT Lab correspondent at the Doctoral School and Member of the Board, demonstrating leadership in academic governance and doctoral education. Her research centers on constrained clustering methodologies, with evolving focus areas including explainable AI, constraint programming applications, and knowledge integration in machine learning systems. Early work established foundational approaches for constraint-based clustering using declarative frameworks, while recent publications demonstrate sophisticated extensions to mineral prospectivity mapping, social media analysis, and knowledge graph-enhanced image classification. Her research consistently bridges theoretical constraint programming with practical applications across diverse domains. Analysis of her publication trends reveals increasing emphasis on explainability and knowledge integration since 2020, with notable contributions to incremental clustering systems and graph-based knowledge representation. Her work maintains strong methodological consistency in constraint programming while expanding into new application areas, particularly evident in her 2023-2025 publications on mineral prospectivity mapping and tweet-based event characterization. As an HDR-qualified researcher, Dr. DIEP-DAO actively supervises doctoral students and contributes to academic training through her roles in the Doctoral School. Her extensive collaboration network, particularly with Christel VRAIN, has produced over 60 publications since 2010, reflecting consistent research productivity. She is integral to the LIFO laboratory's data mining and constraint programming research group, where her work on declarative clustering approaches forms a core research pillar. Current projects focus on knowledge graph integration for explainable image classification systems and constraint-based social media analysis frameworks.

Alan Page Fiskeمشاهده پروفایل

استاد

• Psychological Anthropology
• Social Theory
• Relational Models Theory
• +۵ مورد دیگر

Alan Page Fiske is a Professor in the Department of Anthropology at the University of California, Los Angeles (UCLA), College of Letters and Science. He holds a Ph.D. from the University of Chicago (1985) and has established himself as a leading scholar in psychological anthropology and social theory. His academic career spans several decades with significant contributions to understanding the fundamental structures of human social relationships across diverse cultural contexts. Fiske's primary research interests focus on Psychological Anthropology, Social Theory, and Relational Models Theory. His work examines how humans construct and maintain social relationships through culturally specific frameworks. He has pioneered research on the four elementary forms of human relations that structure social life across cultures. His theoretical contributions have bridged anthropology, psychology, and cognitive science, demonstrating how cultural contexts shape social cognition and moral reasoning. Fiske's research demonstrates particular expertise in African societies, where he has conducted extensive fieldwork. Analysis of Fiske's publication record reveals consistent theoretical development around relational models theory, with increasing interdisciplinary integration into neuroscience, clinical psychology, and developmental studies. His work shows a trajectory from foundational theoretical papers toward applications in diverse fields including schizophrenia research, emotional intelligence, and neuroimaging studies of social cognition. The publications demonstrate strong collaboration patterns, particularly with scholars like Nick Haslam, with whom he has developed and refined relational models theory across multiple publications. Fiske's scholarly impact extends through his influential book Structures of Social Life: The Four Elementary Forms of Human Relations (1991) and his seminal Psychological Review article The Four Elementary Forms of Sociality: Framework for a Unified Theory of Social Relations (1992). His work has been foundational in establishing relational models theory as a major framework for understanding social cognition across cultural contexts. While specific details about his advising and grant activities are not evident from the provided materials, Fiske's extensive collaborative work suggests significant mentorship of junior scholars and research teams. His publications span anthropology, psychology, psychiatry, and cognitive science journals, indicating successful interdisciplinary grant funding and research team leadership. His work with colleagues on schizophrenia research suggests collaboration with clinical research teams. Fiske's research appears to be conducted through UCLA's interdisciplinary research infrastructure, likely involving connections with psychology, psychiatry, and cognitive science research groups. His later work incorporating neuroimaging suggests collaboration with neuroscience laboratories. The consistent theoretical development across decades indicates a stable research program with evolving methodological approaches from traditional anthropological fieldwork to experimental and neuroscientific methods.

Daniel Ritchieمشاهده پروفایل

دانشیار

• Computer Graphics
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Dr. Daniel Ritchie is an Associate Professor of Computer Science at Brown University , where he co-leads the Brown Visual Computing group. His research integrates Computer Graphics , Artificial Intelligence , and Machine Learning to develop neurosymbolic methods that combine procedural models and deep neural networks for 3D shape and scene creation.

• Didactics of Language and Literature
• Reading Promotion
• Educational Methods
• +۷ مورد دیگر

Oswaldo Luis Guerra Sánchez serves as a Professor in the Department of Specific Didactics within the Faculty of Educational Sciences at the University of Las Palmas de Gran Canaria. His academic work falls under the Branch of Knowledge in Social and Legal Sciences, with a specialized focus on the Area of Knowledge in Didactics of Language and Literature. With an active presence in academic research and teaching, Professor Guerra Sánchez maintains institutional affiliation through ULPGC's research portal accessaCRIS and contributes significantly to the university's educational mission. Professor Guerra Sánchez's research interests span multiple dimensions of language education and literacy development. His work particularly emphasizes the integration of emotional education with reading promotion, innovative multisensory approaches to storytelling, and inclusive educational strategies for diverse learners. His research portfolio demonstrates a strong commitment to addressing special educational needs, including work with students with autism (TEA) and ADHD, while also exploring cultural dimensions of language learning and the incorporation of theatrical methods in educational settings. His scholarly focus reveals a comprehensive approach to language didactics that connects cognitive, emotional, and social aspects of learning. An analysis of his recent scholarly output (2020-2023) reveals consistent thematic patterns across 42 thesis projects he has supervised. These works demonstrate increasing integration of multisensory approaches with traditional literacy instruction, growing attention to neurodiversity in educational contexts, and innovative applications of literary works for addressing contemporary social issues. The research trends show movement toward more interdisciplinary approaches that blend language education with emotional intelligence, cultural studies, and special educational needs. Professor Guerra Sánchez has supervised an extensive number of graduate students across multiple programs, including the Degree in Primary Education, Degree in Early Childhood Education, and the Master's Degree in Teacher Training for Compulsory Secondary Education. His advisory work spans diverse research areas while maintaining a consistent focus on language, literacy, and innovative pedagogical approaches. The breadth of his supervision indicates significant contribution to teacher education and educational research at ULPGC.

• Knowledge Representation and Reasoning
• Quantum Computation
• Artificial Intelligence
• +۳ مورد دیگر

Professor Sanjiang Li is affiliated with the University of Technology, Sydney (UTS) as a Professor in the Faculty of Engineering and Information Technology , specifically within the Centre for Quantum Software and Information . With a PhD in Mathematics from Sichuan University and a BSc from Shaanxi Normal University, his research spans quantum computation , knowledge representation and reasoning , and formal verification of quantum systems . His recent work focuses on quantum circuit transformation , including novel methods like adaptive divide-and-conquer and Monte Carlo Tree Search frameworks. He has advanced symbolic verification techniques using tensor decision diagrams and explored classical-quantum hybrid algorithms for resource-efficient training. Dr. Li has received prestigious awards such as the ARC Future Fellowship and Alexander von Humboldt Research Fellowship . He supervises PhD students including Calum Holker and Guangxi Li , and contributes to quantum software development through funded projects like the Sydney Quantum Academy and ARC Discovery Projects .

• Computer Vision
• Computer Graphics
• Artificial Intelligence
• +۴ مورد دیگر

Alexei Efros is a Professor in the Electrical Engineering and Computer Sciences Department at UC Berkeley's College of Engineering, where he holds the Howard Friesen Professorship. He is a leading researcher in computer vision and computer graphics, with a focus on data-driven techniques and self-supervised learning. His work bridges the intersection of vision and graphics, leveraging large quantities of unlabeled visual data to tackle complex problems. Dr. Efros received his PhD from UC Berkeley in 2003 and joined the Berkeley faculty in 2013 after spending a decade at Carnegie Mellon University. He has also held affiliations with École Normale Supérieure/INRIA and the University of Oxford. His research group is part of the Berkeley Artificial Intelligence Research Lab (BAIR). His research interests span computer vision, computer graphics, artificial intelligence, and machine learning, with particular emphasis on data-driven approaches, self-supervised learning, computational photography, and visual data mining. Efros has pioneered numerous techniques in image synthesis, scene understanding, and visual representation learning that have shaped modern computer vision research. An analysis of his recent publications reveals a strong focus on generative models, particularly diffusion models and their applications; interpretability of vision-language models like CLIP; 3D scene understanding and reconstruction; and the application of self-supervised techniques to video and spatial reasoning problems. His work continues to push boundaries in how machines understand and generate visual content. Among his numerous accolades are the ACM Prize in Computing (2016), multiple Helmholtz Test-of-Time Prizes, the SIGGRAPH Significant New Researcher Award, Sloan and Guggenheim Fellowships, and teaching awards including the Jim and Donna Gray Award for Excellence in Undergraduate Teaching (2023). Professor Efros has mentored an impressive cohort of students who have gone on to prominent positions in academia and industry, including faculty positions at CMU, TTIC, Stanford, and research scientist roles at OpenAI, Google DeepMind, and Anthropic. His teaching includes core computer vision courses at both undergraduate and graduate levels.

• Machine Learning
• Big Data Analytics
• Pattern Recognition
• +۷ مورد دیگر

Professor Junbin Gao is Professor of Big Data Analytics at the University of Sydney Business School. He previously served as Professor in Computing (2010-2016) and Associate Professor (2005-2010) at Charles Sturt University, and held academic positions at University of New England and University of Southampton. He is also a Member of the Sydney Southeast Asia Centre, demonstrating his regional academic engagement. Professor Gao earned his BSc and MSc from Huazhong University of Science and Technology (HUST) and PhD from Dalian University of Technology (DUT). His academic journey includes Professor at HUST (1997), Postdoctoral Research Fellow at Wuhan University (1991-1993), and multiple guest professorships at leading Chinese institutions including Wuhan University, HUST, and Beijing University of Technology. Professor Gao's research spans from approximation theory and multivariate spline functions to wavelet applications, and now focuses on machine learning and big data analytics. His recent work explores matrix neural networks, tensorial recurrent neural networks, and innovative approaches to data subspace clustering on manifolds. He integrates Riemannian geometry with machine learning to address challenges with manifold-valued data, finding applications in international relations, financial panel data, and computer vision. His publication record shows a clear progression toward increasingly sophisticated applications of machine learning in business analytics. Professor Gao has secured significant research funding including two Discovery Project grants from the Australian Research Council (ARC). His 260 academic papers and two books demonstrate substantial scholarly impact across multiple domains of data science and machine learning. Professor Gao supervises numerous research students working on cutting-edge topics including multivariate volatility forecasting, interpretable uncertainty systems, graph machine learning for consumer behavior, and deep learning for stock market prediction. His research group bridges theoretical advances with practical applications in business analytics, with particular interest in international relations research and financial panel data analysis.

Jacob Feldmanمشاهده پروفایل

استاد

• Cognitive Psychology
• Visual Perception
• Perceptual Organization
• +۴ مورد دیگر

Jacob Feldman is a Professor at Rutgers, The State University of New Jersey , affiliated with the Department of Psychology and the Rutgers Center for Cognitive Science (RuCCS). His research focuses on visual perception , particularly perceptual organization and shape, as well as categorization and concept learning . He employs Bayesian and mathematical models to explore how humans interpret visual data and organize objects into coherent categories. Feldman received his Ph.D. in 1992 from the M.I.T. Department of Brain and Cognitive Sciences and has remained at Rutgers since. His work emphasizes the simplification of complex data through perceptual and cognitive processes, with applications in robotics, virtual reality, and machine learning . Recent studies examine human-robot interaction, dynamic shape cues, and probabilistic foundations of mental representations. His publications span computer vision, cognitive psychology, and AI , reflecting interdisciplinary collaborations. Key themes include structure-from-motion , shape similarity , and cue integration in perception.

Yu Wangمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Graph Machine Learning
• +۷ مورد دیگر

Yu Wang serves as an Assistant Professor in the Department of Computer and Information Science at the University of Oregon's School of Computer and Data Sciences, where he leads research in graph-based machine learning and AI systems. Joining the faculty in September 2024 after completing his PhD at Vanderbilt University, he directs the Graph Machine Learning Lab and actively recruits PhD students for cutting-edge projects at the intersection of data mining, network analysis, and trustworthy AI. His educational background includes a Ph.D. in Computer Science from Vanderbilt University (2019-2024) and a B.S. from Harbin Institute of Technology (2015-2019). During his doctoral studies, he completed research internships at Adobe Research and The Home Depot, focusing on knowledge graph applications for information retrieval. Wang's research centers on graph machine learning, with emphasis on neural-symbolic learning, LLM integration with structured knowledge, and trustworthy AI systems. His work addresses critical challenges in imbalanced/biased graph learning, generative graph models, and social network analysis, driving applications in cyber-security, biochemistry, and infrastructure systems. Current projects explore agentic social simulations and spatial-temporal machine learning for real-world impact. His publication trajectory shows increasing focus on LLM-graph integration and trustworthy AI, with recent work spanning generative graph models, personalized LLMs, and robust networking systems. Key trends include bridging symbolic reasoning with neural approaches, advancing evaluation benchmarks for social network analysis, and developing privacy-preserving techniques for graph-based retrieval systems. Outstanding Doctoral Student Award (Vanderbilt, 2023-2024) Best Paper Award at NeurIPS GLFrontiers Workshop (2023) Vanderbilt Graduate Leadership Anchor Award for Research (2023) KDD Outstanding Dissertation Award Honorable Mention (2025) NSF IIS-III Core Program Grant as Lead PI (2025) Top-10 Most Influential Papers at CIKM'22 and WWW'23 Wang mentors PhD students including Yongjia Lei (SDM Doctoral Forum Honorable Mention recipient) and Riya (Pulse Research Fellow), with research funded through NSF grants and industrial collaborations with Adobe, Visa, and Home Depot. His lab provides substantial computational resources including L40S GPUs and OpenAI API access, supporting both theoretical innovation and real-world deployments through industry internships. Current advising focuses on generative graph models, LLM-agent collaboration, and trustworthy AI frameworks for societal applications. The Graph Machine Learning Lab maintains strong ties with industrial research groups, facilitating student internships at Adobe Research and other tech companies while developing open-source tools like ChemicalX for drug discovery. Ongoing projects include GraphRAG frameworks for security applications and social simulation environments for studying network dynamics.

Leo Dorstمشاهده پروفایل

استاد

• Geometric Algebra
• Computer Vision
• Clifford Algebra
• +۵ مورد دیگر

Dr. Leo Dorst is a Professor at the Informatics Institute within the Faculty of Sciences at the University of Amsterdam. He leads research in geometric algebra and computer vision, and is actively involved in both teaching and international conference organization. His work bridges theoretical mathematics with practical applications in computer science. Dr. Dorst has been instrumental in advancing geometric algebra as a computational framework for geometrical applications. His research spans from foundational mathematical principles to practical implementations in computer vision, robotics, and machine learning. He is particularly known for his work on conformal geometric algebra, plane-based geometric algebra, and their applications to 3D geometry and physics modeling. His research has evolved from theoretical foundations to cutting-edge applications in deep learning and morphological neural networks. Dr. Dorst has authored the influential book "Geometric Algebra for Computer Science" and has been organizing the Applied Geometric Algebras in Computer Science and Engineering (AGACSE) conference series since 2010. His most recent conference, AGACSE 2024, featured presentations on geometric algebra applications in computer graphics, machine learning, physics, and engineering. 2005 Inventor of the Year award Dr. Dorst has supervised numerous thesis projects and has been active in mentoring students through reading clubs on computer vision topics, including studies of Hartley and Zisserman's "Multiple View Geometry" and Kanatani's works. His teaching portfolio includes linear algebra, computer vision, robotics, and applied mathematics courses at various levels from bachelor to PhD. He is a key member of the Computer Vision Group at the University of Amsterdam, where his work connects mathematical theory with practical implementations in imaging, robotics, and machine learning systems.

Barbara Juhaszمشاهده پروفایل

استاد

• Cognitive Psychology
• Reading Processes
• Word Recognition
• +۵ مورد دیگر

Barbara Juhasz is a Professor at Wesleyan University, holding the Jeffrey L. Shames Professorship of Civic Engagement. She is affiliated with the Department of Psychology, Neuroscience and Behavior Program, College of Education Studies, and College of Integrative Sciences. Her research focuses on cognitive processes in reading, word recognition, and sensory-perceptual grounding of language. Education: B.A. from Binghamton University, M.A. and Ph.D. from University of Massachusetts, Amherst Her lab, the Wesleyan Eye Movement and Reading Lab (Judd Hall 507), investigates how word meanings are represented in the mental lexicon and how childhood word learning impacts adult language processing. Key methods include eye tracking using an Eyelink 1000 eye tracker. Recent research trends show her work spans age-of-acquisition effects, compound word processing, and sensory experience ratings. She has published in journals like Memory and Cognition and Quarterly Journal of Experimental Psychology , often using eye movement data to uncover lexical and semantic processing mechanisms. Grants: $19,818 contract (2010) to study eye movements in older adults during webpage viewing She has mentored students including Jennifer Brewer, Akila Raoul, and Micaela Kaye, integrating undergraduates into her research on morphological and lexical processing. The lab’s work bridges psycholinguistics, cognitive neuroscience, and integrative sciences.