جست‌وجوی استاد و پژوهشگر

Longin Jan Lateckiمشاهده پروفایل

استاد

• Computer Vision
• Medical Imaging
• Digital Geometry and Topology
• +۶ مورد دیگر

Dr. Longin Jan Latecki is a Professor in the Department of Computer and Information Sciences at Temple University. His research focuses on computer vision, medical imaging, digital geometry, and artificial intelligence. He has held editorial roles, including Associate Editor-in-Chief of Pattern Recognition and roles on the editorial boards of Computer Vision and Image Understanding and Journal of Imaging . He has organized events like the Asian Conference on Pattern Recognition and chaired the PhD Track in Artificial Intelligence. His research spans topics such as shape-based object detection, video mining, and robot mapping. Notable projects include the Shape-Based Image Search Engine and work on Well-Composed 3D Images . He has authored over 300 publications and advised numerous PhD students in areas like medical imaging, computer vision, and data mining. Dr. Latecki has collaborated on projects such as ClimatePub4KG for climate modeling and SciDMT for scientific mention detection. His work emphasizes practical applications, including dental imaging analysis, autonomous robotics, and multimedia transmission optimization. He teaches courses like Mathematical Concepts in Computing and Artificial Intelligence .

ÖZGE ÖZTİMUR KARADAĞمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Pattern Recognition
• +۲ مورد دیگر

Özge Öztimur Karadağ is an Assistant Professor at the Department of Computer Engineering, Faculty of Engineering, Alanya Alaaddin Keykubat University. With a doctorate from Middle East Technical University (2014) and a master's degree from the same institution (2008), she specializes in computer vision, machine learning, and image processing. Academic Appointments: Assistant Professor (2014-present), Lecturer (2015), Research Assistant (2008-2014) Research Interests: Skeleton-based action recognition, image segmentation, Markov Random Fields, domain knowledge integration, deep learning Projects: TUBITAK-funded research on visual information precision (2012-2015), entity linking project (2017) Scientific Activities: IEEE member (since 2013), reviewer for journals like IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Silvia Rossiمشاهده پروفایل

دانشیار

• Multi-agent Systems
• Human-Robot Interaction
• Cognitive Architectures
• +۵ مورد دیگر

Silvia Rossi is an Associate Professor at the Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Italy. She co-manages the PRISCA Lab focused on Intelligent Robotics and Advanced Cognitive Systems and coordinates the national project UPA4SAR (User-centered Profiling and Adaptation for Socially Assistive Robotics). Her research emphasizes Multi-agent Systems, Human-Robot Interaction, and Cognitive Architectures. She holds an M.Sc. in Physics from the University of Naples Federico II (2001) and a Ph.D. in Information and Communication Technologies from the University of Trento (2006). Professional experience includes roles at the ITC-irst (Italy), Institute of Cybernetics E. Caianiello (CNR, Italy), and visiting research at Oregon Health and Science University (USA). Research interests span socially assistive robotics, user profiling, and emotion recognition. Key contributions include adaptive robot behaviors for healthcare and education, transparent HRI, and multi-agent systems for smart environments. She teaches courses such as Multi-agent Systems, Distributed Intelligence, and Programming I. Her work integrates AI, robotics, and human-centered design to address challenges in assistive technologies and human-robot collaboration. Publications focus on socially assistive robotics, emotion-aware systems, and transparent AI. Projects include field trials of social robots for elderly care and pediatric emotional support. Awards and recognitions are not explicitly listed but reflect her leadership in socially impactful robotics research.

• Computer Vision
• Assistive Technologies
• Healthcare Informatics
• +۳ مورد دیگر

Irene Ballester Campos is a Researcher at TU Wien's Faculty of Informatics, affiliated with the Department of Computer Graphics and Algorithms. Her work focuses on applying computer vision and AI to develop assistive technologies for healthcare contexts, particularly in dementia care and clinical applications. She is involved in the visuAAL project (2020–2026) and previously contributed to the DIANA project (2020–2023). Her research emphasizes privacy-sensitive systems, 3D sensor technologies, and human-centered design for assistive applications. Key areas include dementia behavior monitoring through depth sensors, action recognition in privacy-sensitive scenarios, and ethical implications of assistive systems. She also explores clinical applications such as Parkinson's disease severity estimation through motion analysis. Publications highlight contributions to 3D human pose estimation, dynamic object tracking in robotics, and vision-based solutions for toileting assistance. Her work bridges computer science with healthcare, emphasizing practical deployment in real-life clinical settings.

Martin Ilcikمشاهده پروفایل

پژوهشگر

• Procedural Modeling
• Shape Grammars
• Layout Generation
• +۶ مورد دیگر

Martin Ilcik is a Researcher at the Department of Computer Graphics within the Faculty of Informatics at Vienna University of Technology (TU Wien). His work focuses on procedural modeling, shape grammars, and related computer graphics techniques. He serves as a Scientist in the Rendering and Modeling workgroup and has been actively involved in research projects since 2008, including GameWorld, PAMINA, Wohnen 4.0, AgroEco, and currently Room Imageer and GreenFDT projects. Ilcik's research interests prominently feature procedural modeling techniques, with specializations in shape grammars, layout generation, semantic modeling, 3D reconstruction, and music generation. His work bridges computer science with practical applications in architecture, urban planning, and creative arts. He has made significant contributions to layer-based procedural design of building facades and collaborative modeling systems. His publication record spans from 2007 to 2024, with a strong focus on procedural modeling techniques. The research trajectory shows evolution from flow visualization to sophisticated procedural modeling systems, with increasing emphasis on shape grammars, collaborative design, and applications in architecture and music generation. His work demonstrates consistent innovation in making procedural modeling more intuitive and accessible. VC CultTech Hackathon winner category 'Vienna State Opera challenge' (2017) INiTS Award 2016 Ilcik has supervised student research, including Lukas Eibensteiner's 2021 Diploma Thesis on polyphonic music composition with grammars. He has been instrumental in organizing the Central European Seminar on Computer Graphics (CESCG) since 2008, which has grown into an important annual event for undergraduate students in computer graphics, vision, and visual computing across Central Europe. His community work includes creating educational resources and organizing EXPO events and Academy Videos through CESCG. He leads research in the Rendering and Modeling workgroup at TU Wien's Computer Graphics department, focusing on developing innovative procedural modeling techniques with applications in architecture, urban planning, and creative domains. His current projects (Room Imageer and GreenFDT) continue his longstanding interest in practical applications of procedural modeling techniques.

• Human Evolution
• Vertebrate Functional Morphology
• Imaging Techniques
• +۳ مورد دیگر

Dr. Christopher Dunmore is a Lecturer in Animal Biology and Director of the Imaging Centre for Life Sciences at the University of Kent’s School of Anthropology and Conservation. His research focuses on human evolution, particularly reconstructing fossil hominin hand use during locomotion and tool-use emergence. He completed his PhD at the University of Kent in 2019, studying the functional morphology of Australopithecus sediba’s hand. His work integrates micro-CT scans, machine learning algorithms, and comparative analyses of extant and extinct vertebrates to interpret bone architecture and behavior. Education : PhD, University of Kent, 2019 (Paleobiology) Research Interests : Functional morphology of post-cranial skeletons Imaging techniques (micro-CT, 3D analysis) Trabecular bone architecture and its relation to mechanical loads Evolution of hominin locomotion and tool use Publications Trends : Recent work emphasizes computational methods for trabecular bone analysis, comparisons of hominid locomotor adaptations, and the application of machine learning to paleontological data. Key topics include metacarpal morphology, femur structure, and Neanderthal manipulation abilities. Professional Affiliations : Member of the American Association of Biological Anthropologists (AABA) and the European Society for the Study of Human Evolution (ESHE). Research Infrastructure : Leads the Imaging Centre for Life Sciences, advancing methodologies in bone analysis and 3D imaging. Collaborates extensively with global researchers on hominin fossils and experimental studies.

Patrick Holthausمشاهده پروفایل

پژوهشگر ارشد

• Social Robotics
• Human-Robot Interaction
• Assistive Robotics
• +۴ مورد دیگر

Dr. Patrick Holthaus is a Senior Research Fellow at the University of Hertfordshire's Robotics Research Group , affiliated with the School of Physics, Engineering and Computer Science . He specializes in assistive and social robotics, focusing on nonverbal communication, trust dynamics, and systems integration. His work integrates fundamental research with real-world applications like the Robot House and Kaspar robot for children with autism. Teaching: Module leader for Data Structures and Algorithms and co-leader of Advanced Research Topics in Computer Science . Supervises multiple PhD students and serves as external examiner for postgraduate programs. Research Leadership: Principal Investigator (PI) for projects such as the Robot House Immersive Showcase and Enhancing Research Cultures grants. Collaborates internationally on initiatives like the SWAG wearable assistive garments and Hospital@Home virtual care system. Key Projects: Co-Investigator (CoI) on Horizon Europe’s SWAG project and the Dinwoodie-funded Hospital@Home initiative. Previously led EPSRC’s EMERGENCE network and the Kaspar Explains study. Labs & Teams: Manages the Robot House facility, a unique environment for HRI studies. Active in the Assuring Autonomy International Programme (AAIP).

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Sven Dickinson is a Professor in the Department of Computer Science at the University of Toronto, where he has held roles such as Chair (2010-2015) and Acting Chair (2008-2009). He also served as Vice President and inaugural Head of the Samsung Artificial Intelligence Research Center in Toronto (2018-2024). His academic journey includes positions at Rutgers University and affiliations with MIT, University of Maryland, and others. Education: B.A.Sc. in Systems Design Engineering, University of Waterloo (1983) M.S. and Ph.D. in Computer Science, University of Maryland (1988, 1991) Research Interests: Focuses on object recognition, shape perception, and the integration of human and computer vision. Key areas include generic object recognition, shape abstraction, symmetry detection, and multiscale part-based representations. His work bridges low-level image features and high-level shape models, emphasizing mid-level shape priors and perceptual grouping. Awards and Honors: NSF CAREER Award (1996) Ontario Premiere's Research Excellence Award (2002) Lifetime Research Achievement Award from CIPPRS (2012) Fellow of IEEE and IAPR Contributions: Co-edited influential volumes like Object Categorization: Computer and Human Vision Perspectives (2009) and Shape Perception in Human and Computer Vision (2013). Served as Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence (2017–2021), and on multiple editorial boards. Active in organizing workshops and conferences, including CVPR 2014 and WACV 2019. Labs and Collaborations: Involved with the Vector Institute for Artificial Intelligence, and has collaborated on projects in artificial intelligence, robotics, and vision-based applications for accessibility and navigation.

Yue Zhangمشاهده پروفایل

دانشیار

• Scientific Visualization
• Computer Graphics
• Mathematical Modeling
• +۴ مورد دیگر

Dr. Yue Zhang is an Associate Professor in the School of Electrical Engineering and Computer Science at Oregon State University. She holds appointments in both academic research and teaching roles, with a focus on computer graphics and data visualization. Her work bridges theoretical mathematics and practical applications in material science and ecological modeling. Ph.D. in Applied Mathematics, North Carolina State University B.S. in Mathematics and Physics, University of Tennessee at Knoxville Research interests span scientific visualization , tensor field analysis , and topology-driven modeling of physical and biological systems. She has pioneered techniques for hypergraph simplification, non-Euclidean geometry visualization, and coupled acoustic-structural simulations. Recent publications demonstrate trends in hypergraph visualization (2024), 3D tensor topology (2024-2022), and environmental stressor modeling (2022). Collaborative works with Eugene Zhang and Peter Oliver dominate her publication record. Students under her advisement include: PhD candidates: Shih-Hsuan Kevin Hung MS/MEng students: Kyle Hiebel, Josiah Blaisdell, Avery Stauber Co-advised projects: Peter Oliver, Xiaofei Gao

• Anatomy
• Physiotherapy
• Medical Imaging
• +۳ مورد دیگر

Oğuzhan ÖZTÜRK is a Lecturer at the Department of Therapy and Rehabilitation within the School of Vocational Studies at Kastamonu University , Turkey. He holds a PhD in Anatomy from Düzce University (2022) and a Master's degree in Veterinary Anatomy from Ondokuz Mayıs University (2018-2019), following his Bachelor's in Physiotherapy and Rehabilitation from Marmara University (2011-2015). PhD: Anatomy, Düzce University Graduate School of Medicine (2022) MSc: Veterinary Anatomy, Ondokuz Mayıs University Institute of Health Sciences (2018-2019) BSc: Physiotherapy and Rehabilitation, Marmara University Faculty of Health Sciences (2011-2015) His research focuses on anatomical sex prediction using machine learning and CT imaging , with applications in forensic anthropology and medical diagnostics. He also investigates smartphone addiction effects on sensory-motor functions and anatomy education methodologies. Recent publications (2024-2025) demonstrate interdisciplinary intersections between anatomy , machine learning , and physiotherapeutic applications . Key subfields include craniofacial morphometry, neural network training for radiological analysis, and behavioral health impacts. He actively participates in social responsibility projects related to family strengthening, obesity prevention, and educational outreach, serving as project coordinator for multiple initiatives funded by national institutions. Collaborates with researchers from Düzce University, Karabük University, and others Teaches courses including Anatomy, Physiotherapy Methods, and Nutrition Principles

Zoran Kapelanمشاهده پروفایل

استاد

• Urban Water Infrastructure
• Hydroinformatics
• Water Distribution Systems
• +۴ مورد دیگر

Professor Zoran Kapelan serves as Chair of Urban Water Infrastructure within the Department of Water Management at Delft University of Technology's Faculty of Civil Engineering and Geosciences. With over 30 years of experience in water engineering, he joined TU Delft in 2018 after leading the Water and Environment Group at the University of Exeter for 19 years, following a decade in international consultancy. His research spans smart water systems, hydroinformatics, and sustainable urban water infrastructure management. Key focus areas include AI-driven solutions for drinking water distribution (leakage detection, pressure management), sewer systems optimization (blockage prediction, sustainable drainage), asset management, and flood risk assessment. He pioneers methodologies integrating artificial intelligence, real-time control, and decision-making under uncertainty to address climate change adaptation and circular economy principles in water systems. Recent publications (2024-2025) reveal strong trends in deep learning applications for infrastructure monitoring (sewer crack detection, floating litter quantification), resource recovery assessment frameworks, and advanced modeling of non-Newtonian fluid dynamics in wastewater systems. His work increasingly emphasizes circular economy metrics, environmental risk assessment of novel materials, and co-developed sustainable solutions through serious gaming approaches. Editor, Journal of Water Resources Planning and Management (ASCE) Associate Editor, Water Resources Research Member, IWA Digital Water Steering Committee Member, External Scientific Advisory Council of KWR Prof. Kapelan has secured over €10 million in research funding across 50+ EU, RCUK, and industry grants. His supervision portfolio includes 40+ PhD students and 25+ postdocs, with significant industry impact through commercialized technologies like AI-based pipe burst detection systems deployed by United Utilities serving 7 million customers. He coordinates TU Delft's Urban Drainage and Water Management course while contributing to asset management and water transport curricula. The Urban Water Infrastructure group under his leadership maintains strong industry partnerships with water utilities globally, focusing on translating research into operational solutions for real-time monitoring, predictive maintenance, and climate-resilient infrastructure design.

Furkan Kıraçمشاهده پروفایل

استادیار

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Furkan Kıraç is an Assistant Professor in the Computer Science Department at Özyeğin University, specializing in Computer Vision and Machine Learning . He previously served as a Part-Time Instructor at the same university (2012-2013) and as a Research Assistant at Boğaziçi University (2009-2013). Education: PhD in Computer Engineering, Boğaziçi University (2013) MS in Systems and Control Engineering, Boğaziçi University (2002) BS in Mechanical Engineering, Boğaziçi University (2000) His research focuses on real-time hand pose estimation , deep learning , and computer vision applications in industrial automation. Recent publications highlight his work on pedestrian tracking, spatio-temporal mapping, and image processing pipelines for test oracle automation. Notable achievements include founding two computer vision companies ( Proksima and Fortibase ) and receiving awards at SIU conferences (2004, 2005, 2012). He has contributed to projects funded by TÜBİTAK and the Scientific and Technical Research Council of Turkey. Scientific Awards: 3rd place in best demo award (SIU 2012) Best application paper award (SIU 2012) 3rd degree in Turkish National Science Competition (1994, 1995) Gold/Silver/Bronze medals in National Computer Science Olympiads

José Carlos Cabaleiro Domínguezمشاهده پروفایل

استاد

• High performance computing
• Parallel systems architecture
• Parallel algorithms
• +۶ مورد دیگر

Dr. José Carlos Cabaleiro Domínguez is a Full Professor in the Department of Electronics and Computing at the University of Santiago de Compostela's Faculty of Computing, Spain. He has been a member of CiTIUS (Centro singular de investigación en tecnoloxías da información e comunicación) since 2010 and was promoted to Full Professor in 2022 after serving as an Associate Professor since 1994. His academic journey began with a BS and PhD in Physics from the University of Santiago de Compostela in 1989 and 1994 respectively, with initial teaching experience at the University of A Coruña from 1990-1994. His research focuses on high performance computing, particularly in parallel systems architecture, development of parallel algorithms for irregular problems with sparse matrices, performance prediction and improvement of parallel applications, memory hierarchy optimization, and applications for grid and cloud computing. He has developed significant expertise in 3D point cloud processing from remote sensors like LiDAR, with applications in urban infrastructure analysis, powerline detection, and route planning. Analysis of his recent publications reveals a strong emphasis on optimizing resource allocation for big data frameworks, developing deep learning applications for point cloud classification, and creating efficient algorithms for powerline detection in LiDAR surveys. His work bridges theoretical computer science with practical applications in geospatial analysis and infrastructure monitoring. His research has been published in top-tier journals including IEEE Transactions, ISPRS Journal of Photogrammetry and Remote Sensing, and Future Generation Computer Systems, reflecting his significant contributions to the field of high performance computing and its applications. Dr. Cabaleiro actively collaborates with researchers across multiple institutions, as evidenced by his extensive publication record with co-authors from various universities and research centers. His work demonstrates a consistent trajectory of advancing parallel computing techniques while applying them to increasingly complex real-world problems involving large-scale geospatial data.

• Synchronous Reactive Programming
• Concurrency
• Parallelism
• +۷ مورد دیگر

Frédéric DABROWSKI is a Lecturer with HDR at the University of Orleans, Faculty of Sciences, affiliated with the LIFO laboratory. He serves as head of the Master in Computer Sciences program since 2021 and participates in university governance through the Academic and Research Councils. His research bridges theoretical computer science and practical system verification, with emphasis on reactive and parallel programming models. His core research spans Synchronous Reactive Programming, Concurrency/Parallelism, Program Semantics, Deductive Verification, Static Analysis, Temporal Logic, and Formal Methods mechanized primarily in Coq. He investigates effect systems in functional reactive languages, textual alignment in SPMD programs, and verification techniques for synchronous systems, contributing foundational semantics and analysis tools. Analysis of his 20+ publications reveals consistent focus on applying formal methods to concurrent systems, evolving from BSP/SPMD semantics (2006-2019) toward functional reactive programming with effects and IoT security (2020-2025). Recent work emphasizes Coq-mechanized verification, effect handling, and safe reactive language design for critical embedded systems. DABROWSKI has supervised eight advisees including PhD candidates Terence Clastres (deductive verification), Darine Rammal (IoT reactive programming), and Jordan Ischard (FRP semantics), plus Master's students like Loic Kerbaul. He participates in funded projects: SIOMediC (IoT medical security, 2023-2024), DeSSUF (safe IoT languages, 2022-2023), BSP Program Analysis (Huawei, 2016-2019), and PaPDAS (parallel skeletons, 2011-2014). Based at LIFO, he co-leads the GT CLAP research group and serves on HLPP symposia steering committee, fostering collaborations with INSA Center Val de Loire, University of Maribor, and Japanese institutions to advance high-level parallel programming and verification.

Benoît Naegelمشاهده پروفایل

استاد

• Mathematical Morphology
• Image Segmentation
• Hierarchical Approaches
• +۱۰ مورد دیگر

Benoît Naegel serves as Full Professor at the University of Strasbourg within the ICube research institute, specifically leading activities in the IMAGeS (Images, Modeling, Learning, Geometry and Statistics) team. His office is located at 300 Bd Sébastien Brant, BP 10413, 67412 Illkirch CEDEX, France. His research spans mathematical morphology with specialization in image segmentation techniques, particularly hierarchical approaches using component-trees and component-graphs. He integrates machine learning methodologies into traditional morphological operations, with strong application focus in medical imaging domains including angiographic cerebral MRI (VIVABRAIN Project), abdominal CT scans, PET/CT multimodal imaging, and electronic microscopy for structural biology. His recent work shows increasing incorporation of deep learning techniques. Professor Naegel teaches across multiple academic levels: Computer Vision and Machine Learning at Master I3D in Computer Science; Image Processing and Mathematical Morphology at Masters OTG, I3D, IRIV, and Healthtech programs; System Programming at L2 Computer Science; and Graph Theory/Image Processing at BUT levels. He maintains active research software development through GitHub repositories including LibTIM (C++ image processing library) and component-graph implementation tools. His research output shows consistent focus on geometric and statistical approaches to image analysis, with recent emphasis on centerline detection methodologies applicable to vascular structures as evidenced by the 2024 PhD proposal supervised with Adrien Krähenbühl. The work demonstrates progression from traditional mathematical morphology toward hybrid approaches incorporating machine learning.