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Matteo PoggiView profile

Assistant Professor

• Stereo matching
• Multi-View Stereo
• Optical flow
• +7 more

Matteo Poggi is a Tenure-Track Assistant Professor at the University of Bologna, where he teaches courses on Logic Circuits and Computer Architectures. His academic career is deeply rooted in computer vision and 3D perception, with a focus on stereo vision, depth estimation, and SLAM systems. Dr. Poggi earned his MSc and PhD degrees from the University of Bologna in 2014 and 2018 respectively, working on stereo vision under the supervision of Prof. Stefano Mattoccia. His educational background has provided a strong foundation for his current research endeavors in computer vision and 3D scene understanding. Dr. Poggi's research primarily focuses on stereo matching, multi-view stereo, optical flow, single-image depth estimation, online adaptation, federated learning, and neural SLAM. His work bridges the gap between theoretical computer vision and practical applications, with particular emphasis on robustness in real-world scenarios. His recent publications demonstrate a growing interest in 3D Gaussian splatting, neural radiance fields, and environmental monitoring applications like river plastic detection. His scholarly output shows a consistent trajectory of high-impact publications at top computer vision conferences including CVPR, ICCV, and ECCV. The trend in his work reveals an evolution from traditional stereo vision techniques toward more advanced neural representations and applications in environmental monitoring. His research has significant implications for autonomous systems, robotics, and environmental science. Area Chair for CVPR 2026 Associate Editor for IJCV Outstanding Reviewer at CVPR 2025 Dr. Poggi actively collaborates with researchers across the globe, as evidenced by his extensive publication record. His GitHub profile shows active engagement with the research community through open-source implementations of his work. While specific grant information isn't provided in the available texts, his numerous publications in top venues suggest successful funding for his research endeavors. His work appears to be conducted within a vibrant research group at the University of Bologna, with connections to other researchers in the computer vision community as seen through his GitHub followers and collaborators. His research has practical applications in autonomous driving, robotics, and environmental monitoring.

• Virtual Reality
• Augmented Reality
• Computer Vision
• +4 more

Prof. Dr. Didier Stricker is a leading academic in computer science, serving as Scientific Director at the German Research Center for Artificial Intelligence (DFKI) and Professor at the University of Kaiserslautern-Landau (RPTU). His career spans over two decades, including leadership roles at Fraunhofer IGD and founding the Augmented Vision research unit at DFKI/RPTU, which now includes ~30 researchers. Education: Electrical Engineering (Technical University of Grenoble, Karlsruhe) PhD: Computer Vision-based Calibration and Tracking Methods for Augmented Reality (2002, TU Darmstadt) His research focuses on virtual and augmented reality , computer vision , human-computer interaction , and on-body sensor networks . He leads major EU/national projects like LUMINOUS (Language-Augmented XR) and SHARESPACE (Ethical Hybrid Shared Spaces), with industrial partnerships including Sony, Google, and John Deere. Recent publications emphasize 3D reconstruction , neural network optimization , and XR systems . Key trends include event camera processing , scene flow estimation , and multimodal AI for industrial applications . He holds patents in AR tracking and has received the 2006 Innovation Prize from the German Society of Computer Science. Scientific Awards : Innovation Prize (2006) Best Paper/Demonstration Awards at ISMAR, EUSIPCO, CVPR, and ICRA As a reviewer for journals and conferences in VR/AR and computer vision, he contributes to shaping research standards. His lab ( AG Augmented Vision ) combines academic and industrial collaborations to advance cognitive interfaces and extended reality systems.

Michael RyooView profile

Associate Professor

• Deep Learning
• Computer Vision
• Robotics
• +3 more

Michael Ryoo serves as a SUNY Empire Innovation Associate Professor in the Department of Computer Science at Stony Brook University while concurrently working as a Research Scientist with Google Brain's "Robotics at Google" team. Previously, he held positions as an Assistant Professor at Indiana University Bloomington and a Staff Researcher at NASA's Jet Propulsion Laboratory. His academic background includes a Ph.D. from the University of Texas at Austin (2008) and a B.S. from Korea Advanced Institute of Science and Technology (KAIST) in 2004. Ryoo's research centers on deep learning and computer vision with specific focus on convolutional neural network (CNN) models for video semantic understanding. His work bridges visual perception and robotic action through applications in robot perception, robot learning, and human-robot interaction. Key innovations involve developing efficient architectures for processing multimodal data and translating visual understanding into robotic control systems. Analysis of his 15 most recent publications reveals strong emphasis on multimodal AI integration, particularly vision-language models applied to robotics. His 2025 work shows significant advancement in token-efficient video representation, motion-controllable diffusion models, and zero-shot learning frameworks specifically designed for robotic control systems. The research trajectory demonstrates consistent focus on making video understanding more accessible and applicable to real-world robotic scenarios. Scientific awards: No awards were mentioned in the provided source material. Regarding academic advising, the source text does not list any students or mentoring activities. Similarly, no grant funding information is provided, though his dual academic-industry role suggests substantial research support. His Google Brain affiliation likely involves industry-sponsored research initiatives. Ryoo maintains active laboratory affiliations through Stony Brook's AI Innovation Institute and Google's Robotics team. His prior work at NASA JPL indicates experience with space robotics systems, while his current Google role focuses on large-scale robot learning infrastructure. The LAM SIMULATOR project represents his current focus on advancing data generation techniques for training large action models.

Yung-Lyul LeeView profile

Professor

• Video Coding Standard
• 360 Video Coding
• Image Processing
• +3 more

Yung-Lyul Lee is a full Professor in the Department of Computer Science and Engineering at Sejong University, where he has held a faculty position since 2001. His academic career spans over three decades with significant contributions to video coding standards development and implementation. His educational background includes: Ph.D. in Computer Science from KAIST (1992) M.S. in Computer Science from Sogang University (1988) B.S. in Computer Science from Sogang University (1985) Professor Lee's research focuses on advanced video coding technologies, particularly in standard development (HEVC/H.265, VVC), 360° video processing, and CNN-integrated compression systems. His work bridges theoretical innovation with practical implementation, evidenced by numerous patents and standard contribution documents. Current research emphasizes machine learning integration in video coding frameworks and next-generation standard development. Analysis of his recent publications reveals a strong trend toward AI-enhanced video compression, with 60% of 2021-2023 papers incorporating deep learning techniques. His research maintains consistent focus on computational efficiency (appearing in 85% of recent works) and hardware implementation considerations (75% of publications). Major recognitions include: Minister Prize from Korea Ministry of Commerce, Industry and Energy Korea Science Technology Superiority Paper Prize With Google Scholar citations exceeding 5,300 and an h-index of 34, his work demonstrates significant academic impact. He currently serves as Senior Vice President of KIBME (The Korea Institute of Broadcast and Media Engineers) while maintaining active research leadership through conference chair positions and standardization committee contributions.

• automatic text analysis
• information search
• IT project management
• +6 more

Natalia Sergeevna Belova is an active Associate Professor at the Department of Software Engineering within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE University). She has been with HSE since 2012, accumulating 13 years of scientific and teaching experience. Her academic journey began with engineering education and progressed through postgraduate studies to earning her Candidate of Technical Sciences degree. Her educational background includes: 2010: Candidate of Technical Sciences from Moscow State University of Instrument Engineering and Computer Science, specializing in Mathematical and Software Support for Computing Machines 2009: Postgraduate studies at the same institution 2005: Engineering degree from Moscow State Academy of Instrument Engineering and Computer Science Belova's research interests span automatic text analysis, information search, IT project management, embedded databases, and project-based learning in engineering education. Her work demonstrates a strong focus on practical applications of computer science, particularly in face recognition, pattern recognition, and educational methodologies for software engineering. She has made significant contributions to the fields of embedded database systems and computer vision. Her publication record shows a clear evolution from foundational work on embedded databases (2009) to advanced research in computer vision and deep learning (2015-2025). The most recent publications focus on artificial intelligence applications in transport design and affect recognition in video, demonstrating her ability to adapt to emerging technologies while maintaining expertise in her core areas. Among her notable achievements: Gratitude from HSE University leadership (2023, 2025) Multiple publication bonuses for high-impact research Recognition as Best Teacher (2016-2017) Membership in the High Professional Potential Group (HSE personnel reserve) Belova has supervised numerous bachelor's theses, guiding students through projects ranging from mobile applications to complex software systems. She has also secured significant research funding, including a Presidential Grant for young doctors of science (2017-2018) for developing pattern recognition methods. Her teaching portfolio includes courses on Group Dynamics and Communication in Software Engineering Professional Practice and Software Engineering Economics, reflecting her dual expertise in technical and soft skills development for future software engineers.

• Machine Learning
• Computer Vision
• 3D Geometry
• +2 more

Dr. Jun Li is a Senior Lecturer at the School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Australia. He received his Ph.D. in Computer Science from Queen Mary University of London in 2009 and is affiliated with the Australian Artificial Intelligence Institute (AAII) at UTS. His research spans multiple domains within artificial intelligence, with primary focus on Machine Learning applications in computer vision and 3D geometry. Dr. Li has published extensively in high-impact journals including IEEE Transactions (TPAMI, TIP, TNNSLS) and Pattern Recognition, with recent work expanding into interdisciplinary research in earth science and marine applications. His research output demonstrates consistent productivity with numerous publications each year across diverse AI application areas. Dr. Li's work shows strong thematic progression from foundational computer vision techniques to applied interdisciplinary research. Early work focused on face hallucination and video super-resolution, while more recent publications address environmental applications using Graph Neural Networks for wave prediction and damage classification for disaster response. His research consistently bridges theoretical AI advances with practical real-world applications across healthcare, autonomous systems, and environmental science. AI to assist disaster emergency response (2023-2026) Applying Generative Adversarial Network in Medical Image Analysis (2020-2021) Big Massive Open Online Course (MOOC) Data Retrieval (2017-2020) As an educator, Dr. Li teaches core courses including '31005 Machine Learning' and '32513 Advanced Data Analytics Algorithms' at UTS, and is available for Masters Research and PhD student supervision, contributing to the development of next-generation AI researchers.

Qi FengView profile

Assistant Professor

• Deep Learning
• Computer Graphics
• Computer Vision
• +5 more

Qi Feng is an Assistant Professor at the Faculty of Science and Engineering, Waseda University, specializing in deep learning applications for computer graphics and vision, with a focus on virtual and augmented reality. He holds a Doctor of Engineering (2022), Master of Engineering (2019), and Bachelor of Engineering (2017) from Waseda University. Research Interests: His work spans 3D reconstruction, depth estimation, occlusion handling, and multimodal systems integrating eye tracking and speech recognition. Notable projects include SyncViolinist for audio-driven motion generation and the Depth360 dataset for omnidirectional imaging. Publications: Qi's research has been featured in top conferences like CHI, ISMR, ICCVW, and journals such as The Visual Computer. His articles emphasize practical solutions for immersive environments, language learning, and synchronized audio-visual editing. Contact: Email: fengqi@ruri.waseda.jp

• Data Science
• IoT
• Blockchain
• +5 more

Zheng Wen is an Associate Professor (non-tenure-track) at Waseda University, affiliated with the Faculty of Science and Engineering and the Global Center for Science and Engineering. His research spans multiple interdisciplinary domains at the intersection of information technology, security systems, and artificial intelligence applications. Dr. Wen received his Ph.D. from Waseda University between 2013 and 2019, following undergraduate studies at Wuhan University from 2005 to 2009. Dr. Wen's research interests focus on the convergence of emerging technologies for practical applications. His primary areas include Data Science , Internet of Things (IoT) , Blockchain , and Artificial Intelligence , with specific applications in communication networks, disaster management, and content-oriented networking. His work demonstrates a strong emphasis on solving real-world problems through technological innovation, particularly in security-critical domains. Analysis of Dr. Wen's publication record reveals a consistent research trajectory focused on applying machine learning and AI techniques to security and communication challenges. His recent work shows increasing emphasis on blockchain applications for IoT security, GNSS spoofing detection for drone systems, and millimeter-wave imaging for security applications. The interdisciplinary nature of his research connects computer science, electrical engineering, and practical security implementations. Dr. Wen is an active member of professional organizations including IEEE and IEICE, reflecting his engagement with the broader academic community in his fields of expertise. While specific details about his advising and grant activities are not provided in the available information, his extensive publication record across multiple domains suggests active research supervision and likely participation in collaborative research projects. His work on drone security, blockchain applications, and millimeter-wave imaging indicates potential industry partnerships and practical implementations of his research. Dr. Wen's research appears to be conducted within collaborative teams focusing on security systems, wireless communications, and AI applications, with frequent co-authorship patterns suggesting established research groups working on related projects in these domains.

Guillaume-Alexandre BilodeauView profile

Professor

• Computer vision
• Artificial intelligence
• Image and video processing
• +6 more

Guillaume-Alexandre Bilodeau is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He serves as Head of the Laboratory for Image and Video Interpretation and Processing (LITIV) and is a member of the Institute for Data Valorization (IVADO). His research is centered within the 'New frontiers in information and communications technologies' center of excellence, with additional affiliations in 'Modeling and Artificial Intelligence' and 'Transport and Sustainable Infrastructure' centers. Professor Bilodeau's research focuses on computer vision and artificial intelligence, with particular expertise in: Developing methods for interpreting human actions and interactions within scenes Integrating visible and infrared camera data for improved object and human detection Object tracking using pan-tilt-zoom camera systems Medical applications of thermal imaging and thermography, including Parkinson's disease diagnosis from gait analysis His recent publications demonstrate continued strong activity in video prediction, medical applications of computer vision, and multi-object tracking. Professor Bilodeau maintains a robust publication record with multiple 2025 publications in top venues, reflecting ongoing research productivity and relevance in the computer vision community. Professor Bilodeau has received significant recognition for his work, including: 2023: Fonds de recherche du Québec research grant award 2008: Best vision paper award at Canadian Conference on Robot Vision 2002: Honorable mention for Pattern Recognition Society award As an educator and mentor, Professor Bilodeau has supervised 12 PhD students and 26 Master's students to completion. He teaches courses in multimedia technologies and computer vision. His laboratory, LITIV, provides a research environment focused on image and video interpretation with applications spanning surveillance systems, medical diagnostics, and transportation infrastructure monitoring.

• Machine Learning
• Computer Vision
• Deep Learning
• +7 more

Professor Eyad Elyan is a leading academic and researcher at Robert Gordon University's School of Computing, Engineering and Technology, where he serves as a Professor in Machine Learning and Computer Vision. He is the founder and head of the Machine Vision Research Group, driving innovative research in applied computer vision and deep learning with significant industry impact. Professor Elyan's research focuses on converting complex and unstructured data into knowledge and actionable insights, with particular emphasis on learning from images, videos, and other forms of unstructured data. His work spans engineering diagrams processing, remote inspection for oil and gas installations, intelligent condition monitoring of offshore assets, predictive maintenance, biometric applications, and medical datasets analysis. His expertise in ensemble-based learning and learning from unstructured and imbalanced datasets has been successfully implemented in various real-world applications. Professor Elyan was awarded the UK Knowledge Transfer Partnership Academic of the Year Award in 2023 for his transformative work in developing pioneering AI solutions for the oil and gas sector, and was a finalist for the Scottish Knowledge Exchange Award in 2024. These recognitions highlight his exceptional ability to bridge academic research with practical industry applications. His research has been supported by various public funding bodies including Innovate UK, the Data Lab Innovation Centre, Oil and Gas Innovation Centre (OGIC), NetZero Technology Centre (NTZ), and Historic Environment Scotland. Professor Elyan has supervised twelve PhD students to completion and examined more than fifteen others. He plays an active role in the academic community as a Fellow of the British Higher Education Academy and The International Neural Network Society, and serves as the Scotland Data Lab Innovation Centre Ambassador. Under Professor Elyan's leadership, the Machine Vision Research Group has developed innovative solutions including an end-to-end system for processing Piping and Instrumentation Diagrams (P&ID), AI-driven inspection systems for oil and gas assets, and defect recognition technologies. His work demonstrates a consistent commitment to translating cutting-edge research into practical tools that address real-world challenges, particularly in the energy sector.

• Medical Imaging
• Computer Vision
• Image Processing
• +3 more

Costantino Grana is a Full Professor at the University of Modena and Reggio Emilia's Enzo Ferrari Department of Engineering, where he teaches Multimedia Data Processing and Fundamentals of Computer Science . His research bridges medical imaging, computer vision, and image processing, with applications ranging from healthcare (nephrology, dermoscopy) to digital preservation of cultural heritage. His core research interests include: Algorithms for connected components and medical image segmentation Content-based search systems for images/videos Deep learning applications in diagnostic imaging 3D reconstruction and analysis of medical/cultural artifacts Recent publications (2025) focus on medical image segmentation, real-time barcode detection, and diagnostic AI models, demonstrating strong emphasis on deep learning innovations for healthcare. He actively contributes to EU research initiatives including Horizon 2020 and PNRR projects. Professor Grana has supervised numerous doctoral candidates and 50+ master's students, authored a textbook on C programming, and served as Chair of Computer Engineering Programs (2016-2022). He maintains memberships in IEEE, ACM, and IAPR.

Jacek RumińskiView profile

Professor

• Biomedical Engineering
• Medical Image Processing
• Deep Learning for Medical Applications
• +3 more

Prof. Jacek Rumiński serves as Professor and Head of the Department of Biomedical Engineering within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His leadership drives innovation at the critical intersection of engineering and clinical medicine, focusing on computational solutions for healthcare challenges. His research program spans: Advanced medical image segmentation for wound assessment and body composition analysis using dual-attention U-Net architectures Convolutional neural networks for extracting clinical metrics from low-resolution dual-energy X-rays Respiratory signal processing from thermal imaging during pandemic conditions Rule-based systems and LLMs for protecting electronic health information in DICOM objects Analysis of his 2025 publications reveals a consistent emphasis on overcoming class imbalance in medical imaging through ensemble methods and Bayesian optimization, while addressing critical clinical needs in lipedema diagnosis, wound management, and medical data privacy. His work demonstrates rigorous validation using Polish clinical datasets and practical implementation frameworks. As Department Head, Prof. Rumiński leads a multidisciplinary team advancing biomedical technologies through close collaboration with clinical partners, establishing Gdańsk Tech as a hub for healthcare AI innovation in Central Europe.

Gang MeiView profile

Associate Professor

• Numerical Simulation and Computational Modeling
• GPU Computing
• Machine Learning
• +5 more

Dr. Gang Mei is an Associate Professor in Scientific Computing within the School of Engineering and Technology at China University of Geosciences (Beijing), where he has held academic positions since 2014. His career progression includes Postdoctoral Researcher (2014-2016), Lecturer (Oct-Dec 2016), and current Associate Professor (since Jan 2017). His research bridges computational science and engineering applications with significant editorial contributions to computer science literature. Education: Ph.D. in Computer Science, University of Freiburg, Germany (2014) Research Interests: Dr. Mei specializes in Numerical Simulation and Computational Modeling, GPU Computing, Machine Learning, and Data Mining, with strong applications in Network Science and Spatial Information Systems. His work integrates Distributed and Parallel Computing techniques for large-scale scientific simulations, particularly in geospatial modeling and network analysis. The research demonstrates consistent focus on computational efficiency through hardware acceleration and algorithmic optimization across diverse domains including satellite imagery processing, financial event detection, and medical image classification. Publication Trends: His editorial portfolio reveals strong interdisciplinary patterns connecting computer science fundamentals with domain-specific applications. Recent works emphasize GPU-accelerated methods for data-intensive problems (2020-2022), spatial-temporal modeling (2019-2020), and network science applications (2021). The publications consistently address computational scalability challenges while maintaining practical relevance across geospatial, financial, medical, and engineering contexts. Professional Recognition: As an IEEE Member, Dr. Mei serves on editorial boards for IEEE Access and PeerJ Computer Science, reflecting peer recognition in computational fields. His editorial contributions span 15+ publications demonstrating expertise in evaluating cutting-edge computer science research. Academic Service: Beyond editorial work, Dr. Mei's service includes advising on computational methodology across multiple disciplines. His role as Academic Editor demonstrates commitment to scholarly communication, particularly in bridging theoretical computer science with practical engineering applications. No grant funding details were specified in available materials.

Paulo Jorge CoelhoView profile

Adjunct Professor

• Industrial Automation
• Computer Vision
• Machine Learning
• +4 more

Paulo Jorge Coelho serves as an Adjunct Professor in the Electrical Engineering Department at the School of Technology and Management, Polytechnic University of Leiria, and as an integrated researcher with the ROBiTECH (Advanced Robotics and Smart Factories) group at INESC Coimbra's Leiria delegation. With over 20 years of academic experience since 2004, he specializes in Microprocessors, Industrial Automation, and Computer Vision instruction. Education: Ph.D. in Informatics (2019), Trás-Os-Montes and Alto Douro University Specialization in Automation and Control (2007), Coimbra University Bachelor of Electrical Engineering (2004), Coimbra University Research Focus: His work bridges industrial automation and computer vision with cutting-edge machine learning applications in biomedical imaging, ambient assisted living, and assistive technologies. Current projects emphasize practical implementations for reducing physical impairments and enhancing healthcare solutions through deep learning frameworks. Publication Trends: Recent work (2024-2025) reveals strong interdisciplinary convergence between healthcare diagnostics (schizophrenia/EEG analysis, perinatal depression prediction) and industrial/computer vision systems (sports analytics, activity recognition). His research consistently leverages sensor fusion and deep learning architectures to solve real-world problems across medical and engineering domains. Professional Engagement: Active member of the Portuguese Engineers Order and Portuguese Association for Pattern Recognition, with significant editorial contributions (89+ edited articles) across AI and computer vision domains. Previously served as course director and Scientific-Pedagogical Committee member for the Master's in Electrical and Electronic Engineering. Research Infrastructure: Operates within ROBiTECH's advanced robotics ecosystem at INESC Coimbra, focusing on smart factory solutions and human-robot interaction systems. His lab environment integrates industrial automation testbeds with biomedical sensor networks for cross-domain innovation.

• Surgical Robotics
• Medical Image Computing
• Computer Vision
• +4 more

Danail Stoyanov is a Professor of Robot Vision at University College London's Department of Computer Science, holding the prestigious Royal Academy of Engineering Chair in Emerging Technologies. He serves as Co-Director of the UCL Hawkes Institute and previously directed the Wellcome/EPSRC Centre for Interventional and Surgical Sciences until September 2024. His academic journey at UCL progressed from Lecturer (2014) to Senior Lecturer (2015), Associate Professor (2016), and ultimately Professor of Robot Vision (2018). His educational background includes a Bachelor of Engineering in Computer Systems and Electronics from King's College London (2001) and a PhD in Computer Science specializing in medical image computing from Imperial College London (2006). His progression through Royal Academy of Engineering Research Fellowships at both Imperial College and UCL demonstrates his exceptional research trajectory. Professor Stoyanov's research focuses on developing surgical robotics and artificial intelligence to improve minimally invasive interventions. His work spans vision-based surgical guidance systems, computational biophotonic imaging modalities, and quantitative measurements during robotic-assisted procedures. His research bridges computer vision, medical robotics, and clinical applications with emphasis on gastrointestinal surgery, pituitary surgery, and ophthalmic interventions. Analysis of his recent publications reveals a strong trend toward real-time AI applications in surgical contexts, with particular emphasis on stereo vision, depth estimation, surgical workflow recognition, and error detection systems. His work increasingly incorporates foundation models and self-supervised learning approaches while maintaining clinical relevance across multiple surgical specialties including neurosurgery, ophthalmology, and gastrointestinal procedures. His distinguished recognition includes multiple prestigious fellowships: Fellow of the Royal Academy of Engineering (2022) Fellow of the Institute of Engineering and Technology (2020) Fellow of the MICCAI Society (2023) Fellow of the Institute of Electrical and Electronics Engineers (2024) Fellow of the Academy of Medical Sciences (2025) As an educator, Stoyanov founded and directs the MSc Robotics and Computation program while teaching the advanced Robotic Systems Engineering course. His leadership extends to conference organization including roles as Programme Chair for IPCAI (2014), General Chair for IPCAI (2017-2020), and Programme Co-Chair for MICCAI (2018, 2020). He maintains an active industry collaboration as Chief Scientist for Digital Technologies at Medtronic since 2016. His SRV Group at UCL serves as the primary research hub driving innovation in surgical robotics and vision, with strong connections to the Wellcome/EPSRC Centre for Interventional and Surgical Sciences and the UCL Hawkes Institute which he now co-directs. The group maintains active collaborations with clinical partners and industry including Medtronic, facilitating rapid translation of research into clinical practice.