جست‌وجوی استاد و پژوهشگر

Hao Caiمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Image Processing
• +۳ مورد دیگر

Hao Cai is an Assistant Professor in the Department of Computer Science at St. Francis Xavier University. His research focuses on computer vision, machine learning, and image processing, with a particular emphasis on crowd counting, generative adversarial networks (GANs), and image quality assessment. He has developed innovative methods for multi-granularity crowd counting, scale-invariant feature aggregation, and disentangled representation learning in GAN frameworks. His work addresses challenges in real-world computer vision tasks such as density estimation, perceptual quality metrics, and 3D navigation for unmanned aerial vehicles (UAVs). Dr. Cai’s research spans technical domains including deep learning architectures, stochastic aggregation networks, and adaptive convolutional neural networks. His contributions to multi-view stereopsis and visual homing algorithms have advanced robotics and autonomous systems. He has also explored sparse representation techniques for image quality evaluation, combining statistical analysis with perceptual models to enhance accuracy and efficiency. His publications reflect a strong focus on practical applications of computer vision, from crowd analysis in urban environments to robust image quality assessment without reference images. Future work directions likely include further integration of adaptive learning strategies, scalable architectures, and interdisciplinary applications of vision systems.

• Computer Vision
• Stereo Vision
• Image Processing
• +۵ مورد دیگر

Dr. Jasmine Banks is a Lecturer in the School of Electrical Engineering & Robotics at Queensland University of Technology (QUT). She holds a PhD in Engineering from QUT and a dual Bachelor's degree in Electronics and Information Technology. Her academic roles include serving as First Year Coordinator for Engineering since 2016, focusing on student welfare, academic advising, and curriculum management. Research Interests: Her work spans FPGA-based signal processing, computer vision, biomedical image analysis, and embedded systems. Key areas include stereo vision algorithms, white blood cell segmentation, and 3D face tracking using stereo cameras. She emphasizes practical applications of hardware-software co-design and FPGA acceleration. Teaching: Over 20 years of university teaching experience, including courses in Electronics, Signal Processing, and FPGA programming. Notable roles include Unit Coordinator for ENB240 Introduction to Electronics and EGH449 Advanced Electronics. She pioneered blended learning approaches and developed FPGA tutorials used globally. Publications: Over 80 peer-reviewed papers in areas like FPGA-based solvers, medical image segmentation, and biometric recognition. Recent work focuses on microstructural analysis of drying processes and deep learning for cell classification. Professional Service: IEEE Queensland Section Treasurer (2009–2010) and QUT Student Branch Counselor. Active member of Engineers Australia and the Australasian Association for Engineering Education (AAEE).

Yasutaka Furukawaمشاهده پروفایل

دانشیار

• Computer Vision
• 3D Reconstruction
• Neural Networks
• +۵ مورد دیگر

Yasutaka Furukawa is a Principal Scientist at Wayve and an Associate Professor of Computing Science at Simon Fraser University (SFU). He leads the SFU Visual Computing Group, ranked 12th globally in Computer Vision and Graphics. His research focuses on 3D reconstruction, neural networks, and computer graphics, with notable contributions in multi-view stereo, CAD modeling, and diffusion models. Furukawa holds a Ph.D. from the University of Illinois at Urbana-Champaign (2008) and has held roles at Washington University in St. Louis, Google, and the University of Washington. Education: Ph.D., Computer Science, UIUC, 2008 Postdoctoral Research, University of Washington (with Prof. Steven Seitz and Brian Curless) Research Interests: Furukawa’s work bridges computer vision and graphics, emphasizing robust 3D reconstruction techniques. His projects include PuzzleFusion++ (diffusion-based fracture assembly), BrepGen (B-rep CAD generation), and HouseDiffusion (floorplan synthesis). He explores applications in autonomous systems, AR/VR, and robotics. Key Projects: Generative Adversarial Networks for architectural design (House-GAN series) Neural networks for inertial navigation (RoNIN) Diffusion models for spatial puzzles and CAD modeling Awards: Recipient of the PAMI Longuet-Higgins Prize (2020), NSF CAREER Award (2015), and multiple Google Faculty Research Awards. Teaching: Courses include CMPT412 Computer Vision and CMPT762 Advanced Computer Vision at SFU. Active in supervising over 30 graduate students and postdocs.

Aleksandra Samardžićمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +۳ مورد دیگر

Aleksandra Samardžić is a researcher at Singidunum University's Faculty of Informatics and Computing, specializing in Artificial Intelligence and Software Engineering. Her academic journey began with undergraduate and master's studies in Software Engineering and Information Technologies (2013-2018) at the University of Novi Sad's Faculty of Technical Sciences, followed by doctoral studies in Computing and Automation (2018-present). Undergraduate: Software Engineering and Information Technologies (2013-2017), University of Novi Sad Master's: Software Engineering and Information Technologies (2017-2018), University of Novi Sad Doctoral: Computing and Automation (2018-present), University of Novi Sad Her research focuses on Artificial Intelligence and Machine Learning, with applications in semantic segmentation, real estate price prediction, medical imaging, and educational technology. She has co-authored multiple conference papers, including works on generative models, stereo image analysis, and automated breast cancer risk assessment. Aleksandra contributes to interdisciplinary projects involving dataset creation, algorithm development, and software systems for research and healthcare domains.

• Computer Vision
• Object Recognition
• Scene Understanding
• +۷ مورد دیگر

Derek Hoiem is a Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Siebel School for Computing and Data Science since 2009. He holds a PhD in Robotics from Carnegie Mellon University (2007) and completed a Beckman Postdoctoral Fellowship (2008). As co-founder and Chief Science Officer of Reconstruct, he develops AI-driven tools for construction progress monitoring. His research focuses on computer vision, object recognition, scene understanding, and graphics. Key honors include IEEE Fellow (2022), University Scholar (2022), and the Sloan Research Fellowship (2013). He has received NSF CAREER and Intel Early Career awards, alongside best paper awards at CVPR (2006) and WACV (2015). His teaching excellence is reflected in numerous 'List of Teachers Ranked as Excellent' accolades across multiple semesters. Research interests span 3D reconstruction, neural networks, and multimodal models. His work addresses challenges in visual program generation, neural radiance fields, and construction monitoring through photogrammetry. He leads projects in continual learning, efficient AI systems, and explainable multimodal interactions. Professional contributions include grants from NSF and industry partnerships. He advises on cutting-edge AI applications in construction, graphics, and vision-language integration. His lab’s innovations bridge theoretical advancements with real-world systems like Reconstruct’s Visual Command Center.

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

C. H. T. Child is an Associate Professor in the Department of Computer Science at City, University of London, with a distinguished research career spanning over two decades. The academic maintains an active research profile with publications extending through 2025, demonstrating continued scholarly contributions across multiple domains of computer science and artificial intelligence. Child's primary research interests center around Reinforcement Learning , Artificial Intelligence , and Computer Vision , with specialized expertise in non-Euclidean geometry applications for gaming environments, medical informatics, and human-computer interaction. The research portfolio reveals a consistent trajectory from foundational work in reinforcement learning algorithms to applied research in game AI, medical informatics, and scene representation learning. A notable research thread involves the application of non-Euclidean geometry to create novel gaming experiences, while another significant strand focuses on developing advanced AI systems for non-player characters with personality modeling capabilities. Analysis of the most recent publications (2019-2025) shows an evolution toward more sophisticated deep learning architectures applied to complex problems in medical informatics and scene representation. The work has increasingly incorporated transformer networks, attention mechanisms, and advanced feature extraction techniques, reflecting broader trends in the field while maintaining the researcher's distinctive focus on reinforcement learning fundamentals. The publication record demonstrates successful translation of theoretical AI concepts into practical applications across gaming, healthcare, and computer vision domains. Child has supervised multiple doctoral students and research collaborators, with several co-authors appearing consistently across publications as first authors on significant works. The research has been supported through university resources and likely external funding, though specific grant details are not provided in the available materials. The academic maintains an active presence in both theoretical and applied research communities, with publications spanning top conferences and journals in computer science and AI. The researcher leads work in the Extreme AI Personality Engine project and contributes to computer vision research with applications in hand pose estimation and medical imaging. These research streams are well-integrated, with reinforcement learning principles providing a unifying theoretical framework across diverse application domains.

• Multi-Dimensional Signal Processing
• System Modelling and Identification
• Image and Video Processing
• +۵ مورد دیگر

Mårten Sjöström is a Professor in Signal Processing at Mid Sweden University, where he serves as the highest representative of the research subject Computer and System Sciences and is part of the managerial group of the Department of Information and Communication Systems (IKS). He leads the Realistic 3D research group and has extensive experience in both academic and industrial settings. His educational background includes a Master of Science from Linköping University (Applied Physics and Electrical Engineering, 1992), a Technical Licentiate degree from the Royal Institute of Technology, Stockholm (Signal Processing, 1998), and a PhD from Ecole Polytechnique Federale de Lausanne (Modelling of Non-linear Systems, 2001). He obtained his Docent degree (Associate Professor) in 2008 and Professor's degree in Signal Processing in 2013. His primary research focuses on Multi-Dimensional Signal Processing with emphasis on System Modelling and Identification. He has successfully applied these techniques to Image and Video Processing, Multi-media Communications, and currently specializes in Multi-Scopic 3D and Light Field Technology including capture, processing, coding, and presentation/visualization. His work spans theoretical foundations to practical implementations across various application domains. His recent publication record demonstrates a clear trajectory toward advanced light field and 3D imaging technologies, with significant contributions to compression algorithms, depth estimation techniques, quality assessment metrics, and telepresence applications. His research bridges theoretical signal processing with practical industrial implementations, particularly in remote operation, mining applications, and immersive visualization systems. Best Paper Award at MMEDIA 2013 Quality Reviewer Award at ICME 2013 Professor Sjöström has supervised an extensive number of doctoral and licentiate students, with numerous current PhD candidates expected to complete their degrees in 2025. His teaching portfolio covers a wide range of subjects including Applied Signal Processing, Automatic Control, Computer Hardware and Architecture, and specialized PhD courses in Video Processing and Realistic 3D. He has led numerous research projects both current and completed, including IMMERSE, PLENOPTIMA, and various initiatives in 3D video technology and visualization. As founder and head of the Realistic 3D research group, he directs activities focused on synthesis and capture of 3D images and video, rendering techniques for virtual perspective views, system modeling for 3D capture and presentation, coding of 3D content, quality metrics and assessments, and remote control and measurement systems. The group maintains strong industrial collaborations across multiple sectors.

Zhixiang Chenمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Human Analysis
• +۲ مورد دیگر

Dr. Zhixiang Chen is a Lecturer in Machine Learning at the Department of Computer Science, University of Sheffield, and an active member of both the Machine Learning and Computer Vision research groups. Previously, he held postdoctoral positions at Imperial College London and Tsinghua University. Education: PhD in Computer Science, Tsinghua University, China B.Eng. in Computer Engineering, Xi'an Jiaotong University, China His research focuses on advancing Computer Vision and Machine Learning through human analysis, object detection, and image retrieval systems. Recent work integrates deep learning for industrial applications like PCB inspection and autonomous systems, while earlier contributions pioneered video hashing techniques for efficient multimedia search. His methodology emphasizes robust feature representation and scalable algorithm design. Publication trends reveal a strategic shift from foundational hashing research (2016-2019) toward applied 3D vision and industrial automation (2020-2024), with increasing emphasis on uncertainty-aware models and sensor fusion for real-world deployment. Scientific Awards: China Society of Image and Graphics Doctoral Dissertation Award (2019) IEEE International Conference on Multimedia and Expo Best Paper Award (2018) China National Postdoctoral Program for Innovative Talents (2017) Dr. Chen currently leads the EPSRC-funded project 'VIM: towards a Vision based System for Inventory Management with Deep Learning' (2024-2026, £46,354) as Principal Investigator. He serves as a Grant Reviewer for UKRI-FLF and NSFC, Senior Program Committee Member for AAAI 2023, and regularly reviews for top-tier conferences (CVPR, ICCV, NeurIPS) and journals (IEEE T-PAMI, IEEE T-MM). As a core member of Sheffield's Machine Learning and Computer Vision research groups, he contributes to collaborative projects involving industrial partners and interdisciplinary teams focused on AI-driven solutions for healthcare, manufacturing, and autonomous systems.

Manolis Sangriotisمشاهده پروفایل

دانشیار

• Image Processing
• Computer Vision
• Stereo Image Coding
• +۴ مورد دیگر

Manolis Sangriotis is an Associate Professor at the National and Kapodistrian University of Athens, affiliated with the Department of Informatics and Telecommunications. His academic journey began with a B.Sc. in Physics (1970), followed by M.Sc. degrees in Electronics and Radioelectrology (1973) and Electronic Automation (1975), culminating in a Ph.D. in Informatics and Telecommunications (1980) from the University of Athens. His research focuses on image processing , computer vision , and 3D imaging , with emphasis on stereo image coding, integral photography, and real-time compression architectures. He explores applications in medical imaging, autostereoscopic displays, and multimedia systems, leveraging wavelet transforms, morphological operations, and statistical clustering. Recent publications (2012–2004) reveal a strong trend toward integral imaging innovations , including rectification frameworks, watermarking, and 3D-DCT compression. His work consistently addresses challenges in multiview data representation, disparity estimation, and photorealistic rendering, bridging computer vision with optical engineering. Scientific Awards: Best Paper Award at IEEE 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (2012) He actively advises PhD students, including Eirini Kostopoulou, Tzanoukos Georgios, and Papageorgiou Louis. His research group collaborates on projects related to real-time systems and image datasets, often supported by institutional grants. As part of the RTSImage laboratory, he contributes to developing computational tools for image analysis and compression, with applications spanning medical diagnostics and 3D display technologies. Future directions include advancing omnidirectional imaging and robustness in automated biomedical assessments.

Joohee Kimمشاهده پروفایل

دانشیار

• Image and Video Signal Processing
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Joohee Kim is an Associate Professor in the Department of Electrical and Computer Engineering at Illinois Institute of Technology, where she has been since 2009. She directs the Multimedia Communications Laboratory and focuses on research funded by U.S. Federal Agencies and the Korean Government. Education: Ph.D., Electrical and Computer Engineering, Georgia Institute of Technology (2003) M.S., Electrical Engineering, Yonsei University (1993) B.S., Electrical Engineering, Yonsei University (1991) Research interests include image/video signal processing, computer vision, machine learning, multimedia communication, and real-time 3D reconstruction . Her work spans applications in autonomous systems, robotics, and advanced driver assistance systems, emphasizing low-complexity algorithms and efficient coding techniques. Her recent articles highlight advancements in pedestrian detection via deep learning, 3D reconstruction fusion methods, and optimized depth map coding. These contributions address challenges in real-time systems, energy efficiency, and robust transmission over wireless networks. Awards: None explicitly listed. Grant Activity: Active in federal and international funding for projects like low-delay distributed video coding and error-resilient transmission. Advising: No students listed, though lab leadership implies mentorship roles. Labs/Teams: Director of the Multimedia Communications Lab, collaborating on 3D imaging, video coding, and intelligent systems integration.

Mladen Vidovićمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Data Science
• +۳ مورد دیگر

Mladen Vidović is a researcher at the Faculty of Informatics and Computing, Singidunum University. His work focuses on artificial intelligence, machine learning, and applied informatics, with a particular emphasis on synthetic dataset generation, remote rendering, and educational technology. Articles : 12 recent publications covering AI applications, semantic segmentation, and data science Research Interests : Synthetic data creation, machine learning workflows, and interactive software systems His research outputs include collaborations on tools like Autonomous Grader for educational assessment and Microraptor Gui for remote rendering monitoring. Notable projects involve leveraging Kohonen Self-Organizing Maps for anomaly detection and developing end-to-end learning approaches for stereo vision tasks. While no explicit awards or student advisories are documented in the available texts, his publications indicate active participation in academic conferences such as Sinteza and ICIST, contributing to advancements in AI and informatics.

• Numerical Methods
• Rock Mechanics
• Geomechanics
• +۳ مورد دیگر

Klaus Thoeni is an Associate Professor in Civil Engineering and Associate Dean Research (Industry Engagement) at the University of Newcastle's School of Engineering. With over 15 years of expertise, he specializes in advanced numerical methods including Boundary/Discrete/Finite Element Methods applied to rock mechanics, geomechanics, mining engineering, and underground excavations. His research focuses on: Computational geomechanics and granular mechanics Rockfall protection systems and slope stability Photogrammetry and 3D monitoring Particle-based modeling of geomaterials He currently teaches courses such as ENGG1002 (Introduction to Engineering Computations) and CIVL2050 (Engineering Computations and Probability), and supervises final-year projects. Recent articles demonstrate strong focus on: Rock slope monitoring and hazard assessment DEM-BEM coupling techniques Industrial safety in mining operations Uncertainty quantification in granular systems Awards and honors include: Industry Engagement Excellence Award (2021) Excellence Award for Research Supervision (2020) Outstanding Research Award (2017) IACMAG Excellent Paper Award (2014) Amann Award (2003) He leads industry-funded projects and contributes to the open-source discrete element software YADE. His team develops photogrammetric monitoring systems for geotechnical applications, with focus on mining and infrastructure safety.

Hamid Karimi-Rouzbahaniمشاهده پروفایل

پژوهشگر ارشد

• Computational Neuroscience
• Cognitive Neuroscience
• Clinical Neuroscience
• +۱۹ مورد دیگر

Dr. Hamid Karimi-Rouzbahani is a Research Fellow and ARC DECRA Fellow at the Queensland Brain Institute, Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland. He previously held a Newton Fellowship at the MRC Cognition and Brain Sciences Unit, University of Cambridge, highlighting his international research experience. His research lies at the intersection of Computational, Cognitive, and Clinical Neuroscience , integrating neural signal processing (EEG, MEG, fMRI), machine learning (deep neural networks), and mathematical modelling . He develops advanced methods for multidimensional connectivity and decoding analysis to understand information coding and transfer in the brain. His cognitive research focuses on visual perception, attention, and the multiple-demand system , while his clinical work aims to quantify and localize brain areas involved in epilepsy . The analysis of his recent publications reveals a strong trend in developing and applying computational models to both fundamental cognitive processes and clinical applications, particularly in epilepsy. His work consistently bridges methodological innovation with empirical neuroscience, using multimodal data to extract meaningful neural signatures. Scientific Awards: ARC DECRA Fellow (Discovery Early Career Researcher Award) Newton Fellowship, MRC Cognition and Brain Sciences Unit, University of Cambridge Dr. Karimi-Rouzbahani is actively involved in research supervision, currently serving as the Principal Advisor for a PhD project on developing novel information decoding and tracking methods. He is the lead investigator on an ARC-funded project (2023–2029) titled "Characterising brain networks of intelligence through information tracking," which demonstrates his success in securing competitive research grants. He is available for supervision and collaborates with researchers across various institutions. His work is conducted within the Queensland Brain Institute , a world-leading neuroscience research centre, where he contributes to collaborative projects like #EEGManyLabs, emphasizing reproducibility in EEG research.

• Image and Video Processing
• Multimedia Signal Processing
• Light Field Imaging
• +۷ مورد دیگر

Luís Ducla Soares is an Associate Professor in the Department of Information Science and Technology at ISCTE – University Institute of Lisbon, where he has been a faculty member since 2000. He is also an Integrated Senior Researcher and the Coordinator of the Multimedia Signal Processing Group (MSP-Lx) at the Institute of Telecommunications - IUL. His academic appointments reflect a strong integration between teaching, research, and institutional leadership. Education: Bachelor's Degree in Electrical and Computer Engineering, Instituto Superior Técnico (1996) PhD in Electrical and Computer Engineering, Instituto Superior Técnico (2004) Aggregate Degree in Information Sciences and Technologies, ISCTE-IUL (2020) His primary research interests lie in image and video processing , light field imaging , multimedia coding , and biometric recognition systems . He has made significant contributions to scalable and error-resilient coding techniques, particularly within the MPEG standardization framework. His recent work extends into biomedical applications of video processing, such as gait analysis and remote diagnostics. The analysis of his recent publications shows a clear trend toward light field processing and deep learning applications in biometrics . His work combines theoretical signal processing with practical implementations in immersive media and healthcare. The integration of AI in 4D light field segmentation and gait classification highlights his interdisciplinary approach. Scientific Awards: Alcatel – Prof. Carvalho Fernandes Award for best student in Telecommunications and Electronics IBM Scientific Award (2016) for a master's dissertation He has supervised 2 completed PhD theses and 14 master's dissertations, with 3 PhDs and 2 master's theses currently in progress. He has led multiple funded research projects, including national and European initiatives such as LIMESA, LIFESYS, QUIS-CAMPI, and COST actions. His editorial roles include serving as Associate Editor for Signal Processing (Q1 journal) since 2018. He has also played key roles in scientific event organization, including co-chairing the International Workshop on Forensics and Biometrics (IWBF) in 2013. Research Labs and Teams: Multimedia Signal Processing Group (MSP-Lx), Institute of Telecommunications - IUL Coordinator since 2021 Active in developing tools for light field coding, biometrics, and immersive media

• Multimedia Signal Processing
• Light Field Coding
• 3D Holoscopic Video
• +۷ مورد دیگر

Paulo Jorge Lourenço Nunes is an Associate Professor in the Department of Information Science and Technology at ISCTE – Instituto Universitário de Lisboa, where he also serves as Coordinator of the Multimedia Signal Processing Group at the Institute of Telecommunications - IUL. He is an Integrated Researcher at the same institute, contributing to advanced research in multimedia and signal processing. PhD in Electrical and Computer Engineering, Higher Technical Institute - UTL (2007) Master's in Electrical and Computer Engineering, Higher Technical Institute - UTL (1995) Bachelor's in Electrical and Computer Engineering, Higher Technical Institute - UTL (1992) His research focuses on multimedia signal processing , particularly in light field coding , 3D holoscopic video , and scalable video representation . He applies deep learning and advanced signal processing techniques to problems in immersive systems and image compression. His work bridges theoretical innovation with practical implementation in next-generation visual media. The recent articles highlight a strong trend in light field processing , including segmentation, coding, disparity estimation, and view synthesis using neural networks. Keywords across publications include computer vision, image compression, deep learning, and immersive media, reflecting a cohesive and impactful research trajectory in next-generation visual technologies. Notable scientific contributions include highly cited works in IEEE Transactions and Signal Processing journals, with over 1300 citations on Google Scholar. While no formal awards are listed, his extensive publication record and leadership in funded research projects underscore significant recognition in the field. He actively supervises postdoctoral researchers, PhD, and Master’s students. His advising portfolio includes ongoing and completed theses on light field coding, deep learning for immersive systems, and multimedia communication. He has also secured and led multiple research projects in multimedia and telecommunications. His teaching includes courses such as Multimedia Communication Systems and Fundamentals of Computer Networks. He leads the Multimedia Signal Processing Group at the Institute of Telecommunications - IUL, a research team focused on advanced multimedia analysis, compression, and delivery. The group works on cutting-edge topics including light field technologies, error-resilient video coding, and machine learning for multimedia.