جستجوی استاد راهنمای دانشگاهی

• Image Compression
• JPEG2000
• Signal Processing
• +۵ مورد دیگر

Professor David Taubman is a distinguished academic serving as Professor and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at the University of New South Wales (UNSW) in Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. With a career spanning over three decades, Professor Taubman has made significant contributions to the field of image and video compression, most notably as the author of the EBCOT coding algorithm adopted in the JPEG2000 international standard. Professor Taubman earned his B.Sc. in Mathematics and Computer Science (1986) and B.E. (Medal) in Electrical Engineering (1988) from the University of Sydney, followed by an M.Sc. (1992) and Ph.D. (1994) in Electrical Engineering from the University of California at Berkeley. His professional journey includes engineering work at the Electricity Commission of N.S.W. (1988-1990), research positions at Hewlett-Packard Laboratories in Palo Alto (1994-1998), and an academic career at UNSW where he progressed from Senior Lecturer (1998-2003) to Associate Professor (2004-2009) and finally to Professor (2009-present). He has held various leadership roles including Head of the EE&T Telecommunications Research Group (2003-2014), Head of the EE&T Signal Processing Research Group (2014-present), Director of Research for the School of EE&T (2011-2016), and Deputy Head of School (Research) since 2017. Professor Taubman's research interests center on image and video compression, with particular expertise in JPEG2000 standards and implementations. His work spans signal processing, wavelet transforms, scalable video coding, motion modeling, and multimedia systems. He has pioneered numerous compression algorithms and frameworks, including the EBCOT coding algorithm that became central to the JPEG2000 standard. His recent research focuses on efficient motion modeling with cuboidal partitioning, learned lifting-based transform structures, and high-throughput implementations of JPEG2000 for video applications. His work bridges theoretical foundations with practical implementations, as evidenced by the commercially successful Kakadu Software tools that have garnered around 500 commercial licensees. Analysis of Professor Taubman's recent publications reveals a consistent focus on advancing compression technologies with particular emphasis on scalability, efficiency, and adaptability. His work spans traditional image compression (JPEG2000 extensions), video coding (cuboid-based partitioning for UHD/360-degree video), and emerging applications (nanopore sequencing data compression). A notable trend is the integration of machine learning techniques with traditional compression frameworks, as seen in his work on learned lifting-based transform structures. His research maintains strong connections to real-world applications across diverse domains including medical imaging, astronomical data processing, and genomic sequencing. IEEE Fellow Engineers Australia Fellow (by invitation) Professor Taubman has served as Associate Editor for the IEEE Transactions on Image Processing for two four-year appointments (2003-2005 and 2010-2013). He has been actively involved in numerous research grants focused on image and video compression technologies, particularly those related to the JPEG2000 standard and its extensions. His work has received significant industry support, reflected in his consultancy with various U.S., Japanese, and Australian corporations. He has also contributed to international standards development as a member of Standards Australia Technical Committee MS-065 (mirroring ISO TC42 on Digital Photography) and as a constitutional member of Standards Australia Technical Committee IT-029 (Coded Representation of Picture, Audio and Multimedia/Hypermedia Information). Professor Taubman co-directs Kakadu Software Pty. Ltd. and its research affiliates Kakadu R&D and Kakadu GPU, which have developed the commercially successful Kakadu Software tools for JPEG2000. His research group at UNSW focuses on advanced image and video compression techniques, with particular expertise in wavelet-based methods, scalable coding, and motion modeling. The group maintains strong industry connections and has contributed significantly to the development and standardization of image compression technologies worldwide.

Jian Zhangمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Data Analytics
• +۳ مورد دیگر

Prof. Jian Zhang is a Professor in the School of Electrical and Data Engineering at the University of Technology Sydney (UTS), specializing in computer vision, pattern recognition, and multimedia signal processing. He leads the Multimedia Data Analytics Lab at the Global Big Data Technologies Centre, focusing on agri-food sector applications such as livestock monitoring and AI-driven solutions for agricultural efficiency. Education : PhD, School of Information Technology and Electrical Engineering, University of New South Wales, 1999 MSc, The Flinders University of South Australia, 1994 BSc, East China Normal University, 1982 Research Interests : His work spans 2D/3D computer vision, large-scale image/video analytics, and cross-disciplinary projects in agriculture and remote sensing. He has pioneered AI systems for livestock counting, poultry welfare monitoring, and fish quality assessment, funded by organizations like Meat & Livestock Australia and Australian Eggs. Grants & Projects : Current projects include AI-based hen health monitoring ($5M+ funding since 2011) Collaborations with industry partners like Sydney Fish Market and Fremantle Port Students & Academic Leadership : Supervised 19 PhD graduates and 5 research fellows Recruiting new PhD candidates in computer vision and data analytics Labs & Teams : Director of the Multimedia Data Analytics Lab, collaborating with global experts through UTS's Distinguished Visiting Scholars program.

Zhenghao Chenمشاهده پروفایل

مدرس

• Computer Vision
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Dr. Zhenghao Chen is a Lecturer in Data Science at the University of Newcastle, affiliated with the School of Information and Physical Sciences. He earned his Ph.D. from the University of Sydney in 2022, following a B.Eng. H1 degree from the same institution in 2017. Prior to his current position, Dr. Chen served as a Postdoctoral Research Fellow at the University of Sydney (2022-2024), a Research Engineer at TikTok (2024), and as a Visiting Research Scientist at Microsoft Research and Disney Research (2022-2023). Dr. Chen's educational background includes: Doctor of Philosophy, University of Sydney (2022) Bachelor of Information Technology (B.Eng. H1), University of Sydney (2017) Dr. Chen's research spans multiple domains within artificial intelligence, with particular expertise in Computer Vision, Natural Language Processing, and Machine Learning. His work in Generative AI has garnered significant recognition, with applications in both academic and industrial settings. His research interests are reflected in his Fields of Research percentages: Deep Learning (30%), Computer Vision (30%), Natural Language Processing (20%), and Multimodal Analysis and Synthesis (20%). His publications in top-tier venues like CVPR, ICCV, ECCV, and journals like IEEE TPAMI demonstrate the breadth and impact of his work. Analysis of Dr. Chen's recent publications (2022-2025) reveals a consistent focus on neural compression techniques, 3D perception, and multimodal AI systems. His work spans medical imaging (CXR bone suppression), video compression, point cloud processing, and neural surface reconstruction. A notable trend is his exploration of efficient AI systems that work well under resource constraints, as evidenced by his involvement in the EMCLR workshop. His research often bridges theoretical advances with practical applications across multiple domains. Dr. Chen has received several prestigious awards: Microsoft Research Asia StarTrack Fellowship (2025) ACM SIGMM Award for Outstanding PhD Thesis in Multimedia Computing (2024) Australia Government Research Training Program (RTP) Fellowship (2019) Google Australia Prize for Excellence in Computer Science (2017) Dr. Chen is actively involved in the academic community, serving on the Program Committee for major AI conferences including CVPR, ICCV, ECCV, SIGGRAPH, AAAI, and others. He also organizes workshops in Multimedia and ICCV conferences, and serves as a reviewer for prestigious journals. His teaching responsibilities include courses on Intelligent Visual Signal Understanding, Video Intelligence and Compression, Database and Information Management, and Computing Fundamentals at both the University of Sydney and University of Newcastle.

• Artificial Intelligence
• Computer Vision
• Digital Agriculture
• +۳ مورد دیگر

Ferdous Sohel is a Professor of Information Technology at Murdoch University and inaugural lead of the Agricultural Technologies program. His research spans AI, computer vision, and digital agriculture, with applications in medical imaging and environmental monitoring. He received the Mollie Holman Doctoral Medal and Vice Chancellor's Early Career Research Award. Research Impact: Developed innovative AI models for aquaculture oxygen prediction, 3D object tracking, quantum neural networks, and prohibited item detection. His work advances precision agriculture through hyperspectral classification frameworks and irrigation decision systems. Professional Service: Associate Editor for IEEE Transactions on Multimedia and senior IEEE member. Current projects include adversarial robustness for LiDAR systems and lightweight dormitory security networks.

• Biomedical Data Computing
• Medical Image Processing
• Image Registration
• +۴ مورد دیگر

Associate Professor Xiu Wang is affiliated with the School of Computer Science at The University of Sydney, where she serves as Associate Director of the Multimedia Lab and a member of the Biomedical & Multimedia Information Technology (BMIT) Research Group. Her research focuses on panoramic data analysis, biomedical computing, image processing, and medical data fusion. Key projects include tumor treatment outcome prediction using deep learning, collaborative learning of multimodal medical imaging, and deformable image registration techniques. She teaches courses such as COMP5214 (Software Development in Java) and supervises graduate students in AI-driven medical diagnostics and imaging. Notable collaborations involve Dr. Hui Cui and Mr. Chaojie Zheng. Her work bridges computer science and healthcare, emphasizing AI applications in oncology, radiology, and neuroimaging. Recent articles highlight advancements in MRI segmentation, PET/CT radiomics, and graph neural networks for disease diagnosis. She actively contributes to interdisciplinary research, integrating computational methods with clinical data for precision medicine.

• Deep Learning
• Medical Image Analysis
• Bio Data Analysis
• +۵ مورد دیگر

Dr. Sicily Fung Fung Ting is a Lecturer at the School of Information Technology (SoIT), Monash University. She earned her Ph.D. in Engineering (2020) from Multimedia University, specializing in breast cancer classification and visualization using deep neural networks. Her research focuses on deep learning applications in medical image analysis, expert systems, and biomedical engineering. Notably, her work has been recognized with the 2020 WSIS Prize (Champion) and the 2023 FIT Dean's Education Excellence Award. Education: Ph.D., Engineering (2020), Multimedia University MEng.Sc., Visualisation for Early Brain Infarct Detection (2017), Multimedia University BEng (Hons), Electronics & Robotics (2014), Multimedia University Research Interests: Deep Learning for Medical Imaging Computer-Aided Detection (CAD) Telehealth Systems Biomedical Signal Processing Key Projects: FRGS 2022: RewardQNet Framework for Intracranial Aneurysm Segmentation GEMS 2025: AI-Driven Neuroimaging Tool for Aneurysm Detection Her publications span journals like Expert Systems with Applications and Computerized Medical Imaging and Graphics , with impactful contributions to breast cancer classification and brain lesion segmentation. She actively engages in academic outreach, including public lectures and industry collaborations.

• Multimedia Computing
• Computer Vision
• Computer Graphics
• +۷ مورد دیگر

Professor Zhiyong Wang is a faculty member at the School of Computer Science, University of Sydney, serving as Deputy Director of Sydney Informatics Hub and Director of the Multimedia Computing Laboratory. His research focuses on multimedia computing, including information retrieval, computer vision, AI, and applications in agriculture, healthcare, and environmental monitoring. He holds a B.Eng., M.Eng. from South China University of Technology, and a Ph.D. from The Hong Kong Polytechnic University. His work bridges theoretical advancements and real-world applications, with over 200 peer-reviewed publications. He is a member of IEEE and former President of Australia Pattern Recognition Society (APRS). Research Interests: Enabling computers to understand and create multimedia content, with emphasis on human-centric computing, remote sensing, and interdisciplinary applications. Notable areas include action recognition, medical image analysis, and environmental monitoring. Current Roles: Leads the Multimedia Computing Lab, collaborates with Sydney Institute of Agriculture and Brain and Mind Centre. Teaches COMP5216 (Mobile Computing), COMP5405 (Digital Media Computing), and COMP5425 (Multimedia Retrieval). Students: Supervises 9 Ph.D. students focusing on topics like precision agriculture, biomedical engineering, and AI-driven video analytics. Key projects include soil moisture forecasting, biomechanical risk analysis for knee osteoarthritis, and explainable machine learning.

Jing Zhangمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Generative AI
• +۵ مورد دیگر

Dr. Jing Zhang is a Lecturer in the School of Computing at the Australian National University (ANU) , within the ANU College of Systems & Society . Previously, he served as a Research Fellow at ANU (2021–2022) and earned his PhD in 2021 under the supervision of Nick Barnes. His academic journey includes a Master’s (2010) and Bachelor’s (2007) from Northwestern Polytechnical University . Education: PhD, Australian National University, 2021 (Supervisor: Nick Barnes) Master’s Degree, Northwestern Polytechnical University, 2010 Bachelor’s Degree, Northwestern Polytechnical University, 2007 Research Interests: Jing Zhang’s research focuses on computer vision and machine learning , with a particular emphasis on generative AI . His work addresses challenges in image, video, and audio generation/editing; explainable model adaptation; out-of-distribution detection; and adversarial attacks/defenses. He explores techniques to enhance model robustness and generalization across diverse domains. Awards & Recognition: CVPR 2020 Paper Award Nominee for “UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders” Teaching & Supervision: Jing Zhang teaches COMP8536: Advanced Topics in Computer Vision (Semester 2, 2024) and ENGN4528/6528: Computer Vision (Semester 1, 2025). He currently supervises 4 PhD students as their primary advisor, focusing on cutting-edge research in computer vision and AI. Professional Contributions: Co-organizer of tutorials on salient object detection (ACCV 2019) and uncertainty estimation (ICCV 2021) Regular reviewer for top venues: CVPR, ICCV, ECCV, TPAMI, IJCV, and others Research Groups: While not explicitly stated, his work is likely affiliated with ANU’s computer vision research groups, focusing on generative AI and robust machine learning systems.

Shyh Wei Tengمشاهده پروفایل

دانشیار

• Image Processing
• Machine Learning
• Bioinformatics
• +۳ مورد دیگر

Assoc Prof Shyh Wei Teng is an Associate Professor and Deputy Dean at Federation University Australia's Institute of Innovation, Science and Sustainability (IISS). He holds a Bachelor of Computing (First-class Honours) and PhD in Information Technology from Monash University (2004), and a Graduate Certificate in Higher Education (2008). His research focuses on image processing, machine learning, bioinformatics, and computer vision, with notable contributions to LiDAR-based building extraction, SIFT algorithm optimization, and multimodal image registration. He has secured significant grants, including an Australian Research Council (ARC) Discovery Project (2013). Prof Teng has supervised numerous PhD and master’s students, contributing to advancements in image retrieval, clustering algorithms, and biomedical applications. His work has been published in top-tier journals like Pattern Recognition and Neurocomputing , and he actively reviews for IEEE conferences. He taught courses in database systems, project management, and systems analysis, receiving teaching and community engagement nominations. Research interests span computational techniques for medical imaging, contour-based corner detection, and machine learning applications in remote sensing. His labs and collaborations include projects on augmented reality for education and ecological data analysis.

• Multimedia
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Dr. Min Xu is a Professor at the School of Electrical and Data Engineering (SEDE) within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). She leads the Visual and Aural Intelligence Laboratory at UTS's Global Big Data Technologies Centre (GBDTC). Her research focuses on multimedia, computer vision, and machine learning, with recent emphasis on machine learning algorithms for data analytics, computer vision applications, and CV/RF integrated sensing for healthcare and smart homes. Education: Doctorate of Philosophy, University of Newcastle, Australia Master of Computing, National University of Singapore Bachelor of Engineering, University of Science and Technology of China Dr. Xu has authored over 250 publications in top-tier venues like IEEE T-PAMI and CVPR, secured over $5M in research funding since 2018, and holds editorial roles at IEEE Transactions on Multimedia and Neurocomputing. Her work bridges theory and application, addressing challenges in multi-modality data fusion, personalized services, and affective computing. Key Contributions: Pioneer in multimedia affective/semantic analysis and multi-modality fusion Developed frameworks for privacy-preserving cross-domain recommendation and WiFi-based human tracking Recipient of ARC Future Fellowship (2018–present) She supervises PhD and Master’s students and teaches courses including Image Processing and Cloud Computing. Current projects emphasize AI-driven healthcare solutions and multimodal AI search engines.

• Image Processing
• Signal Processing
• 3D Environments
• +۴ مورد دیگر

Stuart Perry is a Professor at the University of Technology Sydney (UTS), serving as Head of Discipline for Signal Processing and Analytics in the School of Electrical and Data Engineering. He holds affiliations with UTS' Faculty of Engineering and Information Technology, the Global Big Data Technologies Centre, and the Visualization Institute. With over 20 years of experience, his career spans roles at DSTO and Canon Information Systems Research Australia (CiSRA), focusing on image processing, signal processing, and perceptual quality measurement. Perry co-directs the Perceptual Imaging Laboratory (PILab), researching 3D environments, light field technologies, and human perception in immersive realities. He actively contributes to international standards committees like ISO/TC42 and ISO/SC29/WG7, leading JPEG's point cloud coding efforts. His research emphasizes adaptive image processing, machine learning-driven object detection, and medical imaging applications. Perry has authored 60+ publications, two books, and 20 patents. Current projects include VR empathy case studies, point cloud compression, and disaster management digital transformation. Education: PhD in Engineering, University of Sydney (1999) Research Interests: His work bridges computational imaging and human perception, addressing challenges in augmented/virtual reality (AR/VR), 3D scanning, and immersive media. Key areas include light field and point cloud coding, psychophysics of visual perception, and medical diagnostics via machine learning. He explores how perceptual principles can optimize interactive technologies for education, healthcare, and entertainment. Recent Research Trends: Recent articles focus on 3D Gaussian splatting, glaucoma detection via deep learning, and JPEG Pleno standards for plenoptic imaging. His work balances technical innovation (e.g., efficient point cloud compression) with human-centric design (e.g., reducing VR motion sickness through display lag analysis). Awards & Recognition: Member of IEEE and founding SPINet participant. Over 60 refereed publications and 20 patents highlight his industry-academic impact. Grants & Leadership: Leads SmartSat CRC projects on yield estimation and Aus4innovation-funded disaster response tech. Manages grants totaling millions AUD. Editorial roles include Associate Editor of SPIE/IS&T Journal of Electronic Imaging. Labs & Collaborations: PILab collaborates internationally on perceptual imaging standards. Active in ISO committees shaping future media technologies.

Manoranjan Paulمشاهده پروفایل

استاد

• Artificial Intelligence
• Video/Image Processing
• Machine Vision
• +۸ مورد دیگر

Manoranjan Paul is a Full Professor in Computer Science at the School of Computing, Mathematics, and Engineering, Charles Sturt University (CSU), Australia. He is Director of the Computer Vision Lab and Head of the Machine Vision & Digital Health (MAVIDH) Research Group. Paul holds a PhD from Monash University (2005) and has held academic and research roles at institutions including UNSW, Nanyang Technological University (Singapore), and Ahsanullah University of Science and Technology. His research spans Artificial Intelligence , Video/Image Processing , Quantum Computing , Digital Health , and Medical Imaging . Notable contributions include $10M+ in grants (e.g., ARC Discovery Projects, Japanese grants), 250+ refereed publications, and commercialized apps like VineSignal for vine disease detection and Hunter Gatherer Networks for Indigenous marketplaces. Awards include the Golden Disruptor Award and ICT Researcher of the Year 2017 (ACS), and recognition as a World's Top 2% Scientist (Elsevier/Stanford, 2021). Key research initiatives: EEG-based epilepsy prediction ( 90% accuracy ) Quantum image compression ( novel circuits ) Hyperspectral soil analysis ( regression models ) Non-contact cardiopulmonary detection ( smartphone-based ) Supervised 19 PhD students and led international conferences (e.g., PSIVT-19, DICTA-2023). Editor for IEEE Transactions on Image Processing and Multimedia, and Steering Committee Chair for PSIVT.

• Remote Sensing
• Image Processing
• Multimedia Security
• +۷ مورد دیگر

Dr. Mohammad Awrangjeb is a Senior Lecturer at the School of Information and Communication Technology , Griffith University, with over 15 years of experience in academia and research. He holds a PhD from Monash University (2008) and has held academic positions at Federation University Australia (2014-15), Monash University (2012-13), and the University of Melbourne (2008-12). Education: PhD in Computer Science, Monash University (2008) MSc in Computer Science, National University of Singapore (2004) BSc (Engineering) in Computer Science, Bangladesh University of Engineering and Technology (2001) His research focuses on remote sensing data processing , including automated 3D city modeling, power line corridor monitoring, solar potential estimation on buildings, forest biomass estimation, hyperspectral image processing, multimedia security, EEG signal processing, and medical image analysis. He has co-authored over 80 publications and secured $435,000 in research funding, including an Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA, 2012-15) and grants from CRCSI and Griffith University. His work has been applied in environmental monitoring, energy planning, and infrastructure safety. Scientific Awards: Discovery Early Career Researcher Award (DECRA Fellow) (2012-2015) The Len Curtis Award (2017) Dean’s Highly Commended Certificate, Griffith Sciences (2020, 2019) He actively supervises PhD students in areas like hyperspectral imaging , forest biomass estimation , intrusion detection , and medical image segmentation . He serves on editorial boards and committees, including IEEE and the Remote Sensing and Photogrammetry Society, and has contributed to software development at CRCSI.

• Data Analytics
• Machine Learning
• Image Processing
• +۵ مورد دیگر

Dr. Subrata Chakraborty is a Senior Lecturer at the University of New England (Australia) and a Visiting Fellow at the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney (UTS). He holds a PhD in Decision Support Systems from Monash University and has previously worked at the University of Southern Queensland, Charles Sturt University, and Queensland University of Technology. His research focuses on Data Analytics, Machine Learning, Image Processing, Optimization, and Decision Support Systems, with applications in health, agriculture, business, and education. Dr. Chakraborty’s work spans diverse domains, including automated disease detection (e.g., schizophrenia, ADHD, Alzheimer’s), crop yield prediction using remote sensing, and lung nodule classification in medical imaging. He has secured grants from the NSW Defence Innovation Network for projects on AI-based risk management and deep learning model optimization. He actively supervises Masters and PhD students and contributes to academic leadership roles, including conference organizing and peer-reviewing for journals like IEEE Transactions on Multimedia . His teaching expertise includes Information Systems, Data Analytics, Complex Systems, and Project Management. He is a member of the Australian Computer Society and IEEE, reflecting his commitment to advancing computational and engineering practices in academia and industry.

• Machine Learning
• 3D Computer Vision
• 3D Multimedia Signal Processing
• +۵ مورد دیگر

Professor Wanqing Li is a leading academic in machine learning and 3D computer vision at the University of Wollongong, where he serves as Director of the Advanced Multimedia Research Lab (AMRL). He holds a B.Sc. and M.Sc. from Zhejiang University and a Ph.D. from The University of Western Australia. His career includes roles at Motorola Labs (Senior/Principal Researcher) and visiting stints at Microsoft Research. His research focuses on 3D multimedia signal processing, human activity understanding, and medical image processing, with applications in aged care and health monitoring. He has published over 250 papers in top venues like TIP, CVPR, and AAAI, and his work has been recognized with awards including the Motorola CTO’s Award (2003). He currently leads the Centre for Artificial Intelligence (CAI) at UOW and is a co-founder of the Centre for Multimedia Signal Processing and Content Management. Education: B.Sc. (Zhejiang University), M.Sc. (Zhejiang University), Ph.D. (University of Western Australia). Research Interests: Machine Learning (statistical/deep learning), 3D computer vision (human motion analysis, 3D reconstruction), medical imaging, free-viewpoint video systems, and applications in healthcare. He pioneered 3D data-driven human activity understanding, now a cornerstone of modern computer vision. Awards & Recognition: Motorola CTO’s Award (2003), Australia’s top multimedia researcher (2021–2023), elected Associate Editor of IEEE Transactions on Image Processing (2022). Labs/Teams: Director of Advanced Multimedia Research Lab (AMRL), co-founder of CAI and Multimedia Signal Processing Centre.