جست‌وجوی استاد و پژوهشگر

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.

• Signal Processing
• Wireless Communications
• Underwater Acoustic Communications
• +۷ مورد دیگر

Professor Yue Rong is a Full Professor at Curtin University's Department of Electrical and Computer Engineering, within the School of Electrical Engineering, Computing and Mathematical Sciences. He holds editorial roles at IEEE Transactions on Signal Processing and IEEE Wireless Communications Letters. His research focuses on signal processing for communications, underwater acoustic systems, wireless networks, and healthcare IoT. Rong has authored over 140 journal and conference papers and received multiple awards, including the 2010 Young Researcher of the Year Award. Education: B.E. (Electrical Engineering), Shanghai Jiao Tong University (1999) M.Sc. (Electrical Engineering), University of Duisburg-Essen (2002) Ph.D. (Electrical Engineering), Darmstadt University of Technology (2005) Research Interests: Rong's work spans cooperative MIMO communications, underwater acoustic systems, OFDM modulation, radar-based healthcare monitoring, and secure wireless protocols. His innovations include adaptive modulation schemes for underwater environments and radar-based vital signs detection. Recent trends in his publications emphasize AI-driven signal processing for healthcare IoT and underwater optical communication systems. Awards: Best Paper Awards (WCSP 2011, APCOMM 2010) Chinese Government Award (2004) DAAD/ABB Fellowship (2001-2002) Grants & Labs: His research is supported by grants focusing on UAV-enabled data collection and underwater network optimization. He leads projects in the Distributed Data Fusion and Emerging Technologies (DDFE) lab, advancing radar-cardiography and wearable health monitoring systems.

Ghazal Bargshadyمشاهده پروفایل

مدرس

• Affective Computing
• Artificial Intelligence
• Computer Vision
• +۷ مورد دیگر

Dr. Ghazal Bargshady is a Lecturer at the University of Canberra , with expertise in Affective Computing , Artificial Intelligence , and Healthcare Technology . Her roles include teaching units such as Computer Vision, Data Analytics, and Soft Computing, as well as supervising PhD and Master by Research students in AI-driven projects for healthcare and road safety. Education: She earned her PhD in Artificial Intelligence and Computer Vision from the University of Southern Queensland in 2020. Research Interests: Dr. Bargshady specializes in Computer Vision Deep Learning Biosignal Processing Facial Expression Analysis Human Factors in AI Wearable Sensors Multimodal Data Fusion Brain–Computer Interfaces Her work addresses real-world challenges in pain assessment, depression recognition, and driver safety using cutting-edge AI models. Article Trends: Her recent publications focus on Transformer architectures , fNIRS signal analysis , multimodal pain detection , and depression severity estimation via facial video data. These studies highlight her contributions to AI in healthcare , transportation safety , and biomedical signal processing . Teaching Activities: Dr. Bargshady has lectured units including Programming for Data Science , Computer Vision , and Soft Computing , emphasizing practical AI applications.

Clinton Fookesمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Human Behavior Analysis
• +۷ مورد دیگر

Professor Clinton Fookes is a faculty member at the Queensland University of Technology (QUT) within the School of Electrical Engineering & Robotics . His research focuses on leveraging computer vision and artificial intelligence to develop automated systems that understand, anticipate, and interact with human behaviors, with applications in medical diagnostics, autonomous vehicles, defense, and industrial efficiency . Research areas include AI adaptability, multimodal biosignal analysis, and human-machine interaction Collaborates with CSIRO Data61, Defence Science and Technology Group, Orica, Airbus, and Sentient Vision Systems Develops systems for human action detection, infrastructure monitoring, and stress response prediction His work addresses critical challenges in AI deployment, such as environmental adaptability and reducing diagnostic errors in medical and autonomous systems. Recent publications highlight trends in self-supervised learning, zero-shot knowledge transfer, multimodal integration , and 3D reconstruction for healthcare , while exploring ethical AI use in sectors like mining and defense . Professor Fookes emphasizes interdisciplinary collaboration, bridging engineering, medicine, and social sciences to advance AI systems capable of real-world impact. His research agenda includes improving AI memory capabilities and explainability for safer, more reliable automation.

• Machine Learning
• Data Mining
• Computer Vision
• +۳ مورد دیگر

Professor Xue Li is a faculty member in the School of Electrical Engineering and Computer Science at the University of Queensland. His research focuses on machine learning, data mining, and their applications in healthcare, materials science, and computer vision. He has authored over 300 publications, including seminal works on knowledge graph completion, video quality enhancement, and alloy design using machine learning. His work bridges theoretical advancements with real-world applications, such as clinical diagnosis andTinyML systems. Key research interests include graph representation learning, medical informatics, and efficient algorithms for multimedia data. Notable contributions include developing commonsense-enhanced relation extraction models and frameworks for compressed video reconstruction. His research also addresses challenges in federated learning and privacy-preserving genomics. Prof. Li has collaborated extensively with industry and academia, contributing to projects in RFID systems, electronic nose pattern recognition, and cybersecurity. His work is published in top-tier venues like IEEE Transactions and ACM conferences. Despite no listed awards, his prolific output underscores academic impact.

Abhinav Dhallمشاهده پروفایل

دانشیار

• Computer Vision
• Affective Computing
• Deep Learning
• +۳ مورد دیگر

Abhinav Dhall is an Associate Professor in the Department of Data Science & AI at Monash University. His research focuses on computer vision, affective computing, and human-centered AI, with a particular emphasis on deepfake detection, multimodal analysis, and ethical AI applications. He is actively involved in organizing workshops like the Multimodal and Responsible Affective Computing (MRAC) and chairs conferences such as ACCV. Dhall accepts PhD students and has contributed significantly to datasets like AV-Deepfake1M and EmotiW challenges. His work spans topics including HDR imaging, facial expression recognition, and AI ethics in multimedia systems.

Weihao Liمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Weihao Li is a Research Fellow at The Australian National University's School of Computing, specializing in computer vision and machine learning. His research focuses on object detection, image segmentation, open-set recognition, and point cloud segmentation. He holds a Dr. rer. nat. (PhD equivalent) and is registered to supervise research students. His research interests revolve around advancing techniques for dynamic instance segmentation, open-set learning, and 3D point cloud analysis. Notable projects include the ANU bushfire smoke dataset and contributions to generalized semantic segmentation and anomaly recognition. His work emphasizes data augmentation strategies and weakly-supervised learning methods. Key technical areas include synthetic dynamic instance copy-paste for video segmentation, curved geometric networks for anomaly detection, and cross-modal fusion in building facade analysis. He collaborates on computing-for-social-good initiatives, such as environmental monitoring via hyperspectral imaging. Dr. Li's publications span 2016–2024, with a focus on advancing computer vision through innovative architectures and methodologies. His recent work explores open-set recognition, few-shot learning with reinforced attention, and geometric prior-based segmentation techniques.

Vanessa Bowdenمشاهده پروفایل

مدرس ارشد

• Human factors
• Applied psychology
• Visual perception
• +۵ مورد دیگر

Vanessa Bowden is a Senior Lecturer in the School of Psychological Science at The University of Western Australia. She serves as Graduate Research Coordinator for the School, Deputy Director of the Master of Industrial and Organisational Psychology program, and Co-Director of the Human Factors and Applied Cognition Laboratory. Her academic credentials include a PhD from The University of Western Australia and a Graduate Diploma in Human Factors and Safety Management Systems from the University of South Australia. Dr. Bowden's research expertise spans multiple domains within human factors and cognitive psychology. Her primary research interests include: Human interaction with technological systems Automation design and human-automation interaction Driver distraction and transportation safety Cognitive processes in complex work settings Situation awareness and workload management Prospective memory in applied contexts Her recent research has focused on understanding how humans interact with automated systems across various domains, from driving to air traffic control. Dr. Bowden has developed computational models of human decision-making with automated advice and has investigated the impact of automation transparency on operator performance. Her work has important implications for designing safer technological systems that optimize human performance. Dr. Bowden has received significant research funding, including an Australian Research Council Discovery grant (2024) for $924,198 for "A Unified Computational Model of How Humans Use Automated Advice" and multiple Department of Defence grants. Her research has been published extensively in top-tier journals in human factors and cognitive psychology. She has supervised numerous research students and currently accepts PhD and other Higher Degree by Research students. Dr. Bowden teaches several courses including Psychology of Training (PSYC5573), Industrial and Organisational Psychology (PSYC3309), and Perception and Sensory Neuropsychology (PSYC3318).

• Hyperspectral Imaging
• Computer Vision
• Pattern Recognition
• +۳ مورد دیگر

Jun Zhou is Professor and Deputy Head of School (Research) at Griffith University's School of Information and Communication Technology. His research specializes in hyperspectral imaging, computer vision, and pattern recognition with applications in agriculture, environmental monitoring, and remote sensing. Zhou leads significant projects including the ARC Research Hub for Driving Farming Productivity and Disease Prevention. His work develops innovative computer vision systems for agricultural automation, environmental conservation, and industrial quality control. He has received the ARC Discovery Early Career Researcher Award and secured extensive research funding from ARC, CSIRO, and industry partners. Zhou's publications demonstrate consistent contributions to hyperspectral image analysis, object tracking, and deep learning applications. As Deputy Director of the ARC Industrial Transformation Research Hub, he coordinates multi-institutional research teams developing AI-powered solutions for farming productivity and disease prevention.

Ibrahim RADWANمشاهده پروفایل

دانشیار

• Machine Learning
• AI
• Computer Vision
• +۹ مورد دیگر

Ibrahim RADWAN is an Associate Professor in Machine Learning/AI and Robotics at the University of Canberra. His research focuses on advancing AI techniques in areas such as human pose estimation, affective computing, and healthcare technology. He leads projects addressing challenges in robotics, autonomous systems, and human behavior analysis. RADWAN’s work bridges theory and application, contributing to fields like sports science, medical diagnostics, and security through innovative machine learning approaches. Research Projects: Assistive Technologies for Young People Safety on Two-Wheelers AI-Based Methods for Driver Sentiment and Mood Prediction Robotics Applications in Organic Waste Management Research Interests: RADWAN’s expertise spans human pose reconstruction , nonverbal behavior analysis , and EEG-based healthcare diagnostics . He pioneers methods for real-world applications such as: 6G Extended Reality systems using wearable sensors Multimodal deception detection via motion analysis Affective computing for mood and emotion inference Publications: His recent work emphasizes trends in spatiotemporal data analysis, few-shot learning, and synthetic data applications in healthcare and robotics. Key contributions include novel architectures like CrossFormer for 3D pose estimation and Resanet for dense prediction tasks. Advising & Grants: RADWAN supervises PhD students and has secured grants for projects integrating AI with robotics and medical technology. His team collaborates on interdisciplinary challenges, including railway safety and surgical instrument tracking. Labs/Teams: Part of the AI and Robotics research group at the University of Canberra, contributing to cutting-edge solutions in autonomous systems and human-centered AI.

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.

• Computer Vision
• Natural Language Processing
• Machine Learning
• +۱ مورد دیگر

Dr. Zhenghao Chen is an Assistant Professor at the University of Newcastle. He holds a B.Eng. H1 and Ph.D. from the University of Sydney (2017 and 2022). His research focuses on Computer Vision, NLP, and Machine Learning, with expertise in Generative AI. He has published in top conferences like CVPR and journals such as IEEE T-IP. Awards include the Google Australia Prize and ACM SIGMM Outstanding Thesis Award. He previously worked at TikTok and Disney Research, and serves on program committees for major conferences. Research interests emphasize generative models and industrial applications. His publications span topics like image compression, facial recognition, and medical imaging. Awards highlight academic and industrial recognition. Teaching includes courses on visual signal understanding and video intelligence. Current roles include HDR recruitment and organizing international workshops.

Xiaohan Yuمشاهده پروفایل

مدرس

• Ultra-Fine-Grained Visual Categorization
• Smart Farming
• Automated Crop Cultivar Identification
• +۳ مورد دیگر

Dr. Xiaohan Yu is a Lecturer in Artificial Intelligence at Macquarie University's School of Computing, joining in December 2023. Previously, he completed his doctoral studies at Griffith University and served as a Research Fellow at the ARC Research Hub for Driving Farming Productivity. His research focuses on Ultra-Fine-Grained Visual Categorization (Ultra-FGVC), Smart Farming, and Automated Crop Cultivar Identification, with over 70 publications in top-tier venues like ICCV, CVPR, and IEEE Transactions. He holds editorial roles at Pattern Recognition and SN Computer Science , and received the APRS Early Career Award (2022) and ACM MM 2024 Outstanding Area Chair distinction. Education: Completed doctoral studies in Artificial Intelligence at Griffith University, Australia. Research Interests: Ultra-Fine-Grained Visual Categorization (Ultra-FGVC) Smart Farming and Agricultural Robotics Computer Vision Applications in Healthcare (e.g., trachoma detection) Deep Learning, Continual Learning, and Domain Adaptation Key Contributions: Pioneered Ultra-FGVC research, developed frameworks like Mix-ViT and CLE-ViT, and contributed to benchmarking multi-object tracking in farming. His work bridges pattern recognition with real-world applications in agriculture and healthcare. Scientific Awards: Australian Pattern Recognition Society (APRS) Early Career Researcher Award 2022 ACM Multimedia 2024 Outstanding Area Chair Award Advising & Grants: Actively involved in editorial roles (Area Chair for ACM MM, IJCNN) and grant-funded research through ARC hubs. His work is supported by collaborations in agriculture and AI-driven solutions for crop cultivar identification. Labs & Affiliations: Member of Macquarie's Smart Green Cities Research Centre and Frontier AI Research Centre , advancing interdisciplinary AI applications.

Max Schleserمشاهده پروفایل

دانشیار

• Immersive Media
• Cinematic VR
• Interactive Filmmaking
• +۱۲ مورد دیگر

Max Schleser is Associate Professor in Film and Television at Swinburne University of Technology, where he also conducts research with the Centre for Transformative Media Technologies (CTMT). He is an Adobe Creative Educator Innovator, Founder of the Mobile Innovation Network & Association (MINA), and Screening Director of the International Mobile Innovation Screening & Festival. His work spans academic, artistic, and community domains, with a focus on innovative screen practices. Max holds a PhD in Creative Arts from the University of Westminster, an MA in Art and Media Practice with Distinction, and a BA Hons. His research centers on immersive media, documentary film, and creative arts 4.0, particularly cinematic VR and interactive filmmaking. He investigates screen production, emerging media, and smartphone filmmaking as tools for community engagement, creative transformation, and transmedia storytelling. His recent publications reveal a strong trend toward integrating Generative AI into documentary and city film genres, exploring the 'AI eye' and computational non-fiction. He also investigates novel immersive production methods in VR, HyFlex pedagogies in film education, and collaborative mobile storytelling through workshops. His work consistently bridges creative practice with theoretical inquiry. Best Cinematography, International Cell Phone Cinema - Cannes (2025) Best Mobile Film, Nitiin International Film Festival (2024) Dean's Award for Teaching and Research, Swinburne University (2023) International Visiting Research Scholar, Auckland University of Technology (2024) Early Career Award - Research, Massey University (2012) Max leads multiple research grants focused on health storytelling, intergenerational connection, AI in screen industries, and sustainability education. He supervises creative projects and has conducted digital storytelling workshops for cultural institutes and government bodies worldwide. As founder of MINA and director of major festivals, he leads international networks in mobile media innovation. He also serves on the editorial board of the Media Practice and Education journal and has co-edited several key volumes on mobile media and storytelling.

David Scott Taubmanمشاهده پروفایل

استاد

• Multimedia Communication
• Video compression
• Image processing
• +۳ مورد دیگر

Professor David Scott Taubman is a faculty member and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at UNSW Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. He earned his academic credentials from the University of Sydney and University of California at Berkeley: B.Sc. in Mathematics and Computer Science, University of Sydney, 1986 B.E. (Medal) in Electrical Engineering, University of Sydney, 1988 M.Sc. in Electrical Engineering, University of California at Berkeley, 1992 Ph.D. in Electrical Engineering, University of California at Berkeley, 1994 Professor Taubman's research interests span multiple domains within electrical engineering and telecommunications, particularly focusing on: Image Compression (EBCOT algorithm, JPEG2000 technologies) Video Compression (scalable video compression, motion compensated temporal lifting) Image and Video Processing (motion and depth estimation, demosaicing of digital color images, medical image analysis) Multimedia Communication (JPIP standard for interactive imaging, scalable communication systems) He has received numerous scientific awards and honors, including best paper awards from IEEE Signal Processing Society, IEEE Circuits and Systems Society, and IEEE Int. Conf. Image Processing. He has also received teaching awards from UNSW and was recognized with the NSi Inventor of the Year Award. Professor Taubman has contributed significantly to industry standards: Author of the EBCOT coding algorithm adopted in the JPEG2000 standard in November 1998 Author of Verification Model and associated documentation for JPEG2000 Central contributor to IS15444-1, IS15444-4, IS15444-9, IS15444-15 and IS15444-17 Developer of the commercially successful Kakadu Software tools for JPEG2000 He has held various leadership positions at UNSW including Head of the Telecommunications Research Group, Head of the Signal Processing Research Group, and Director of Research at School of EE&T.