جست‌وجوی استاد و پژوهشگر

Taotao Caiمشاهده پروفایل

مدرس

• Graph Data Processing
• Social Network Analytics
• Data Mining
• +۶ مورد دیگر

Dr. Taotao Cai serves as an Honorary Lecturer and Postdoctoral Research Fellow in the School of Computing at Macquarie University, bringing expertise in graph analytics and machine learning. Previously, they held an Associate Research Fellow position at Deakin University's School of Information Technology. Their academic foundation includes a Ph.D. from Deakin University and a Master's in Computer Science from Shenzhen University, China. Education: Ph.D. in Computer Science, Deakin University, Australia Master of Computer Science, Shenzhen University, P.R. China Research spans graph data processing, social network analytics, and data mining with significant contributions to causal graph neural networks, influence maximization, and privacy-preserving machine learning. Recent work demonstrates strong interdisciplinary applications in remote sensing, healthcare decision systems, and battery management, while maintaining core focus on dynamic network analysis. The research portfolio reveals increasing sophistication in causal reasoning within graph structures and practical implementations for real-world systems. Publication trends from 2023-2025 show concentrated activity in graph neural networks (40%), privacy/security (25%), and remote sensing applications (20%), with emerging work in causal classification and medical AI. This evolution reflects both theoretical advancement and practical problem-solving across diverse domains. No formal scientific awards are documented in available sources. While no formal student advising is recorded, collaborative patterns indicate active mentorship within research teams. Grant activity remains unspecified, but extensive cross-institutional collaborations (particularly with Deakin University and international partners) suggest participation in multiple funded projects. Current trajectories point toward causal graph neural networks for explainable AI and privacy-enhanced federated learning systems as primary research frontiers. Active participation in international research networks is evident through co-authorship spanning Australia, China, and global institutions, with particular strength in graph algorithm development and social network analysis communities.

• 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.

Anuradhi Welhengeمشاهده پروفایل

مدرس

• Biomedical Engineering
• Telecommunications
• Fog Computing
• +۳ مورد دیگر

Dr. Anuradhi Welhenge is a Lecturer at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences within the Faculty of Science and Engineering. She holds a BSc in Electronics Engineering from Asian Institute of Technology (Thailand), Master of Biomedical Engineering from University of New South Wales (Australia), and Doctor of Engineering in Telecommunications from Asian Institute of Technology. With international work experience across Australia, Japan, Thailand, and Sri Lanka, her research spans biomedical engineering, IoT systems, and signal processing. Research interests focus on developing novel computational approaches for healthcare applications, including fog computing-based medical diagnosis systems, wireless body sensor networks for physiological monitoring, and deep learning solutions for telehealth. Her work integrates signal processing techniques with IoT architectures to create sustainable medical technologies. Articles demonstrate strong trends in AI applications for healthcare, particularly deep learning architectures applied to medical imaging diagnostics, physiological signal processing, and IoT-based health monitoring systems. Recent publications show growing emphasis on security frameworks for medical IoT and multi-modal healthcare applications. Scientific Awards: Asian Institute of Technology Fellowships (2011, 2012) Best Paper Awards at UMEDIA 2015 and IEEE ICKII 2019 University of Kelaniya Senate Research Honours (2021, 2022) Outstanding Researcher Award (2022) Active in academic service with memberships in IEEE, IET, and Institution of Engineers Sri Lanka. Coordinates industry-academia collaborations through telehealth research and student mentorship. Leads research on sensor network implementations and computational healthcare systems through university laboratory facilities, collaborating with international teams on medical IoT innovations.

• Multimedia Indexing and Search
• Social Data Analysis
• Knowledge Discovery
• +۳ مورد دیگر

Professor Helen Huang is a faculty member in the School of Electrical Engineering and Computer Science at The University of Queensland, where she serves as the Data Science Discipline Leader. She holds a BSc from Tsinghua University's Computer Science Department and a PhD from The University of Queensland's School of ITEE (2001 and 2007, respectively). Her research focuses on multimedia indexing, social data analysis, and knowledge discovery, with over 200+ publications in top-tier venues. Dr. Huang is an Associate Editor for The VLDB Journal, ACM Transactions on Information Systems, and Pattern Recognition Journal, and a member of the VLDB Endowment Board of Trustees. Her awards include the 2016 Chris Wallace Award for breakthroughs in Computer Science, the 2014 Women in Technology Research Award, and the 2018 Excellence in Higher Degree Research Supervision Award. She leads projects in data science, AI-driven systems, and interdisciplinary applications such as Auslan learning and material science modeling. Her work spans domains like recommender systems, domain adaptation, and graph learning, with contributions to both theoretical advancements and real-world applications. Dr. Huang’s publications reflect expertise in machine learning, computer vision, and data science, addressing challenges in multimedia, social networks, and interdisciplinary collaborations. She has advised numerous students and led grants in AI, domain adaptation, and educational technologies. Her leadership roles and editorial contributions highlight her impact on the academic community.

• Computer Vision
• Machine Learning
• Object Detection
• +۷ مورد دیگر

Du Huynh is an Associate Professor at the School of Physics, Mathematics and Computing, Department of Computer Science and Software Engineering, The University of Western Australia (UWA). She holds a 0.8 FTE appointment and has served as Graduate Research Coordinator since 2013. Her research focuses on Computer Vision, Machine Learning, Object Detection, and Video Analytics with applications in mineral processing, intelligent transportation systems, and medical imaging. She has secured over $3M in ARC grants and is a Chief Investigator in the Australian Centre for Quantum Growth ($18M funding). Education: BSc (Hons) and PhD in Computer Science from UWA. Prior to UWA, she lectured at Murdoch University for five years. Research Highlights Developed algorithms for automated mineral ore analysis, pedestrian trajectory prediction, and surgical instrument detection. Received three Best Paper Awards (ICPR2014, Kenneth Clarke Journal 2019, AusDM2022) and multiple award nominations. Editorial roles include PLOS One, Journal on Artificial Intelligence, and guest editorships for special issues in CVIU and Electronics. Grants & Projects Lead investigator in quantum computing algorithms for real-time optimization ($3M ARC funding). Collaborations with industry (e.g., Main Roads WA) on traffic video analytics and drone-based prediction systems. Academic Contributions Developed courses in Computer Vision, Machine Learning, and Java Programming. Supervised numerous HDR students and served on technical committees for WACV, ICCV, and DICTA conferences.

Yuankai Qiمشاهده پروفایل

مدرس

• Vision-and-Language for Robot Action
• Video Captioning
• Medical Image Processing
• +۸ مورد دیگر

Dr. Yuankai Qi is a Lecturer in Artificial Intelligence at Macquarie University's School of Computing and an Adjunct Lecturer at the Australian Institute for Machine Learning (AIML), University of Adelaide. His research focuses on Vision-and-Language for Robot Action, Video Captioning, Medical Image Processing, and Movie Dubbing. He holds a PhD from Harbin Institute of Technology, specializing in visual object tracking, and has won awards such as the DAVIS 2017 Challenge and CAAI Outstanding Doctoral Dissertation. He has published over 60 papers in top-tier venues like CVPR and NeurIPS, and serves as an Area Chair for NeurIPS, ICCV, and ICLR. His current projects include AI-enhanced CT image analysis and generative AI for banking solutions. Dr. Qi collaborates with the Data Horizons, Smart Green Cities, and Frontier AI Research Centres. He teaches COMP 3420 (AI for Text and Vision) and COMP 2250 (Data Communication). His work spans robotics navigation, medical imaging, and video anomaly detection, recognized globally as a Top 2% Scientist in 2024. Education: PhD (Harbin Institute of Technology, China) Key Awards: ACM Multimedia 2024 Best Paper Award, ICPR 2024 Best Student Paper Award Grants: Projects on AI in healthcare and banking (2024–2028) Labs/Teams: Data Horizons Research Centre, Smart Green Cities Research Centre

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.

• Machine Learning
• Computer Vision
• Hyperspectral Imaging
• +۵ مورد دیگر

Associate Professor Ivan Lee is affiliated with the University of South Australia's STEM division at Mawson Lakes Campus. He actively supervises research degrees and contributes to interdisciplinary research intersecting computer science, food safety, and smart materials. Research Interests: Specializes in machine learning applications for hyperspectral imaging in food contamination detection Develops novel graph learning algorithms for real-time driver fatigue monitoring Investigates eco-friendly synthesis techniques for graphene-based nanomaterials Advances intelligent sensing systems for structural health monitoring Publications Trends: Recent work focuses on spatio-temporal graph modeling, multi-objective optimization algorithms, and applying deep learning to food safety challenges like aflatoxin detection. His research spans computer vision, IoT systems, and sustainable materials. Supervision: Currently serves as a research degree supervisor at University of South Australia. No scientific awards mentioned in available records.

• Learning Classifier Systems
• Evolutionary Computation
• Machine Learning
• +۷ مورد دیگر

Will N. Browne is a Professor specializing in Artificial Intelligence with extensive contributions to Learning Classifier Systems, Evolutionary Computation, and Machine Learning. His research spans multiple disciplines including Robotics, Computer Vision, and Explainable AI, with publications in top-tier conferences and journals across these fields. Dr. Browne's research interests primarily center around Learning Classifier Systems, which are rule-based machine learning systems combining reinforcement learning, supervised learning, and evolutionary algorithms. His work has significantly advanced the field by developing methods to scale these systems for complex problems, addressing perceptual aliasing through lateralized learning approaches, and extending them to handle continuous features. He has pioneered the integration of attention mechanisms with rule-based learning, creating more robust systems for applications like emotion recognition from partially covered faces. His recent research strongly emphasizes interpretable and explainable AI, developing evolutionary methods that maintain model transparency while achieving high performance. His scientific contributions show a clear progression from theoretical foundations to practical applications. Early work focused on core Learning Classifier System algorithms and their application to Boolean problems, while recent publications demonstrate successful applications in multi-robot systems, emotion recognition, and human-robot interaction. His publications in IEEE Robotics and Automation Letters, Evolutionary Computation, and Neurocomputing reflect the interdisciplinary nature of his work. Dr. Browne has mentored numerous researchers, with frequent collaborations indicating his role in guiding students and postdocs. His work often bridges theoretical advances with practical implementations, as evidenced by applications ranging from robot navigation and collision avoidance to smart home technology adoption frameworks. He has been instrumental in developing Learning Classifier Systems for real-world problems, particularly focusing on making these systems applicable to continuous domains and enhancing their interpretability. His laboratory appears to focus on creating AI systems that can be understood by humans, which addresses a critical need in the deployment of AI technologies across various domains.

• Urban Planning
• Sustainable Urban Development
• Transport Planning
• +۴ مورد دیگر

Shan He is an Adjunct Lecturer at the School of Design, University of Western Australia, with a focus on urban planning and sustainable development. He holds a PhD from UWA, a Postgraduate Diploma in Architecture from Cambridge University, and a Bachelor of Architecture from Zhejiang University. His research explores urban infill strategies in Perth through water-sensitive urbanism, and high-density urban growth around transport infrastructure in China, particularly high-speed rail nodes. He is funded by the CRC for Water Sensitive Cities. Research interests include Chinese Urbanism, transport planning, and sustainable architectural design. His work aligns with UN Sustainable Development Goals related to sustainable cities and communities. Recent publications focus on galaxy surveys and cosmological studies through large-scale astronomical surveys like GAMA and DEVILS. No scientific awards are explicitly mentioned. He has contributed to collaborative projects such as the WAVES Input Catalogue and ProFound source extraction software. His academic activities include interdisciplinary research bridging urban studies and environmental design.

• Computer Vision
• Medical Image Computing
• Machine Learning
• +۴ مورد دیگر

Associate Professor Weidong Cai is affiliated with the University of Sydney's School of Computer Science, serving as Director of the Multimedia Lab and Associate Director of the Biomedical & Multimedia Information Technology (BMIT) Research Group. He has held visiting roles at Harvard Medical School and holds a PhD in Computer Science from the University of Sydney. His research focuses on medical image computing, computer vision, machine learning, and computational neuroscience, with over 300 peer-reviewed publications. Dr. Cai leads projects on 3D point cloud processing, label-efficient learning, cross-domain medical image analysis, and neuroimaging computing. Dr. Cai's educational background includes a PhD from the University of Sydney (2001) and postdoctoral experience at Harvard Medical School (2014). He supervises students in courses like COMP3419 Graphics and Multimedia and COMP5424 Information Technology in Biomedicine. His current research explores cutting-edge topics like 3D neuron reconstruction, multimodal neuroimaging, and AI-driven medical diagnostics. His research interests span medical imaging, bioinformatics, and computer vision, with a focus on developing algorithms for big data analytics, deep learning, and biomedical applications. Projects include automated neuron tracing, cross-domain adaptation for medical imaging, and geometric deep learning for neuroimaging analysis. Dr. Cai's students have received numerous awards, including the MICCAI IUGC Grand Challenge (2024) and IEEE ISBI Travel Grants. He serves on editorial boards for journals like IEEE Transactions on Image Processing and Brain Informatics, and co-edits Springer's Brain Informatics & Health book series. His lab, the Multimedia Lab, collaborates on projects like 3D point cloud applications, label-efficient feature learning, and cross-domain medical image analysis. Research teams include the BMIT group, focusing on biomedical and multimedia technologies.

Dengsheng Zhangمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Big Data
• Image Classification
• +۵ مورد دیگر

Dr. Dengsheng Zhang is a Senior Lecturer at Federation University Australia (formerly Monash University) and a Guest Professor at Xi'an University of Posts & Telecommunications, China. He holds a PhD in Computing (2002) and a GCHE (2006). His expertise spans over 25 years in artificial intelligence, big data, image processing, and music classification. He has published over 100 refereed papers (10,000+ citations) and authored the award-winning book Fundamentals of Image Data Mining (Springer, 2019). His research focuses on image classification, object detection, deep learning, and feature extraction. Dr. Zhang leads the Computational Science and Mathematics Group, contributing to projects like SIRBOT (Semantic Image Retrieval) and VFR (Visiting Friends and Relatives Travel Research). He serves as an Associate Editor for World Scientific's Transactions on Signal Processing. His teaching spans calculus, web design, computer networks, and project management. He has supervised 8 completed PhD students and actively engages in grants, including two ARC Discovery projects. His lab explores innovations in image retrieval, music classification, and machine learning applications.

• Astronomical Sciences
• Cosmology
• Astrophysics
• +۵ مورد دیگر

Prof. Jeff Cooke is a Professor at Swinburne University of Technology's School of Science, Computing and Emerging Technologies. He leads research in high-redshift galaxies, supernovae, fast transients, and gravitational waves. As a Chief Investigator at OzGrav and Associate Investigator at ASTRO-3D, he explores cosmic reionization, galaxy evolution, and transient phenomena. His work includes detecting Lyman continuum galaxies and pioneering techniques for super-luminous supernova studies. He chairs the Deeper, Wider, Faster (DWF) program to detect fast transients like FRBs and gravitational wave counterparts using global telescope networks. Professional activities include committee roles in SATCON, Keck Observatory, and international conferences. Awards include ARC Future Fellowship (2013) and multiple Vice-Chancellor's Research Excellence Awards. He supervises PhD students on topics like host galaxy studies and AI-driven transient detection. Key grants include ARC funding for DWF and Keck Wide-Field Imager development. His lab collaborations span over 30 institutions globally. Research focuses on multiwavelength surveys, real-time data analysis, and cosmic baryon distribution studies via FLIMFLAM. His expertise bridges observational astronomy, instrumentation, and computational methods to address fundamental questions in astrophysics and cosmology.

Nimsiri Abhayasingheمشاهده پروفایل

مدرس ارشد

• Inertial Measurement Unit (IMU) Applications
• Human Gait Modeling and Analysis
• Sensor Data Processing
• +۳ مورد دیگر

Nimsiri Abhayasinghe is a Senior Lecturer at Curtin University's School of Electrical Engineering, Computer and Mathematical Sciences. Their work focuses on inertial measurement units (IMUs), human gait modeling, and sensor-based systems for navigation, robotics, and biomedical applications. They are affiliated with the Faculty of Science and Engineering and contribute to the Office of the Provost's initiatives. Research interests include IMU calibration techniques, wearable sensor algorithms, and IoT-enabled assistive technologies. Notable contributions involve developing cost-effective IMU systems for navigation research, validating thigh angle estimation accuracy using optical motion capture, and creating gesture recognition systems for sign language. Publications span topics like human gait phase recognition, RPM detection using ignition pulses, and solar microgrid designs. Their work bridges mechanical engineering, computer science, and biomedical applications, emphasizing practical sensor integration and algorithmic innovation. No scientific awards are listed, but their research demonstrates expertise in sensor data analysis and interdisciplinary engineering solutions. Advising and grant details are not explicitly provided in the profile. They are based at Curtin Perth's New Technologies Building, Room 329.

Weijia Zhangمشاهده پروفایل

مدرس

• Causal Inference
• Machine Learning
• Weakly Supervised Learning
• +۳ مورد دیگر

Dr. Weijia Zhang is a Lecturer in Data Science and Applied Statistics at the University of Newcastle (Australia), focusing on causal inference and machine learning from ambiguous data. He holds a PhD from the University of South Australia (2018) and has held roles as Associate Professor at Southeast University (China) and Research Fellow at the University of South Australia. His research bridges causal inference and machine learning, addressing challenges in weakly supervised learning (e.g., multi-instance partial-label learning) and survival analysis under dependent censoring. Affiliations: University of Newcastle (current), Southeast University (2021–2023), University of South Australia (2018–2020) Teaching: Courses on predictive analytics, unsupervised methods, statistical decision-making, and time series forecasting Research interests include causal representation learning, weakly supervised learning from noisy/ambiguous labels, and applications in healthcare, telecommunications, and automotive industries. Key projects include developing causal methods for battery lifespan prediction with Chang'an Automobile and models for dependent censoring in survival analysis. Publications (over 30+): Focus on causal inference, weak supervision, and survival analysis. Notable contributions include CausalPLL+, MiplGp, and TEDVAE algorithms. Editor for Data Science and Engineering and reviewer for top conferences (NeurIPS, ICML, AAAI). Grants: $125k from Changan Automobile, $63.5k from National Natural Science Foundation of China, and institutional funding ($132.5k total) Awards: Top Reviewer Awards at AAAI (2021) and UAI (2022) Labs/Teams: Leads research on causal representation learning and survival analysis within the School of Information and Physical Sciences, collaborating internationally with institutions in China, Switzerland, and the US.