جست‌وجوی استاد و پژوهشگر

• Robotics
• Computer Vision
• Simultaneous Localization and Mapping (SLAM)
• +۶ مورد دیگر

Niko Sünderhauf is a Professor in the School of Electrical Engineering and Robotics at Queensland University of Technology (QUT), specializing in robotics, computer vision, and autonomous systems. His research spans visual place recognition, simultaneous localization and mapping (SLAM), neural radiance fields, and out-of-distribution detection for robotics applications. His research interests focus on enabling robots to understand and navigate complex environments through advanced computer vision techniques. He has made significant contributions to visual place recognition, particularly for handling viewpoint and environmental changes. His work on switchable constraints has improved robustness in SLAM systems, while his more recent research explores neural radiance fields, Gaussian splatting, and integrating large language models with robotic perception. Prof. Sünderhauf's publication record demonstrates consistent research productivity with 115 publications spanning from 2005 to 2025. His recent work shows a clear progression from traditional SLAM techniques toward more advanced neural scene representations and integration with large language models. His research has been published in top venues including IEEE ICRA, CVPR, IROS, and the International Journal of Robotics Research. Among his scientific contributions are novel approaches to visual place recognition that work across seasons and viewpoints, robust SLAM techniques that handle outliers and non-linearities, and recent innovations in neural scene representations for robotics. His work on switchable constraints has been particularly influential in the robotics community. Prof. Sünderhauf actively supervises PhD students including Dimity Miller, Krishan Rana, and Jad Abou-Chakra, who frequently appear as first authors on publications. His collaborative network includes researchers from QUT and international institutions, demonstrating strong research leadership in the robotics community.

• Machine Vision
• Artificial Intelligence
• Medical Imaging
• +۵ مورد دیگر

Assoc. Prof. Andrew Busch is the Head of the School of Engineering and Built Environment at Griffith University since 2024. With over two decades of academic experience, he specializes in machine vision for agricultural applications , medical imaging , human movement analysis , and marine science engineering . His research has secured over $14M in funding (including $6M from ARC and $7M from industry partners) and spans collaborations across disciplines. Education: PhD, Queensland University of Technology (2004) BEng (Electrical and Electronic), Queensland University of Technology (1998) Bachelor of Information Technology, Queensland University of Technology (1998) Andrew's research bridges computer vision and artificial intelligence with applications in smart farming , medical device development , and marine ecosystem monitoring . Recent works focus on sugarcane quality automation, retinal vessel delineation, and AI-driven energy systems for buildings. His 15 most recent publications (2025-2002) highlight trends in: Agricultural engineering (sugarcane billet analysis, strawberry grading) Medical imaging (retinal vasculature, speech enhancement) Energy systems (smart buildings, digital twins) Education innovation (industry placements, curriculum design) Scientific Awards: 2021: Excellence in Teaching - Group Educational Leadership Award (Highly Commended) Andrew has supervised 13 doctoral students across projects in aquaculture automation, sugarcane AI systems, and medical diagnostics. He chairs conferences like AAEE 2023 and leads programs such as the Bachelor of Advanced Engineering (Honours) . Labs & Teams: He collaborates with the Institute for Integrated and Intelligent Systems (2018-2024) and directs projects funded by Sugar Research Australia , ARC , and DFAT .

• Marine Biology
• Coral Reef Ecology
• Biodiversity Conservation
• +۵ مورد دیگر

Ben Radford is an Adjunct Associate Professor at the UWA Oceans Institute , The University of Western Australia. His work focuses on marine ecosystems, coral reefs, and coastal environmental challenges. He contributes to UN Sustainable Development Goals related to life below water and climate action. Research Interests: Marine benthic habitats and their conservation Climate change impacts on coral reefs and fisheries Hydrodynamic influences on tropical fish assemblages Remote sensing and AI-driven ecological monitoring Marine reserve management and habitat mapping Notable Projects: WESTPORT - Benthic Habitat Mapping (WA Dept. of Transport, 2021-2024) Coral reef AI benchmarking for image surveys (2025) Geographe Marine Park long-term ecological monitoring (2019-2024) Grants & Funding: Director of National Parks (A$ millions) Fisheries Research & Development Corporation Department of Agriculture, Fisheries & Forestry Labs/Teams: Collaborates with interdisciplinary teams across UWA Oceans Institute, Australian Institute of Marine Science, and international partners in coral reef research.

• Computer vision
• Machine learning

Dr. Zhongyan Zhang serves as an Associate Research Fellow at the School of Computing and Information Technology within the Faculty of Engineering and Information Sciences at the University of Wollongong, Australia, a position held since 2022. His academic foundation includes: PhD from University of Wollongong Master's degree from Beihang University, Beijing Bachelor's degree from Beihang University, Beijing Specializing in Computer Vision and Machine Learning , Dr. Zhang pioneers annotation-free methodologies for instance image retrieval. His research eliminates dependency on manual labels by exploiting dataset-internal supervisory signals, with innovations in unsupervised object discovery and spatial-context-aware feature representation that significantly enhance retrieval accuracy and user experience through region-based matching. Publication analysis (2015-2023) reveals an evolutionary trajectory from cascade classifiers for pedestrian detection to self-boosting unsupervised frameworks. His recent work demonstrates consistent focus on efficient, dataset-driven solutions that improve retrieval performance while maintaining algorithmic simplicity and low memory costs. No scientific awards are documented in source materials. The provided information contains no details regarding student advising, research grants, or laboratory affiliations.

• Geoinformatics
• Intelligence Informatics
• Geospatial Data Mining
• +۷ مورد دیگر

Ickjai Lee is an Associate Professor at the School of IT, James Cook University, specializing in geoinformatics and intelligence informatics. He leads the Information Technology department and has held roles including Senior Lecturer and Lecturer since 2003. He obtained his PhD in 2002 from the University of Newcastle, Australia. Research Interests: Geospatial data mining, trajectory analysis, Voronoi tessellations, mobile AR, and IoT applications. Projects: Includes VR healthcare trials, AI-driven vehicle damage assessment, and environmental monitoring systems. His work focuses on applying machine learning and geospatial techniques to solve real-world problems in health, environmental, and urban domains. Notable achievements include best paper awards at international conferences and faculty citations for teaching excellence. He collaborates on interdisciplinary projects, such as using AR for marine growth monitoring and developing tools for crime pattern analysis. His research also involves large-scale ecoacoustic data analysis and VR-based education platforms. Lee has supervised numerous PhD students exploring topics like differential privacy in biokinetics, spatio-temporal anomaly detection, and VR in cultural heritage preservation.

Ruwan Tennakoonمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Computer Vision
• Medical Imaging
• +۳ مورد دیگر

Dr. Ruwan Tennakoon is a Senior Lecturer in Artificial Intelligence at RMIT University's School of Computing Technologies in Melbourne, Australia. He holds a BSc (Hons) in Electrical and Electronics Engineering from the University of Peradeniya and a PhD in Computer Vision from Swinburne University of Technology (2015). Prior to his current role, he held post-doctoral positions at RMIT School of Engineering and IBM Research. His research focuses on robust computer vision, particularly in medical imaging and industrial automation. Key areas include distribution shift challenges, large vision foundation models, and robust algorithms for low-level vision tasks. His work spans applications like medical image analysis, autonomous systems, and defect detection in manufacturing. Ruwan has supervised numerous research projects, including those on threat detection, medical imaging AI, and industrial automation. He teaches courses in computer vision and machine learning, emphasizing practical applications. His research has been published in top-tier venues such as ACM Transactions on Intelligent Systems and Technology and Medical Image Analysis .

• Astronomy
• Astrophysics
• Antarctic Astronomy
• +۵ مورد دیگر

Professor Michael Ashley is a faculty member at the University of New South Wales (UNSW), affiliated with the School of Physics. His primary research focuses on observational astronomy, particularly leveraging the unique conditions of the Antarctic plateau for astronomical observations. He is deeply involved in developing and deploying robotic telescopes and instrumentation for Antarctic sites like Dome A and Dome C. His research interests encompass: Time-domain astronomy and infrared surveys (e.g., Palomar Gattini-IR) Exoplanet detection and characterization Stellar variability, transients, and binary systems Site testing and atmospheric conditions in Antarctica Telescope automation and wide-field imaging systems Analysis of his recent articles reveals a strong emphasis on Antarctic astronomy infrastructure development (e.g., AST3-II/CHESPA, Cryoscope), large-scale infrared time-domain surveys (Palomar Gattini-IR), and the study of variable objects like R Coronae Borealis stars, Mira variables, Be/X-ray binaries, and transients. His work frequently involves public data releases and catalog construction. Professor Ashley contributes to major collaborative projects including the Chinese Small Telescope ARray (CSTAR), the Antarctic Survey Telescopes (AST3), Kunlun Infrared Sky Survey (KISS), and the PLATO observatory. He has significant expertise in instrument design, data pipelines for automated surveys, and analyzing Antarctic meteorological data for astronomical site characterization.

• Digital Communications
• Signal Processing
• Wireless Communications
• +۳ مورد دیگر

David Huang is a Professor at The University of Western Australia (UWA), affiliated with the School of Engineering and the Electrical, Electronic and Computer Engineering Institute of Agriculture. His roles include research, teaching, and supervision of higher degree students in areas such as digital communications and signal processing. Education: PhD in Electrical & Electronic Engineering, Hong Kong University of Science and Technology (2004) Master of Engineering in Electronic Engineering, Tsinghua University (1999) Bachelor of Engineering in Electronic Engineering, Tsinghua University (1996) Research Interests: David’s work focuses on signal processing for radar and satellite systems, underwater acoustic communications, and meteorological applications . His research leverages innovative techniques such as forward scatter radar, LEO satellite signals, and tomographic rainfall reconstruction. His contributions align with UN Sustainable Development Goals through advancements in environmental monitoring and communication technologies. Grants & Projects: David has secured significant funding, including ARC Discovery Projects (e.g., $385K for reconfigurable antennas, $520K for underwater acoustic systems) and UWA grants. Current projects include passive positioning using satellite signals and real-time NMR data acquisition for rock optimization. Teaching: He has taught courses such as Advanced Communications, Signal Processing, and Wireless Communications across UWA and Tsinghua University. Labs/Teams: His work involves collaborations with the Institute of Agriculture and global networks like WUN, focusing on radar systems, underwater networks, and meteorological signal processing.

Hao Xiongمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Health Informatics
• Medical Imaging
• +۳ مورد دیگر

Hao Xiong is a Research Fellow at the Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University. He specializes in applying machine learning and artificial intelligence to healthcare challenges, including medical imaging, signal processing, and clinical diagnostics. His work bridges computer science and health informatics, focusing on applications like cough classification, medical prognosis modeling, and automated analysis of clinical interactions. Key research areas include neural networks, acoustic signal analysis, and deep learning for healthcare applications. His systematic reviews and models address issues such as wet/dry cough differentiation, esophageal fistula prognosis, and delirium-dementia correlations. He has contributed to advancements in brain extraction algorithms, logo detection frameworks, and multimodal clinical data analysis. Dr. Xiong has been recognized with prestigious awards, including the IBM Rational Prize in Software Engineering (2010) and Best Student Paper Award (2016). His work emphasizes interdisciplinary collaboration, evidenced by projects involving robotics, epidemiology, and wearable technology for health monitoring. Awards: IBM Rational Prize (2010), Best Student Paper (2016), First Place in Algorithms (2012), Mentoring in Visual Computing (2017/2018) Grants/Projects: Visual Computing and Big Data Analytics (2017-2018), multiple collaborations in healthcare AI His research portfolio includes over 40 peer-reviewed publications, showcasing his impact in health informatics, medical imaging, and machine learning applications.

Andrew Skidmoreمشاهده پروفایل

استاد

• Hyperspectral Remote Sensing
• LiDAR Remote Sensing
• Vegetation Monitoring
• +۳ مورد دیگر

Andrew Skidmore is a Professor of Spatial Ecology at Macquarie University and holds an Honorary Professorship at the University of Twente. He specializes in hyperspectral and LiDAR remote sensing technologies for vegetation monitoring, biodiversity conservation, and climate change impact analysis. His research focuses on developing innovative image processing techniques and essential biodiversity variables (EBVs) for sustainable natural resource management. Previously, Skidmore worked for the Forestry Corporation of New South Wales and served as Head of the Department of Natural Resources at the University of New South Wales from 1997. He has directed the Centre for Remote Sensing and GIS and currently leads projects on coastal blue carbon ecosystems, forest community monitoring, and climate adaptation strategies in Australia and Europe. His research interests include canopy structure analysis, ecosystem resilience, and the integration of environmental DNA (eDNA) with satellite remote sensing. Skidmore has contributed to over 450 publications and collaborates internationally on biodiversity observation systems. He has received grants for projects such as 'Monitoring Blue Carbon Ecosystems' and 'Can coastal floodplains survive ferals and rising seas?' Key awards and recognitions are not explicitly listed, but his extensive editorial work includes roles at the International Journal of Applied Earth Observation and Geoinformation . His advisory efforts focus on leveraging remote sensing for ecological conservation and policy alignment with essential biodiversity variables.

• Information Retrieval
• XML Search Engines
• Text Search Engines
• +۸ مورد دیگر

Shlomo Geva is an Adjunct Professor in the School of Computer Science at Queensland University of Technology's Faculty of Science. His research focuses on information retrieval systems, particularly in specialized areas including XML search engines, text search engines, link discovery, and document computing. His academic work spans multiple disciplines within computer science, with particular emphasis on information retrieval technologies and their applications. Professor Geva's research interests include clustering algorithms, cross language information retrieval, focused information retrieval, information retrieval systems, link discovery mechanisms, search engine technologies, text indexing and retrieval methods, and XML indexing and retrieval techniques. His work demonstrates a consistent focus on improving the efficiency and effectiveness of information access systems across various data formats and domains. His recent publications reveal a trend toward applications of information retrieval techniques in diverse fields including remote sensing, bioinformatics, and data stream processing. The research shows an evolution from traditional information retrieval problems toward more specialized applications requiring advanced clustering algorithms and efficient data processing techniques for large-scale datasets. Professor Geva has successfully supervised numerous doctoral students whose research topics include indoor environment mapping by robots, cross-language information retrieval, natural language query interfaces for XML, evolvable hardware, and autonomous robot behavior systems.

Lei Gaoمشاهده پروفایل

پژوهشگر

• Complex Systems Modelling
• Sustainability Assessment
• Adaptive Management
• +۶ مورد دیگر

Dr. Lei Gao is a Principal Research Scientist at CSIRO Land and Water with over 20 years of expertise in complex environmental systems research. A nationally and internationally recognized thought leader, he pioneers methods in sustainability modelling and AI-driven environmental predictions while collaborating with institutions like Oxford University, IIASA, and the Chinese Academy of Sciences to address land and water management challenges. His research centers on complex systems modelling, sustainability assessment, adaptive management under uncertainty, and machine learning applications for environmental systems. He specializes in translating these methodologies into practical solutions for water resources, land systems, and socio-economic analysis, with a focus on robust decision-making under climate extremes and global change. Recent publications reveal a dominant trend integrating artificial intelligence with environmental modelling, particularly in water flow prediction, soil carbon sequestration, and climate impact assessment. His work increasingly addresses systemic sustainability challenges through scenario analysis, trade-off assessments, and SDG-focused frameworks, demonstrating strong interdisciplinary convergence. Dr. Gao's scientific accolades include: Cell Press China Paper of the Year Award (Sustainability Category) CSIRO Chairman’s Medal for Science Excellence Julius Career Award for exceptional early to mid-career scientists MVIPIT Best Paper Award Impactful Publication Award Publons Peer Review Award — Top reviewers for CSIRO Multiple Best Paper Awards and Research Excellence recognitions As Project Leader for the CAS-CSIRO Joint Project on agricultural climate adaptation, CSIRO Strategic Project on AI-based water banking, and Australia-China Science Fund on soil carbon sequestration, he demonstrates exceptional grant leadership in securing competitive funding for high-impact environmental research. Based at CSIRO Land and Water, Dr. Gao operates within a dynamic international research ecosystem, leveraging cross-program collaborations and partnerships with global institutions to develop innovative sustainability solutions for complex environmental challenges.

Jane Kellyمشاهده پروفایل

پژوهشگر

• Livestock Management
• Biosecurity
• Invasive Species Management
• +۱۲ مورد دیگر

Jane Kelly is a Researcher at Charles Sturt University, affiliated with the Faculty of Agricultural, Environmental and Veterinary Sciences and the Gulbali Research Institute. Her work focuses on livestock management, biosecurity, and the application of remote sensing and artificial intelligence in agricultural and environmental systems. Education: PhD in Agriculture, Charles Sturt University (Awarded: 23 Dec 2020) Graduate Diploma in Education, University of Western Sydney (Awarded: 03 Apr 2002) Bachelor of Science in Agriculture (Honours), University of Sydney (Awarded: 23 May 2001) Her research interests include weed-livestock interactions, zoochory, feral livestock management, remote detection of weeds using drones and AI, and sustainable agricultural practices. She actively contributes to the integration of digital technologies in weed and biosecurity management. Recent publications highlight a strong trend in using UAV-based multispectral and hyperspectral imaging combined with artificial intelligence for detecting invasive species such as African Lovegrass, Bitou bush, and mouse-ear hawkweed. Her work bridges ecological research with practical agricultural applications, emphasizing real-time monitoring and precision management. Scientific Awards and Grants: CAWS Travel Award (2022) – Successful A No-Antibiotic Low Emission verification and traceability pathway for lamb production (2024) – Grant Development of drone technology for Tropical Soda Apple control (2022) – Successful Grant A Smart Weed Seed Management Tool (2020) – Unsuccessful Grant Development of digital feed efficiency measurement in sheep (2021) – Unsuccessful Grant Jane Kelly is actively involved in advising and collaborative research, particularly in grant-funded projects focusing on sustainable livestock systems. She co-founded the Weed Remote Sensing Community of Practice, fostering knowledge exchange. She participates in public engagement through field days, media contributions, and academic conferences, enhancing the impact of her research. She is a key member of interdisciplinary research teams utilizing drone technology and AI for environmental monitoring. Her lab collaborations involve experts in remote sensing, machine learning, and agricultural engineering, working toward scalable solutions for invasive species management.

• Artificial Intelligence
• Computer Vision
• Data Mining
• +۷ مورد دیگر

Motiur Rahaman is a research-focused academic at Charles Sturt University, Australia, affiliated with the Data Mining Research Group (DaMRG) in the School of Computing, Mathematics and Engineering. His work bridges artificial intelligence with practical applications in agriculture and healthcare. Charles Sturt University Data Mining Research Group (DaMRG) Department of Computer Science His research interests center on artificial intelligence, computer vision, and data mining , with strong applications in remote sensing for agriculture (e.g., weed detection in crops) and medical imaging analysis (e.g., predicting ICU admission from X-ray features in COVID-19 patients). He also explores speech emotion classification using augmented audio data. His work often involves Gaussian mixture modeling, spatial correlation analysis, and deep learning techniques. The recent publications show a clear trend toward applied AI in solving real-world problems, particularly in sustainable agriculture and public health. His interdisciplinary collaborations reflect a strong emphasis on translating AI models into practical tools for farmers and clinicians. Scientific Awards: World-first computing and artificial intelligence COVID-19 severity scoring study (Grant, May 2020) Dr. Rahaman has been involved in significant grant-funded research, particularly in AI for healthcare. He collaborates extensively with researchers in viticulture, medicine, and agricultural technology. While no formal advisees are listed, his role in doctoral theses and datasets suggests mentoring and team leadership. He is a key contributor to the Smart Wine Project , which developed an open image library for grapevine nutrient disorders, demonstrating his involvement in data-driven agricultural innovation.

Sara Webbمشاهده پروفایل

مدرس

• Observational Astronomy
• Artificial Intelligence
• Space Science
• +۳ مورد دیگر

Dr. Sara Webb is a Lecturer at Swinburne University of Technology's School of Science, Computing and Emerging Technologies , where she serves as: Course Director for Swinburne Astronomy Online Microgravity Program Lead Program Mission Director for SHINE and the Swinburne Space Innovation Challenge Her research integrates observational astronomy, artificial intelligence, and space science , focusing on transient astrophysical phenomena using machine learning. Key research areas include: Gamma-ray burst detection and classification Interstellar object analysis Space debris tracking systems Microgravity experiment development Human-machine teaming for cosmic discovery Recent publications demonstrate expertise in fast transient detection (7 papers 2021-2025) and machine learning applications in astronomy (3 publications). Her work has been cited 48 times across these papers. Scientific recognition includes: Forbes 30 Under 30 Asia (2025) Space Domain Awareness Leadership AI & Anomaly Detection Innovation International Collaboration Network As a science communicator , she supervises 5+ doctoral students, authors The Conversation pieces (20+ 2021-2025), and has written The Little Book of Cosmic Catastrophes . Grants include funding from University of Melbourne and Swinburne internal programs for space research initiatives.