جست‌وجوی استاد و پژوهشگر

Prashan Premaratneمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Pattern Recognition
• Image Processing
• +۴ مورد دیگر

Prashan Premaratne is a Senior Lecturer at the University of Wollongong's School of Electrical, Computer and Telecommunications Engineering, where he has been affiliated since 2003. His academic qualifications include a Doctor of Philosophy in Image/Signal Processing from the National University of Singapore (1999-2001), a Bachelor of Engineering in Optical Communications and Signal Processing from the University of Melbourne (1994-1997), and a Graduate Certificate in Higher Education from the University of Wollongong (2008-2009). His research focuses on: Computer vision systems for gesture recognition and object detection Advanced image/video processing techniques including steganography Artificial intelligence applications in speech processing and sensor networks Energy system optimization through electromagnetic modeling His publications demonstrate strong focus on computer vision (45%), signal processing (30%), and AI applications (25%), with recent work emphasizing real-time systems optimization and deep learning architectures. Award recognition includes: Vice Chancellor's Award for Teaching Excellence (2020) IEEE Senior Member recognition (2008) Singapore Science Award (1999) He actively supervises graduate researchers in computer vision and AI, with 11 completed PhD/MPhil students. Grant funding includes internal university awards for robotics education and face detection technology. He serves as program chair for major AI conferences and reviews for national research councils.

Mehwish Nasimمشاهده پروفایل

مدرس ارشد

• Complex Systems
• Social Network Analysis
• Human-Centric Cyber Security
• +۵ مورد دیگر

Mehwish Nasim is a Senior Lecturer at The University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing's Department of Computer Science and Software Engineering. She previously held roles at the University of Adelaide, Flinders University, and CSIRO. As the Social Media Coordinator for the Australian Mathematical Society (AustMS), she bridges academic communication and public engagement. Her research focuses on complex systems, social network analysis, cyber security, and health analytics. Current projects include AI-driven detection of misinformation, polarization modeling on social media, and predictive models for population-level events. She also explores decision-making frameworks using complex systems methodologies. Recent publications highlight her work on large language models in social science, video moment retrieval, and mental health surveillance. Her contributions span computational methods for social good, environmental prediction, and ethical AI applications. Awards: Shining Star in AI (WiTWA 2023), PMC School Special Commendation (2023), Anne Penfold Street Award (2023) External Roles: Adjunct Lecturer at Flinders University and University of Adelaide; Visiting Scientist at CSIRO-Data61; Associate Investigator at ARC Centre of Excellence for Mathematical and Statistical Frontiers Activities: Organized the 1st International Workshop on Multimodal Content Analysis for Social Good (2023); Public outreach on cybersecurity and data science She actively supervises PhD students in areas like AI ethics and social network dynamics, contributing to UWA's research degree opportunities.

Katrina Nevilleمشاهده پروفایل

مدرس ارشد

• Electrical and Electronic Engineering
• Communications Technologies
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Katrina Neville is a Senior Lecturer in the School of Engineering at RMIT University, Australia. Her research focuses on interdisciplinary areas including electrical engineering, communications, artificial intelligence, image processing, and educational technology. She actively supervises research projects in robotics, stroke rehabilitation, and signal processing. Her work bridges theoretical advancements with practical applications in healthcare technology and manufacturing systems. Research Interests: Electrical/Electronic Engineering, Communications Technologies, AI & Image Processing, and Education Technology. Teaching expertise spans signal processing, speech processing, and communication systems. She has published extensively in journals like IEEE Transactions and conferences such as SigTelCom and ICCE. Supervision Projects: Includes topics like collaborative robotics programming, post-stroke motor function recovery via VR/motor imagery, and wind noise reduction mechanisms. She has advised projects ranging from neural network applications in image recognition (2015) to advanced OFDM systems (2008). Labs/Teams: While specific lab affiliations aren’t listed, her research collaborations involve interdisciplinary teams working on biomedical informatics, robotics, and educational technology innovations.

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

Dr. Maryam Haghighat is a Lecturer at QUT's School of Electrical Engineering & Robotics, specializing in Machine Learning, Computer Vision, and Robotics. She holds a PhD from UNSW's School of Electrical Engineering and Telecommunications, where her thesis earned the 2020 UNSW Award for Outstanding Doctoral Thesis. Her postdoctoral research at the University of Oxford's Big Data Institute (2020–2022) focused on medical image analysis for the PathLAKE project. Currently, she leads AI projects with over $10M in funding, including ARC grants and industry collaborations. Her research spans applications in robotics, healthcare, and remote sensing. Notable contributions include developing machine learning algorithms for histopathology image analysis and robust multi-robot localization systems. Awards include the UNSW Doctoral Thesis Award (2020). She actively supervises PhD students in topics like multimodal fusion and 3D visual understanding. Publications highlight advancements in AI-driven medical imaging, hyperspectral transformers, and illumination-aware video compression. Her work bridges theoretical machine learning with practical applications in healthcare, robotics, and environmental monitoring.

Michael Antolovichمشاهده پروفایل

مدرس ارشد

• Drones
• 3D printing
• AI and robotic systems
• +۲ مورد دیگر

Michael Antolovich is a Senior Lecturer and Course Director in Computer Science at Charles Sturt University (CSU), affiliated with the School of Computing and Mathematics and the Machine Vision and Digital Health (MaViDH) Research Group. He has held roles such as Associate Head of School and Acting Head of the Environment Studies Unit (ESU) at CSU. Doctor of Philosophy, University of New South Wales (1988) Bachelor of Science, University of New South Wales (1983) His research focuses on drones, 3D printing, AI, robotic systems, sensor networks, and data analysis. Recent work explores parametric classification of Bingham distributions, low-rank matrix approximation algorithms, and sports highlight generation using audio-visual features. Key trends in his publications include advancements in image processing , machine learning , and data visualization applied to video analysis, sensor networks, and business communication. Antolovich has served as a registered supervisor at CSU and coordinated the Apple University Consortium Development Fund (AUCDF). He is a Member of the Australian Computer Society (MACS) and a Practicing Computer Professional (PCP).

• Artificial Intelligence
• Medical Imaging
• Computer Vision
• +۹ مورد دیگر

Dr. Sajib Kumar Saha is a Research Scientist at the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia, with honorary and adjunct affiliations at the University of California-Los Angeles (UCLA) and Murdoch University. His work bridges artificial intelligence, medical imaging, and clinical decision support, focusing on automated diagnosis of eye diseases. PhD in Biomedical Imaging, University of New South Wales, Canberra, Australia (2015) Erasmus Mundus Master in Color in Informatics and Media Technology, University Jean Monnet, France & Norwegian University of Science and Technology, Norway (2011) Bachelor of Science (Hons) in Computer Science and Engineering, Khulna University, Bangladesh (2007) Dr. Saha's research lies at the forefront of AI-driven healthcare innovation. His expertise spans artificial intelligence, computer vision, medical imaging, deep learning, and clinical decision support systems , particularly for ophthalmic conditions like diabetic retinopathy and glaucoma. He applies machine learning to analyze retinal images, X-rays, and mobile-based diagnostics to improve early detection and remote monitoring. His recent publications and grants reflect a strong trend toward translational AI in medicine , with projects involving smartphone-based glaucoma assessment, automated teleretinal screening, AI for diabetic foot infections, and cavity detection from dental photos. These works emphasize real-world implementation, home-based care, and integration into primary healthcare settings. Notable scientific awards include: Julius Career Award (CSIRO) Endeavour Visiting Research Fellowship Office of Chief Executive Postdoctoral Fellowship Best Presenter, International Conference on Machine Vision (2012) Erasmus Mundus Scholarship Rising Star Award, Science on the Swan 2018 Dr. Saha has secured multiple competitive grants as Chief Investigator or Principal Investigator, including from WA Future Health Research and Innovation (FHRI), Australia-Vietnam Enhanced Economic Engagement, CSIRO ACORN, and Spinnaker. These grants support the development and deployment of AI tools in clinical environments. He has also advised or mentored researchers through collaborative projects, though formal student names are not listed. He has delivered invited seminars at leading institutions including the University of Paris, University of Milan, and ETH Zürich, and serves on the editorial board of EC Ophthalmology and the programming committee of ICONIP. His professional memberships include IEEE Computer Society and the Association for Research in Vision and Ophthalmology (ARVO).

Kalin Stefanovمشاهده پروفایل

پژوهشگر ارشد

• Affective Computing
• Artificial Intelligence
• Accessible Computing
• +۵ مورد دیگر

Kalin Stefanov is an ARC DECRA Fellow and Research Fellow in the Department of Human Centred Computing at Monash University. He holds a PhD in Computer Science from KTH Royal Institute of Technology and an MSc in Artificial Intelligence from the University of Amsterdam. His research focuses on Affective Computing, exploring systems that recognize and simulate human affects, with applications in social robotics, neurodiverse communication, and multimodal interaction. He has led projects on sign language translation and large-scale deepfake detection datasets. Key collaborations include work at the University of Southern California’s Institute for Creative Technologies and National Institute of Informatics. He has received accolades such as the Discovery Early Career Researcher Award (2023) and Best Paper Awards (2019, 2024). His research also contributes to UN SDG 4 (Quality Education) through accessible technologies for neurodiverse groups and visually impaired learners. Projects include the 'Active Generation of fingerspelling in Australian Sign Language' and 'Research Towards automated Australian Sign Language translation,' funded by the Australian Research Council. Stefanov’s work spans AI ethics, multimodal data platforms (e.g., OpenSense), and systems for social signal processing in human-robot interaction.

• Artificial Intelligence
• Machine Learning
• Probabilistic Inferencing
• +۵ مورد دیگر

Barry Drake serves as an Adjunct Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he is also affiliated with the Faculty of Engineering and Information Technology and the Data Science Institute. Additionally, he holds the position of Co-Chief Scientist at the Digital Health Cooperative Research Centre and participates in government advisory committees. His career bridges academic research and industry application, with significant experience in translating research into practical technology solutions for business and government sectors. Drake earned his PhD in Computer Science from UNSW Australia (1998-2003) and a BSc with First Class Honors in Computer Science from UTS (1991-1996). His educational background is complemented by a Certificate of Proficiency for Radio Fitter/Mechanic Electronic Systems from the Royal Australian Navy. His research focuses on algorithms and software for practical smart systems, with particular expertise in applications of AI and machine learning, probabilistic inferencing and knowledge compilation, learning probabilistic models, and fast-efficient near-neighbor searching in ultra-high dimensional spaces. His work demonstrates a clear trajectory toward health informatics applications , especially in health systems and delivery of health services, where he has made significant contributions through projects like the Lumos statewide linkage programme. The analysis of his publication record reveals a consistent pattern of research bridging theoretical computer science with practical healthcare applications. His recent work shows increasing focus on health data integration, patient journey modeling, and privacy-preserving synthetic data generation for healthcare applications, while maintaining his foundational expertise in probabilistic models and efficient search algorithms. Inventor on 20 filed patents ORCID identifier: 0000-0003-0572-9936 Drake actively supervises Masters and PhD students according to his profile, and his funded research portfolio includes multiple grants from the Digital Health CRC and NSW Health. His industry experience, particularly his 13 years at Canon Information Systems Research Australia where he served as Senior Principal Engineer and lead researcher for machine learning, informs his approach to technology research methods from a commercial perspective. His current projects focus on the impact of integrated care in New South Wales, synthetic data generation, and patient journey modeling. As Co-Chief Scientist at the Digital Health CRC, Drake contributes to a major national initiative focused on digital health innovation. His work with the Lumos programme has created Australia's first statewide linked data asset across primary care and other health settings, providing unique insights about cross-setting healthcare utilization. This initiative represents a significant contribution to health data infrastructure in Australia.

• Computer Vision
• Multimedia Computation
• Machine Learning
• +۲ مورد دیگر

Litao Yu is a Part-Time Lecturer and Visiting Scholar at the Faculty of Engineering and Information Technology, University of Technology Sydney. Concurrently, he serves as a Senior Data Analyst at Australia's Department of Agriculture, Fishery and Forestry. His academic appointments include Research Fellow at UTS (2019-2024) and post-doctoral positions at Griffith University and Queensland University of Technology. Education: PhD from The University of Queensland (2013-2016) MPhil from Dalian University of Technology (2009-2012) BSc from Dalian Maritime University (2003-2007) Dr. Yu's research focuses on computer vision and machine learning with applications in agriculture, multimedia systems, and multimodal learning. His work spans few-shot learning, semantic segmentation, fine-grained recognition, and multimodal fusion techniques. Key application domains include animal welfare monitoring, aquaculture quality control, and travel information enhancement. His publications demonstrate strong thematic convergence around efficient visual recognition systems , with recurring focus on few-shot learning paradigms and attention mechanisms. Recent work shows increasing emphasis on agricultural applications of computer vision, including poultry monitoring, fish processing automation, and sheep tracking. Blockchain integration for supply chain transparency represents another emerging theme. No scientific awards are mentioned in available records. Teaching responsibilities at UTS include courses on Real-time Operating Systems and Shell Programming . No information is available regarding research grants, supervised students, or laboratory affiliations.

Lee Sue Hanمشاهده پروفایل

مدرس ارشد

• Computer vision
• Artificial intelligence
• Machine Learning
• +۲ مورد دیگر

Dr. Lee Sue Han serves as Senior Lecturer and Head of Department for Information and Communication Technologies at Swinburne University of Technology Sarawak's Faculty of Engineering, Computing and Science. She joined Swinburne in April 2020 following post-doctoral research with Pl@ntNet (CIRAD and INRIA) in France. Her academic credentials include a BEng (Hons) in Electronic Engineering from Multimedia University (2011), MSc in Electrical and Electronic Engineering from Shinshu University, Japan (2014), and PhD in Computer Science from University of Malaya (2018). Dr. Lee's research bridges artificial intelligence and plant biology, focusing on computer vision, machine learning, and deep learning applications for plant disease identification, cross-domain classification, and environmental monitoring. Her work explores how deep learning models interpret botanical features and align with human perception in disease diagnosis, with significant contributions to automated plant health systems. Recent publications (2021-2022) demonstrate advanced work in multi-task learning for disease identification, global-scale plant recognition, and interpretable AI models. Her research consistently appears in top venues including Frontiers in Plant Science, IEEE Transactions, and Pattern Recognition. Her accolades include the 2022 Outstanding Student Feedback on Teaching award, 2022 Swinburne Research Success Award, and Best Paper Awards in 2020 and 2018. She actively secures research funding as PI for FRGS 2021 and co-PI for FRGS 2022 and SDEC 2021 projects. Dr. Lee supervises research in generative models, sign language production, drug discovery, animal re-identification, and plant disease modeling. She maintains international collaborations initiated during her France-based post-doctoral work and participates in global challenges like PlantCLEF.