جست‌وجوی استاد و پژوهشگر

Murat Tahtaliمشاهده پروفایل

مدرس ارشد

• Medical Imaging
• Light Field CT and SPECT
• Adaptive Optics
• +۶ مورد دیگر

Dr. Murat Tahtali is a Senior Lecturer at UNSW Canberra within the School of Engineering and Information Technology at the University of New South Wales. His academic career spans multiple engineering disciplines with a strong focus on interdisciplinary research connecting engineering principles with medical applications. Dr. Tahtali's research interests span several interconnected fields including Medical Imaging , particularly Light Field CT and SPECT (L-SPECT), Adaptive Optics , Computer-Aided Design , Finite Element Analysis , Vibration Analysis , and Image Processing . His work bridges theoretical engineering concepts with practical medical applications, developing novel imaging techniques that can operate through challenging environments and media. His research has significant implications for both defense applications and medical diagnostics. An analysis of Dr. Tahtali's recent publications reveals a strong trend toward integrating machine learning techniques with traditional engineering approaches. His work spans medical imaging innovations, structural health monitoring using vibration analysis, computer vision for challenging environments, and deep learning applications. The interdisciplinary nature of his research connects mechanical engineering principles with computational methods and medical applications, demonstrating consistent innovation across multiple domains. Dr. Tahtali actively supervises research students and offers PhD scholarships ($35,000 per year) for high-achieving students in Computer Science, Electrical Engineering, or Mechanical Engineering. His research group works on cutting-edge problems at the intersection of engineering disciplines, with particular emphasis on medical imaging technologies and computational methods for challenging environments. His laboratory work focuses on developing novel imaging systems, particularly Light Field SPECT technology, as well as computational methods for image restoration through turbulent media. The research team combines experimental work with sophisticated computational modeling to advance imaging capabilities in both medical and defense contexts.

• Computer Vision
• Pattern Recognition
• Artificial Intelligence
• +۵ مورد دیگر

Professor Yongsheng Gao is a distinguished academic at Griffith University's School of Engineering and Built Environment, where he directs the Institute for Integrated and Intelligent Systems and the ARC Research Hub for Driving Farming Productivity and Disease Prevention. His extensive research spans computer vision, pattern recognition, and AI applications in agriculture and biomedicine. Research interests focus on hyperspectral imaging for disease detection, intelligent agriculture systems, and facial recognition technologies. His work bridges theoretical frameworks with industrial applications, particularly in sustainable farming and environmental monitoring. Publications demonstrate consistent focus on computer vision innovations, particularly transformer architectures and fine-grained classification methodologies. Recent articles emphasize applications in agricultural automation and medical imaging diagnostics. Key grants include multiple Australian Research Council projects such as the Industrial Transformation Research Hub ($12.8M) focused on farming productivity. He leads laboratories in environmental informatics and supervises doctoral candidates in computer vision applications.

Jon Lawrenceمشاهده پروفایل

استاد

• Astronomical Instrumentation
• Optical Engineering
• Photonics
• +۳ مورد دیگر

Prof Jon Lawrence is a distinguished academic and Head of Instrumentation at AAO-Macquarie, affiliated with Macquarie University. He leads a multidisciplinary team advancing astronomical instrumentation for premier telescopes, focusing on optical, mechanical, and software engineering. His research spans telescopes, spectrographs, and photonic technologies for capturing and analyzing astronomical light. Major projects include the Gemini North Adaptive Optics Bench (GNAO), GHOST spectrograph, and MANIFEST instrument for the Giant Magellan Telescope. He collaborates globally on adaptive optics systems and next-generation instrumentation. Key research interests include astronomical instrumentation development, photonics, optical engineering, and spectrograph design. Projects emphasize high-resolution spectroscopy, adaptive optics, and instrument integration for large telescopes. His work bridges engineering and astrophysics, enabling breakthroughs in observational astronomy. Collaborations span institutions like Gemini Observatory, Swinburne University, and international teams. Recent publications focus on commissioning results from GHOST (Gemini South), chemodynamical studies of Milky Way satellites, and foundational work on future instruments like MANIFEST and GNAO. Awards and recognitions include his role as Distinguished Professor (Technical/Commercial), reflecting industry-impactful research. Leadership roles include managing large-scale projects and teams in instrument R&D. His contributions advance both technical capabilities and our understanding of galaxies, stellar populations, and cosmic evolution.

• Mining Automation
• Robotics
• Machine Learning
• +۵ مورد دیگر

Dr. Mehala Balamurali is a Senior Research Fellow at the University of Sydney's Australian Centre for Field Robotics within the Faculty of Engineering. She leads research initiatives at the Rio Tinto Centre for Mine Automation and serves as Network and Engagement Lead for the Sydney Early and Mid-Career Academic Network (SEMCAN). Her research contributes to developing fully autonomous mining operations through interdisciplinary approaches combining robotics, AI, and geospatial analysis. Balamurali's research bridges multiple disciplines: Machine learning applications in geological data analysis Robotics for mining automation and safety Computer vision for equipment and environmental monitoring Cross-domain applications of AI in healthcare and materials science Her work focuses on solving industrial challenges in mining through technological innovation. Publications demonstrate consistent focus on developing AI solutions for complex industrial problems. Recent work shows expansion into multimodal applications including biomedical imaging and infrastructure monitoring. Honors and Awards: Outstanding Service Award, Australasian Joint Conference on Artificial Intelligence (2023) Best Paper Award, Center for Vascular Research (2010) Promotion to Senior Research Fellow (2024) Sydney Women's Leadership Program completion (2020) Balamurali mentors students in interdisciplinary projects spanning machine learning, geological analysis, medical imaging diagnostics, and robotics. She supervises projects on: Deep Learning in Geological Analysis, Motion Tracking in Medical Imaging, Spectral Data for Structure Mapping, and Mining Operations Optimization. As SEMCAN Engagement Lead, she coordinates academic networking and professional development for early-career researchers across the university.

Peyman Moghadamمشاهده پروفایل

دانشیار مدعو

• Self-Supervised Learning for Robotics
• Embodied AI
• 3D Multi-Modal Perception (3D++)
• +۴ مورد دیگر

Peyman Moghadam is an Adjunct Associate Professor at the University of Queensland (UQ) and a Principal Research Scientist at CSIRO Data61. He also holds an adjunct professorship at Queensland University of Technology (QUT). His roles include leading the Embodied AI Research Cluster at CSIRO Data61 and overseeing the Spatiotemporal AI portfolio within CSIRO's Machine Learning and Artificial Intelligence (MLAI) Future Science Platform. He has held visiting appointments at ETH Zürich (2022) and the University of Bonn (2019). His research focuses on self-supervised learning for robotics, embodied AI, 3D multi-modal perception, and computer vision applications in robotics and environmental science. Education details are not explicitly provided in the text, but his professional experience and research output suggest advanced academic training in robotics, computer science, and machine learning. Awards include the CSIRO Julius Career Award, National and Queensland iAwards, and the Lord Mayor's Budding Entrepreneurs Award. He has led large-scale interdisciplinary projects and published extensively in top-tier journals and conferences. His research themes span robotics perception, AI-driven environmental modeling, and sensor fusion. Notable contributions include benchmark datasets (e.g., WildScenes), novel algorithms for LiDAR place recognition, and geo-encoded transformers for plant species prediction. His work bridges robotics, machine learning, and real-world applications in agriculture, environmental monitoring, and autonomous systems. Awards: CSIRO Julius Career Award, Collaboration Medal, National/Queensland iAwards, Lord Mayor's Budding Entrepreneurs Award Grants & Projects: Led multidisciplinary projects in robotics, AI, and environmental science funded by CSIRO and industry collaborators Labs/Teams: Embodied AI Research Cluster (CSIRO Data61), Spatiotemporal AI portfolio (CSIRO MLAI)

Deborah Apthorpمشاهده پروفایل

پژوهشگر

• Neuroscience
• Parkinson's Disease Research
• Cognitive Psychology
• +۵ مورد دیگر

Dr. Deborah Apthorp is a researcher at the University of New England's School of Psychology within the Faculty of Humanities, Arts, Social Sciences and Education. She leads innovative Parkinson's disease research focusing on early diagnosis and progression tracking through EEG brain imaging, eye-tracking, visual perception tests, finger-tapping assessments, and postural sway analysis. Her work addresses critical healthcare needs as Parkinson's affects approximately 80,000 Australians with cases projected to increase 30-50% in the next decade. Her research interests span multiple neuroscience domains: Neurological disorders, particularly Parkinson's disease Motor function and control mechanisms EEG and brain imaging techniques Postural stability assessment Machine learning applications in neurological diagnosis Cognitive performance in neurological conditions Dr. Apthorp has pioneered a mobile research lab initiative funded by the Perpetual Foundation's Impact Philanthropy program, bringing cutting-edge neurological research directly to rural and regional communities in NSW and southern Queensland. This addresses healthcare disparities by ensuring people outside major metropolitan areas can participate in research that has traditionally been concentrated in cities. Her scientific contributions include numerous publications on Parkinson's disease diagnostics, with recent work analyzing EEG data, developing machine learning algorithms for disease classification, and establishing objective measurement techniques for disease progression. Her research demonstrates significant interdisciplinary collaboration between neuroscience, computer science, and clinical practice. Dr. Apthorp's work has received recognition through Impact Philanthropy funding from the Perpetual Foundation, supporting her mobile research lab initiative. This funding enables extension of previous Parkinson's research she began at the Australian National University into rural and regional Australia. She maintains strong collaborative relationships with colleagues at the Australian National University's Research School of Computer Science and clinicians at The Canberra Hospital, creating comprehensive research frameworks that bridge multiple disciplines to address complex neurological questions. Her commitment to equitable research access strengthens UNE's role as a regional university contributing meaningfully to community health outcomes.

• Artificial Life
• Ecological Modelling
• Insect-plant interaction
• +۳ مورد دیگر

Alan Dorin is a Professor in the Department of Data Science & AI within the Faculty of Information Technology at Monash University. His research explores ecological modeling, artificial life, and biologically-inspired electronic art. He teaches units including Research and Innovation in Computer Science and Creative Computing. His research bridges computing with biology and ecology through: Insect-plant interaction simulations for sustainable agriculture Generative art systems inspired by biological processes Historical analysis of technology-human relationships Recent publications focus on pollinator behavior analysis, ecological simulation, and applications of computer vision in biodiversity conservation. He leads projects on precision pollination and national pollination strategy development funded by ARC and National Geographic. Dorin serves on multiple faculty committees and is Editor-in-Chief of the Artificial Life journal.

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

Dr. Hamid Rezatofighi is a Senior Lecturer in the Department of Data Science & AI at Monash University's Faculty of Information Technology in Australia. He holds a PhD from the Australian National University (2015) and a Master's in Biomedical Engineering from the University of Tehran (2009). Previously, he was an Endeavour Research Fellow at Stanford University and a Senior Research Fellow at the University of Adelaide's Australian Institute for Machine Learning (AIML). His research focuses on computer vision, robotics, and deep learning, with emphasis on robot visual perception in dynamic environments. Key contributions include multi-object tracking, autonomous systems, and neuro-symbolic AI frameworks. He has led over $17M in research funding, including DARPA projects and an ARC Discovery Project. His work contributes to UN SDG goals related to innovation and infrastructure. Notable projects include the Neuro-Symbolic Systems (HARNESS) project ($5M+), real-time safety intervention systems, and semantic 3D reconstruction frameworks. He has published >80 peer-reviewed papers in top venues like CVPR/ICCV/ECCV and serves as an Area Chair for major AI conferences. His datasets (JRDB series) are widely used benchmarks for robotic perception. Current research spans autonomous navigation, multi-sensor fusion, and socially-aware human-robot interaction.

Houying Zhuمشاهده پروفایل

مدرس

• High-dimensional learning
• Statistical computing
• Modelling and visualisation
• +۴ مورد دیگر

Dr. Houying Zhu is a Lecturer in Statistics at Macquarie University's School of Mathematical and Physical Sciences. Her research focuses on high-dimensional learning, statistical computing, and modelling/visualization, with an emphasis on developing efficient methodologies for modern data analysis. She holds a PhD in Applied Mathematics from the University of New South Wales and has held research roles at City University of Hong Kong and the University of Melbourne as a Maurice Belz Research Fellow in Statistics. Dr. Zhu is Co-Director of the Statistical Modelling Research Group, providing mentorship to early-career researchers and HDR students through regular supervision and collaborative projects. She actively engages in academic leadership, serving on the Board of Directors for the IASC-ARS (2023-2027) and the SSA NSW branch council (2021-2023). Her teaching spans undergraduate and postgraduate units, including Bayesian Data Analysis, Generalized Linear Models, and Statistical Graphics. Research Highlights: Innovations in quasi-Monte Carlo methods, feature selection algorithms, and medical imaging applications. Awards: Includes the President’s Award for Leadership in Statistics (2022) and multiple funding grants (MATRIX Family Funding, SSA Fellowship). Community Engagement: Organized the MATRIX Computational Mathematics for High-Dimensional Data program (Feb 2023) and contributes to statistical education initiatives. Her interdisciplinary work bridges computational statistics and real-world applications, with recent projects addressing wildfire risk modelling, protein engineering, and 6G communication systems.

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.

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.

Golnoush Abaeiمشاهده پروفایل

مدرس

• Software Engineering
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Golnoush Abaei is a Lecturer in Industry Practice at RMIT University's School of Computing Technologies. She holds dual roles as Professional Practice Lead (since 2022) and Program Manager for the Bachelor of Software Engineering (since 2023). Her expertise bridges academia and industry, focusing on software engineering, machine learning, and data analytics. She coordinates the Software Testing Course and supervises capstone projects for software engineering undergraduates and IT postgraduates. Research interests include software quality, INS/GPS navigation systems, health-related recognition applications, and blockchain. Notable projects involve AI-powered digital lecturer twins for education and machine learning-driven visual defect detection in software interfaces. Teaching methodologies emphasize collaborative active learning (CAL), problem-based learning (PBL), and curriculum design innovation. She actively supports student internships and graduate job placements through industry partnerships. Over 25 peer-reviewed articles span topics like anomaly detection in IoT networks, medical image processing, and hybrid optimization algorithms. Key Projects: Machine Learning for Testing, AI in Education, BDD Test Automation Grants/Industry Collaboration: Multiple industry-sourced capstone projects Curriculum Development: PBL integration, CAL frameworks, and active learning strategies

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۴ مورد دیگر

Prof Kai Qin is a Professor of AI and Data Science at Swinburne University, affiliated with the School of Science, Computing and Emerging Technologies. He holds roles including Director of the Intelligent Data Analytics Lab, Deputy Director of the Swinburne Space Technology and Industry Institute, and Vice President for Education at IEEE Computational Intelligence Society (CIS). Qin earned his B.Eng. from Southeast University (2001) and PhD from Nanyang Technological University (2007). His research focuses on Computational Intelligence (CI), encompassing Neural Networks, Evolutionary Computation, and Fuzzy Systems, with applications in Machine Learning, Remote Sensing, and Pervasive Computing. His work has garnered over 20k Google citations and recognition such as IEEE Fellow (2025). Key achievements include the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award and leadership in conferences like IJCNN 2022. He pioneered the Master of Data Science program at Swinburne (2018–2020) and leads initiatives in federated learning, onboard AI for satellite missions, and AI-driven medical diagnostics. Qin’s professional contributions span editorial roles in journals like Swarm and Evolutionary Computation and leadership in IEEE technical committees. His grants include SmartSat CRC projects for satellite AI and ARC-funded research on gravitational lensing and traffic analytics.

• Biomedical Imaging
• Dermatology
• Machine Learning
• +۳ مورد دیگر

Dr. Nathasha Naranpanawa is a Postdoctoral Research Fellow at Queensland University of Technology (QUT), affiliated with the School of Mech., Medical & Process Engineering. She holds a PhD from the University of Queensland. Her research focuses on biomedical imaging and genomics, with interdisciplinary applications in dermatology, machine learning, and plant genetics. Key research areas include automated lesion classification via deep learning, melanoma risk biomarkers, and genetic analysis of plant species. Her work bridges engineering and life sciences, demonstrated through collaborations like the QUT-Sealy Centre for Biomechanics and Sleep Research. Notable publications include studies on nevus counting methodologies, neural network-based lesion classification, and genomic analysis of Cinnamomum species. She has contributed to both clinical and computational domains, with peer-reviewed articles in journals like Journal of Investigative Dermatology and Scientific Reports . Dr. Naranpanawa's research emphasizes practical applications, such as improving medical imaging tools and developing accessible bioinformatics workflows for biologists. No specific scientific awards are listed, though her active publication record indicates sustained academic engagement.

• Transportation Safety
• Intelligent Transportation Systems (ITS)
• Machine Learning
• +۳ مورد دیگر

Dr. Mohammed Elhenawy is a Senior Lecturer and Senior Research Fellow at the Centre for Accident Research & Road Safety – Queensland (CARRS-Q), Queensland University of Technology (QUT), Brisbane, Australia. He holds a PhD in Computer Engineering from Virginia Tech. His research focuses on applying machine learning, AI, and statistical learning to transportation safety and intelligent transportation systems (ITS). He has authored over 120 papers and led projects like Australia’s largest Cooperative Intelligent Transport Systems (C-ITS) pilot, the LAARMA system for animal-vehicle collision prevention, and the 'Hold the Red' traffic signal evaluation. His work has been recognized with awards such as the Best Paper Award in Intelligent Vehicle Technologies. Affiliations: CARRS-Q, Faculty of Health (School of Psychology & Counselling) Education: PhD in Computer Engineering (Virginia Tech) Research Interests: Transportation safety, AI-driven traffic solutions, cooperative vehicle-infrastructure systems, and data-driven safety models. His work spans connected vehicles (V2V/V2I), near-miss detection, and multimodal large language models (MLLMs) for real-time traffic control. He also explores applications in pedestrian safety, e-scooter rider protection, and tunnel glare mitigation. Articles Trends: Recent work emphasizes LLMs for traffic safety analysis, multimodal data fusion, and autonomous vehicle perception. Key themes include real-time intersection control, zero-shot learning for crash analysis, and optimizing urban mobility systems. Awards: Best Paper Award in Intelligent Vehicle Technologies Advising & Grants: Leads interdisciplinary projects funded by Australian transport agencies and collaborates globally on ITS initiatives. His work bridges academia and industry, focusing on deployable safety solutions. Labs/Teams: CARRS-Q, collaborating with QUT’s School of Psychology & Counselling and external transport agencies.