Find Academic Supervisors

• Computational Mechanics
• Biomechanics
• Mechanical and Material Engineering
• +3 more

Professor YuanTong Gu is the Pro Vice-Chancellor (Research Career Advancement) and Head of the School of Mechanical, Medical and Process Engineering at Queensland University of Technology (QUT). He holds an ARC Future Fellowship and leads the Laboratory for Advanced Modelling and Simulation in Engineering and Science. His research focuses on computational mechanics, biomechanics, and nanotechnology, with over $40M in secured research funding since 2000. He has supervised over 15 PhD students and collaborates with global institutions including Tsinghua University and the University of California. His awards include the International Computational Methods Award (2017) and ICACM Computational Mechanics Award (2017). Prof. Gu's work spans interdisciplinary projects in joint biomechanics, advanced materials, and AI-driven engineering solutions. Education: PhD (National University of Singapore), Graduate Certificate in Education (QUT). Research Groups: Leads a team of 3 academics, 2 ARC Future Fellows, and 15+ PhD students. Key projects include diamond nanothread composites, AI in biomedical devices, and fracture detection frameworks. His group collaborates with industry leaders and global universities to advance computational methods and materials science. Editorial Roles: Associate Editor of Engineering Analysis with Boundary Elements and Applied Mathematical Modelling , among others. Conference leadership includes chair roles in international computational mechanics conferences since 2012.

• Artificial Intelligence
• Image Processing
• Narrative Planning
• +3 more

Associate Professor Julie Porteous is affiliated with the School of Computing Technologies at RMIT University. Her research focuses on Artificial Intelligence applications including narrative planning, medical image processing, and human-agent interaction. She supervises research projects in areas such as dynamic transportation networks, medical imaging frameworks, and explainable AI in planning systems. Her work combines AI techniques with storytelling, medical diagnostics, and collaborative decision-making. Notable contributions include SellaMorph-Net for medical image segmentation and advancements in automated narrative generation systems. She actively contributes to conferences like AAMAS and AAAI, publishing on topics ranging from story sifting algorithms to intention communication in multiagent environments. Dr. Porteous collaborates on projects involving interactive narrative platforms, virtual urban environments, and neurofeedback systems for adaptive storytelling. She maintains an ORCID profile (0000-0003-4618-2359) and is open to supervising PhD/Masters students in her research areas.

Sunil AryalView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +10 more

Sunil Aryal is an Associate Professor of Data Science at the School of Information Technology, Faculty of Science Engineering and Built Environment, Deakin University, Australia. He received his PhD and Master by Research degrees from Monash University Australia and has published over 70 papers in top-tier international venues in Artificial Intelligence, Machine Learning and Data Mining. Dr. Aryal's educational background includes: Graduate Certificate of Higher Education Learning and Teaching, Deakin University (2020) PhD in Computer Science, Monash University (2017) Master of Information Technology (Research), Monash University (2012) Master of Information Technology (Coursework), University of Southern Queensland (2008) Bachelor of Information Technology, Purbanchal University, Nepal (2005) His primary research interests focus on making Machine Learning and Data Mining algorithms robust and flexible to handle heterogeneous, noisy and uncertain data in real-world problems. His work spans across several specific areas including anomaly detection, clustering, kernel/similarity-based learning, ensemble methods, learning from limited data, reinforcement learning, natural language processing, and computer vision. Dr. Aryal is particularly interested in applying these techniques to solve challenges in Defence, National Intelligence, Engineering, Manufacturing, Healthcare and Education. Dr. Aryal co-leads the Machine Learning for Decision Support (MLDS) Research Group at Deakin University and has secured over AUD 4.5 million in external research funding. His research is supported by diverse organizations including US and Australia Defence Agencies, the Australian Office of National Intelligence, Worksafe Victoria, the Victorian State Department of Education and Training, the Technology Innovation Institute (TII) UAE, and Table Tennis Australia (TTA). His notable awards include multiple Deakin University research and teaching awards, the Australian Postgraduate Award for his PhD studies, and several student travel awards during his doctoral candidature. Dr. Aryal actively supervises numerous PhD and Master's students and has contributed significantly to teaching in various courses at Deakin University and previously at Federation University. He serves on several university committees and contributes to the research community as a reviewer, program committee member, and editor for various journals and conferences.

• Computer Vision
• Artificial Intelligence
• Robotics
• +7 more

Christopher McCarthy is an Associate Professor in the Department of Computing Technologies within the School of Science, Computing and Emerging Technologies at Swinburne University of Technology. His research focuses on computer vision algorithms applied to robotics, intelligent transport systems, and assistive technologies, particularly for people with low vision. He serves as Stream Leader in Swinburne’s Innovative Planet Research Institute, leading the Intelligent Transport stream, and is a Chief Investigator in the Australian Cobotics Centre funded by the ARC. He has held research roles at CSIRO Data61, the Bionics Institute, and the University of Melbourne, contributing to bionic eye technology under the Bionic Vision Australia consortium. His research interests include: Computer Vision and AI for real-time systems Robot perception and navigation Assistive technologies for low-vision and blind users Intelligent transport systems and video analytics Human-machine interaction and cyber-human teams His recent publications reflect strong trends in deep learning, continual learning, and real-world deployment of vision systems in transport and healthcare. He has led numerous field trials and evaluations to assess system performance in real-world contexts. His work is highly interdisciplinary, combining computer science with engineering, medicine, and urban planning. Christopher McCarthy has received multiple awards, including: FSET Research Collaboration Award Excellence in Industry Engagement Special Commendation – VC Research Impact Finalist – National Disability Award in Technology Best Paper Award (IEEE) Excellence in Teaching (University of Melbourne) He has supervised over 20 HDR students in areas including robotics, AI, assistive tech, and transport analytics. He has led major research grants from ARC, Defence, SmartCrete CRC, iMOVE, and city councils. He also served as Academic Director for Work-Integrated Learning (2016–2023) and coordinated professional placements. His teaching includes core computer science units such as Computer Systems and Object-Oriented Programming. He maintains ongoing affiliations with: Bionics Institute (Honorary Member) Bionic Vision Australia (Affiliate) Data61 (Honorary Member) Royal Children's Hospital, Melbourne International Task Force for Vision Restoration Outcomes (Chair)

• Artificial Intelligence
• Machine Learning
• Artificial Neural Networks
• +19 more

Professor Stephan Chalup is a leading academic in Artificial Intelligence and Machine Learning at the University of Newcastle , affiliated with the School of Information and Physical Sciences and the Data Science and Statistics department. He leads the Interdisciplinary Machine Learning Research Group (IMLRG) and the Newcastle Robotics Lab , where his team has achieved global recognition, including two RoboCup world championships. PhD in Computing Science , Queensland University of Technology (2002) Diplom in Mathematics with Neuroscience , University of Heidelberg His research focuses on artificial neural networks , deep learning , and high-dimensional data analysis , with applications in robotics, computer vision, medical imaging, and architectural analysis. He investigates how biological neural systems inspire robust AI models, particularly in topological data analysis and 4D vision . The recent publications highlight a strong trend in topological and geometric machine learning , with a focus on 4D data analysis , multi-agent reinforcement learning , and robot perception . His work bridges theoretical AI with practical industry solutions in transport, healthcare, and robotics. RoboCup World Champion (2008, 2006) Leadership Excellence, CESE (2024) Supervision Research Excellence Award (2015) Multiple Best Student Paper Awards (2019, 2018, 2011) Chalup has supervised numerous students and led significant research projects, including the ARC Discovery Project on estimating topology of low-dimensional data. His lab fosters interdisciplinary collaboration and innovation, with alumni working in top global tech roles. He is an active keynote speaker and program committee member in major AI conferences. His labs, including the Newcastle Robotics Lab , are equipped with state-of-the-art robots and computing systems, supporting cutting-edge research in humanoid robotics, autonomous navigation, and AI-driven data analysis.

Jia LiuView profile

Research Fellow

• Bayesian statistics
• Spatial data analysis
• Image analysis
• +3 more

Dr. Jia Liu serves as a Research Fellow at the Australian National University's Research School of Biology (Division of Ecology and Evolution) and concurrently holds a Technical Officer position in the Research School of Earth Sciences. Holding a PhD in Statistics, Dr. Liu conducts interdisciplinary research spanning statistical methodology, earth sciences, and computer vision applications. Education: PhD in Statistics Research focuses on Bayesian statistics, spatial data analysis, image analysis, geostatistics, and experimental design. Dr. Liu applies these methods to paleomagnetism, atmospheric science, and computer vision, with recent emphasis on uncertainty quantification and deep learning architectures for complex data analysis. Publication trends reveal dual trajectories: earth sciences applications (2020-2022) featuring directional statistics for paleomagnetic data and cloud physics modeling, alongside computer vision advancements (2021-2025) in 3D reconstruction, object detection, and segmentation. Core methodological contributions include novel approaches for spatiotemporal survey design and aleatoric uncertainty modeling. Awards: No major scientific awards documented Student advising activities and research grant funding details are not publicly available. The researcher maintains affiliations across multiple ANU schools but specific laboratory or team memberships were not specified in source materials.

Alexandre MendesView profile

Senior Lecturer

• Machine Learning
• Optimization
• Deep Learning
• +6 more

Dr. Alexandre Mendes is a Senior Lecturer at the University of Newcastle, affiliated with the School of Information and Physical Sciences and the Department of Computing and Information Technology. His research focuses on machine learning, optimization, and deep learning, with applications in robotics, energy systems, and transport logistics. Education: PhD in Electrical Engineering, Universidade Estadual de Campinas, Brazil MSc in Electrical Engineering, Universidade Estadual de Campinas, Brazil Bachelor in Applied Mathematics, Universidade Estadual de Campinas, Brazil Graduate Certificate in the Practice of Tertiary Teaching, University of Newcastle Research Interests: Mendes specializes in applying machine learning to real-world problems such as optimizing railway logistics, predicting solar energy output, and improving robotic systems. His work bridges theory and practice, collaborating with industries like CSIRO and Hunter Valley Coal Chain. Grants and Funding: He has secured over AU$1.5M in grants from ARC, CSIRO, and industry partners. Key projects include coal train scheduling optimization and solar energy prediction using deep learning. Awards: Work Integrated Learning Staff Member of the Year (2020) Best Student Paper Award (2012) Pro Vice-Chancellor's Research Excellence Award (2011) Labs and Teams: He leads the NUbots robotics team, focusing on humanoid robots and autonomous systems. His research group collaborates on projects like the Sydney Metro and Hunter Region rail network optimization.

• Medical Imaging
• Computer Vision
• Deep Learning
• +4 more

Dr Abdullah Nazib is a Research Fellow at Queensland University of Technology (QUT) in the Faculty of Engineering, School of Electrical Engineering & Robotics. His research focuses on applying advanced computational techniques to medical imaging challenges, particularly in cancer detection and treatment planning. His research interests include: Medical image registration and segmentation Deep learning architectures for healthcare applications 3D medical image analysis Prostate cancer detection and grading Organ segmentation for radiation therapy Radiomics-based diagnostic systems Analysis of Dr Nazib's publication history reveals a clear evolution from general computer vision research toward specialized medical applications. His recent work demonstrates significant contributions to uncertainty quantification in medical image segmentation and multimodal learning approaches for diagnostic imaging. The 2024 publications particularly highlight his focus on clinically relevant applications with immediate potential impact on cancer diagnosis and treatment planning. His collaborative research network includes strong partnerships with Dr Fookes, Dr Perrin, and other members of QUT's biomedical imaging group, reflecting an interdisciplinary approach that bridges computer science, engineering, and clinical medicine to develop practical AI solutions for healthcare challenges.

• Geographic Information Systems (GIS)
• Remote Sensing
• Spatial Science
• +4 more

Dr. Xiaoye Liu is a Senior Lecturer in the School of Surveying and Built Environment at the University of Southern Queensland, specializing in Surveying and Spatial Science. Their research focuses on spatial data quality, remote sensing applications, and geospatial technologies for environmental and disaster management. Dr. Liu holds a PhD from Monash University, preceded by a MAppIT from Monash, and degrees from Wuhan University. Education: Bachelor of Engineering (BEng), Wuhan University Master of Engineering (MEng), Wuhan University Master of Applied Information Technology (MAppIT), Monash University Doctor of Philosophy (PhD), Monash University Research interests include LiDAR data analysis, crowdsourced data credibility assessment, and spatial modeling for environmental conservation and disaster response. Dr. Liu’s work bridges GIS applications with ecological monitoring, particularly in rainforest ecosystems and wildlife habitat studies. Research Trends in Publications: Recent work emphasizes crowdsourced data quality (VGI/CSD), LiDAR-derived environmental modeling, and machine learning applications in spatial science. Key themes include geospatial data fusion, disaster management automation via SDI, and biodiversity tracking through spatial analytics. Advising and grants information is not explicitly detailed in the provided data. Dr. Liu’s contributions include collaborations on projects such as bilby habitat modeling and Hendra disease spatial epidemiology.

• Big Data Analytics
• Sentiment Analysis
• Data Management
• +3 more

Bela Stantic is a Professor at Griffith University's School of Information and Communication Technology, where he founded and directs the Big Data and Smart Analytics Lab . With a PhD from Griffith University, his academic appointments include Head of School (2015-2019) and Deputy Head of School (2013-2015). He remains an active member of the Institute for Integrated and Intelligent Systems since 2005. Research Focus: Professor Stantic specializes in Big Data Analytics, particularly in social media sentiment analysis and complex data management. His interdisciplinary work spans tourism analytics, environmental monitoring, cryptocurrency forecasting, and AI ethics. Key applications include disaster response systems, aquaculture optimization, and sustainable infrastructure. Publication Trends: Recent articles (2023-2025) demonstrate strong emphasis on AI-driven solutions: 40% focus on time-series forecasting and cryptocurrency prediction, 30% on computer vision/LiDAR applications, and 30% on social media analytics for tourism and public policy. Machine learning integration across environmental and financial domains is a consistent theme. Grants & Projects: Secured 24 funded projects totaling ~$1.8M AUD, including: Automated seagrass monitoring ($403K, 2023-2025) NSW flood impact analysis via social media (2022) Offshore aquaculture welfare systems ($466K, 2022-2026) COVID-19 pandemic behavior tracking (2020) Student Advising: Supervised 15+ doctoral students to completion in areas like 3D data extraction, social media mobility tracking, and stream-processing engines. Currently mentoring PhD candidates on multimodal sentiment analysis and real-time recommendation systems.

• Signal processing
• Audio processing
• Spatial audio
• +7 more

Professor Christian Ritz serves as Director of SMART and Professor in the School of Electrical, Computer and Telecommunications Engineering within the Faculty of Engineering and Information Sciences at the University of Wollongong. He has held his professorship since 2017 and served as Associate Dean (Research) in 2022 and Associate Dean (International) from 2016-2021. Professor Ritz's research focuses on signal processing for speech, audio, acoustics and visual information. His current projects include microphone array signal processing for enhancing sound recorded in classrooms, sound scene classification for dementia care environments, spatial audio for Virtual and Augmented Reality, and undersea surveillance. His work spans multiple technical domains including signal processing, audio engineering, computer vision, and communications engineering. His recent publication trends show a strong focus on neural network applications for audio processing, particularly for classroom monitoring and spatial audio reproduction. His work integrates deep learning with traditional signal processing techniques to address challenges in spatial audio synthesis, room impulse response generation, and directional audio reproduction. He has also contributed to applications in mining automation, public safety surveillance, and positioning systems. Professor Ritz is a member of the Centre for Signal and Information Processing (CSIP), part of the Signals, Information and Communications Research Institute (SICOM) at the University of Wollongong. His research is funded by the ARC, overseas research institutes, and defense organizations. His grant portfolio is extensive, including projects such as AI/IoT-powered Airborne System for Monitoring Water Level and Tidal Floods, Multi-Modal Satellite-based Vessel Surveillance, Net Zero Industry and Innovation Program, Smart Eye: Airborne and AI-Driven Assessment Solution of Sugarcane, and numerous projects related to spatial audio and underwater surveillance. He has secured funding from diverse sources including the Australian Research Council, defense organizations, and international collaborations. Professor Ritz maintains an active research laboratory environment focused on signal and information processing. His work spans multiple application domains from classroom monitoring to underwater mine detection, with strong connections to both academic and industry partners. His research group appears to focus on practical applications of signal processing techniques to solve real-world problems across various domains.

Cheryl McCarthyView profile

Associate Professor

• Agricultural Engineering
• Machine Vision
• Robotics
• +5 more

Dr. Cheryl McCarthy is an Associate Professor in Mechatronic Engineering and Senior Research Fellow at the University of Southern Queensland, focusing on automation and machine vision technologies in agriculture. She has been a full-time researcher since completing her PhD in 2009, with expertise in machine vision, robotics, and precision agriculture. Education: BEng (2005) and PhD (2004) in Mechatronic Engineering from the University of Southern Queensland. Research interests include crop disease detection (e.g., wheat root rot using NIR spectroscopy and machine learning), automation in farming operations, and robotic systems for precision agriculture. Her work spans orchard mapping, UAV-based irrigation monitoring, and livestock welfare assessment via machine vision. She has led projects on weed detection, fruit load estimation, and robotic navigation in agricultural environments. Key contributions include developing the 'MangoYOLO' deep learning model for fruit detection and advancing non-destructive crop sensing technologies. Her research integrates robotics, computer vision, and environmental sensing to address challenges in sustainable agriculture. Current and completed supervisions include projects on machine learning applications in meat processing, precision agriculture, and robotic localization in orchards. Major grants include farm trials for chicken welfare monitoring (2023), precision weed detection systems (2016), and harvester loss assessment (2019). Labs/Teams: Works within the Centre for Agricultural Engineering, collaborating on projects like space agriculture sensing and crop disease diagnostics.

• Surveying
• LiDAR Technology
• Cadastral Systems
• +4 more

Glenn Campbell is an Associate Professor (Surveying) at the University of Southern Queensland's School of Surveying and Built Environment. His research spans spatial data law, LiDAR applications, cadastral systems, and remote sensing of water resources. Campbell employs advanced techniques including machine learning for topographic feature extraction and matrix inversion methods for water quality assessment. Research Focus: Geospatial law and indigenous land boundaries LiDAR processing for urban feature extraction Water quality remote sensing techniques Cadastral system modernization His publications demonstrate consistent focus on measurement accuracy, spatial data governance, and advanced geospatial analysis techniques. Recent work shows increasing emphasis on digital copyright issues in spatial data and machine learning applications for LiDAR processing. Awards & Recognition: No major scientific awards reported. Student Advising: No advisee information available.

• Programming Languages
• Compiler Construction
• Parallel Computing
• +7 more

Wayne Kelly is an Associate Professor in the School of Computer Science at Queensland University of Technology (QUT), Faculty of Science. He has over 25 years of academic experience and serves as the Academic Lead for Teaching and Learning and Course Coordinator for the Bachelor of Information Technology degree. PhD in Computer Science, University of Maryland, College Park, 1996 BSc (Hons) in Computer Science, University of Queensland, 1989 His research expertise lies in Programming Languages, Compiler Construction, and Parallel Computing, with significant contributions to High Performance Computing, Big Data, and Bioinformatics. His work has led to collaborations with Microsoft Research and over $2 million in external funding. His recent publications reflect a strong trend in parallel and distributed systems, embedded computing, bioinformatics data analysis, and remote sensing. Key themes include optimization of computational systems, memory management, and scalable data processing. Wayne Kelly has made impactful contributions to both teaching and research, guiding numerous postgraduate students and leading curriculum development in information technology. Optimizing I/O cost and managing memory for bioinformatics A communication model for streaming applications on MPSoC Ruby.NET: a compiler for the Common Language Infrastructure He is actively engaged in real-world technology development, including a project with a vision-impaired student to improve public transportation accessibility, currently trialed by transport authorities in Australia and the US.

• Deep Learning
• Computer Vision
• Few-shot Learning
• +4 more

Tan Yong Xuan is an Assistant Lecturer at the Malaysia School of Information Technology, specializing in advanced AI research. His work focuses on Deep Learning, Computer Vision, and Self-supervised Learning techniques, particularly in Few-shot Learning and Generative Adversarial Networks (GANs). He holds a Master of Science in Information Technology (2023) and a Bachelor of Information Technology (Honours) in Artificial Intelligence (2019), both from Multimedia University. His research explores innovative applications of AI in fields like medical diagnostics (e.g., brain tumor detection), e-commerce analytics (customer behavior modeling), and text-to-image synthesis. Notable contributions include developing self-supervised learning frameworks for Few-shot Learning and advancing GAN-based image generation systems. His work frequently addresses challenges in data scarcity and model efficiency. Recent publications span 2022-2025, emphasizing methodological advancements in Few-shot Learning, Transfer Learning, and generative AI. Collaborations involve multidisciplinary teams focusing on real-world AI applications. No formal awards are listed, but his active publication record reflects academic engagement.