Find Academic Supervisors

Enrico CoieraView profile

Professor

• Health Informatics
• Artificial Intelligence
• Health Services Research
• +5 more

Enrico Coiera is a Professor and Director of the Centre for Health Informatics at Macquarie University's Australian Institute of Health Innovation. He holds a PhD in Computer Science (UNSW) and MBBS (University of Sydney). His research spans digital health, patient safety, AI in healthcare, and health systems science. He has led projects at Hewlett-Packard Research Laboratories and co-founded Healthbanc via Healthy.me technologies. He is an Associate Editor of 'Artificial Intelligence in Medicine' and a Fellow of multiple health informatics societies. Education: PhD (Computer Science, UNSW), MBBS (Sydney) Research focuses on leveraging AI and informatics for healthcare delivery, including clinical decision support, e-health systems, and climate change resilience. Notable contributions include the textbook 'Guide to Health Informatics' and awards like the IMIA François Grémy Award (2015). He leads initiatives like the Fujitsu Macquarie AI Research Lab and projects on AI advisories and climate risk communication. Over 350+ publications and 8+ prizes highlight his global impact. Awards: IMIA Award, UNSW Inventor of the Year, IBM Faculty Award

• Artificial Intelligence
• Retinal Diseases
• Gene Therapy
• +5 more

Han Trinh is an Associate Lecturer at the School of Allied Health , The University of Western Australia , specializing in Optometry and Vision Science . Their work focuses on applying Artificial Intelligence to inherited retinal diseases and ocular health evaluation. Their research intersects Medicine , Genetics , and Biomedical Engineering , particularly in developing AI models for Retinal Disease diagnosis and Gene Therapy applications. Recent outputs include reviews on AI techniques in retinal disorders and clinical evaluations of gene therapy outcomes. Collaborative efforts extend to international institutions, with expertise covering Visual Impairment , Genetic Disorders , and Clinical Trials . No awards, part-time status, or supervisory activity details are available in the current profile.

Moein KhajehnejadView profile

Research Fellow

• Active Inference
• Reinforcement Learning
• Computational Neuroscience
• +4 more

Moein Khajehnejad is a Research Fellow in the Computational & Systems Neuroscience Laboratory at the Turner Institute for Brain and Mental Health, Monash University, working with Prof. Adeel Razi. His research integrates Active Inference, Reinforcement Learning, and Network Science to study biological mechanisms of cognition using in-vivo and in-vitro brain recordings. He holds a PhD in Data Science/AI from Monash University, focusing on task allocation in multi-agent systems via Game Theory and Evolutionary Graph Theory. He has conducted visiting research at institutions including Max Planck Institute, EPFL, and HKUST. Education: PhD in Data Science and AI (Monash University, 2023), specializing in Game Theory and Multi-Agent Systems. Prior academic qualifications include a focus on computational methodologies. Research Interests: Active Inference, Reinforcement Learning, Computational Neuroscience, Graph Representation Learning, and Multi-Agent Systems. His work bridges theoretical frameworks with experimental neuroscience, exploring how biological systems achieve lifelong learning through network dynamics and decision-making processes. Key Contributions: Published 10+ peer-reviewed articles across journals/conferences, including work on social learning dynamics, fairness in node representation, and neuronal network analysis. His research has been recognized through awards like the Best Reviewer Award at LoG 2022 and travel grants to NeurIPS 2019. Grants & Advising: Accepting PhD students since 2017. Collaborates internationally with teams in Switzerland, Singapore, and Hong Kong. Active in interdisciplinary projects linking AI, neuroscience, and complex systems. Labs & Affiliations: Core member of Monash’s Computational & Systems Neuroscience Lab. Affiliated with Turner Institute’s brain and mental health research programs.

Fuad NomanView profile

Lecturer

• Biomedical Engineering
• Machine Learning
• Signal Processing
• +4 more

Fuad Noman is a Lecturer at the Malaysian School of Information Technology, Monash University. He holds a PhD in Biomedical Engineering from Universiti Teknologi Malaysia (2019), an M.Sc. in Electronics Engineering (Telecommunication Systems) from Universiti Teknikal Malaysia Melaka (2011), and a B.Sc. in Electronics from the University of Sudan for Science and Technology. Previously, he was a postdoctoral fellow at the Institute of Sustainable Energy (ISE), University Tenaga National (UNITEN). His research focuses on biomedical signal processing, machine learning applications, and biometrics. Key research projects include deriving functional MRI biomarkers for Friedreich Ataxia using structural data and developing adversarial AI for materials discovery. He actively contributes to UN Sustainable Development Goals related to education and health. As of 2024, he is a Chief Investigator on two major research projects and has published extensively in conferences like IJCAI, ICASSP, and ISBI. His recent work explores deep learning frameworks for brain disorder identification, functional connectivity augmentation via GANs, and dynamic MRI reconstruction techniques. He accepts PhD students and emphasizes interdisciplinary approaches in biomedical and computational research.

• Biomedical Imaging
• Neuroimaging
• Neurodegenerative Diseases
• +3 more

Professor Gary Egan is a leading neuroscientist at Monash University's Monash Biomedical Imaging department. He specializes in neurodegenerative diseases such as Huntington's and Friedreich's ataxia, using advanced imaging techniques like MRI and X-ray synchrotron imaging to map disease progression. His research focuses on early detection of brain changes to enable preventive treatments, leveraging cutting-edge technologies such as deep learning for medical image analysis. Research Interests: Biomedical imaging and neuroimaging techniques Neurodegenerative disease mechanisms Early biomarker identification for neurodegenerative conditions Medical imaging technology development (MRI, PET-MR, synchrotron imaging) Projects: Leading studies on Huntington's disease using MRI to track brain changes Mouse model studies at the Australian Synchrotron to investigate cellular-level disease progression Cross-disciplinary collaborations in AI-driven medical image reconstruction (e.g., CL-MRI, Co-Manifold learning) Grants & Collaborations: Chief investigator on NHMRC-funded projects studying brain structure/function impacts Collaborations in AI-based image analysis frameworks International partnerships in neuroimaging and metabolic connectivity research Labs/Teams: Leads the Monash Biomedical Imaging team, collaborating with institutions like the Victorian Heart Institute (VHI) and Australian Synchrotron. Active in global initiatives like ENIGMA-Ataxia for coordinated neuroimaging research.

• Artificial Intelligence
• Machine Learning
• Cloud Computing
• +4 more

Cheng-Hao Cai serves as an Adjunct Lecturer in the Department of Data Science & AI at Monash University. His research focuses on interdisciplinary applications of AI, including cloud computing, software engineering, and industrial process optimization. He collaborates with the Monash Suzhou Research Institute on patents related to smart manufacturing and data-driven methodologies. Key research interests include AI-driven modeling for quality prediction in cokemaking, service-oriented cloud scheduling, and advanced NLP techniques like Chinese named entity recognition. His work integrates deep learning frameworks with domain-specific challenges, such as lattice-transformer-graph models for language processing and automated refactoring of state transition models. Cai has contributed to 23 research outputs since 2014, with recent publications emphasizing practical AI solutions for industry and computational systems. His patents highlight collaboration between academia and industry, particularly in Suzhou.

• Control Theory
• Fault Detection
• State Estimation
• +6 more

Tan Chee Pin is an Associate Professor and Head of the Mechatronics Engineering program at Monash University Malaysia. His research focuses on observer design for system state estimation, with applications in robotics, traffic management, and energy systems. He has published extensively in top journals like IEEE Transactions on Automation Science and Engineering, and secured RM1.5m in research funding. His teaching emphasizes student-centric approaches, earning him internal awards and a national educator award nomination. Research interests include model-based observer configurations for minimizing sensor usage, condition monitoring, and predictive maintenance. Applications span soft robotics, traffic congestion reduction, and energy optimization. He collaborates on projects like IoT-based predictive maintenance systems and solar technology for Orang Asli communities. Editorial roles include associate editorships at IEEE Transactions on Cybernetics and the Journal of the Franklin Institute. Awards include finalist status for the 2018 National Outstanding Educator’s Award. Advising includes supervising 7 PhD completions and 13 current students. Projects include smart drone trash monitoring and control systems for soft robots using few-shot learning.

• Computer Vision
• Robotics
• Remote Sensing
• +3 more

Boying Li is a Research Fellow in the Department of Data Science & AI at Monash University. Their research focuses on advancing computer vision, robotics, and remote sensing technologies. Key contributions include developing SLAM algorithms using semantic planar text features, self-supervised depth estimation systems, and SAR datasets for ship interpretation. Research interests span neuro-symbolic AI frameworks, autonomous navigation, and sensor data fusion. Their work contributes to the UN Sustainable Development Goals through applications in maritime surveillance and autonomous systems. Collaborations involve structural regularities in indoor environments and satellite imagery analysis. Notable outputs include the OpenSARShip dataset (2017–2018) and recent advancements in Hier-SLAM++ (2025). Awards and grants are not explicitly listed in available texts. No lab affiliations or future works are detailed.

Ye LuView profile

Associate Professor

• Structural Health Monitoring
• Smart Materials and Structures
• Advanced Signal Processing
• +5 more

Ye Lu is an Associate Professor in Structural Engineering at Monash University's Department of Civil and Environmental Engineering, leading the Civil Master Program. He holds a PhD in Mechanical Engineering from the University of Sydney (2007) and has been awarded an Australian Research Council (ARC) Discovery Project and ARC Australian Postdoctoral Fellowship (2008). His research focuses on structural health monitoring, smart materials, and artificial intelligence applications in civil engineering infrastructure. Dr. Lu leads projects such as the ARC Research Hub for Smart Next Generation Transport Pavements and the Non-contact Integrity Assessment of High-rise Building Facades. Education: PhD in Mechanical Engineering, University of Sydney, Australia (2007) Research Interests: Structural health monitoring Smart materials and structures Advanced signal processing and AI Infrastructure durability Projects: SPARC: ARC Research Hub for Smart Transport Pavements (2019–2025) Non-contact Façade Panel Integrity Assessment (2020–2025) Smart Sensing for Construction Monitoring (2019–2024) Awards: ARC Australian Postdoctoral Fellowship (2008) ARC Discovery Project Grant (2008) Teaching & Advising: PhD supervision in structural engineering and smart materials Courses: CIV3294, CIV5135 (Advanced Structural Design), CIV5887 (Infrastructure Monitoring) Labs/Teams: Leader of the Civil Master Program, collaborator in interdisciplinary projects across Monash and industry partners like Transurban and EON Reality.

Sandra RodriguesView profile

Research Fellow

• Coal Petrology
• Carbon Materials
• Geochemistry
• +4 more

Dr Sandra Rodrigues is an Honorary Senior Research Fellow at the School of the Environment, University of Queensland. Her research focuses on coal petrology, carbon materials, and geochemistry, with emphasis on thermal maturation, organic geochemistry, and coal seam gas potential. She has authored over 60 peer-reviewed articles, contributing to global understanding of coal structure, hydrocarbon generation pathways, and applications in energy and environmental sectors. Her work spans coal characterization via hyperspectral imaging, coking behavior analysis, and high-temperature material transformation studies. Key projects include investigations into the Toolebuc Formation’s unconventional gas potential and thermal evolution in Mozambique’s Moatize Basin. Rodrigues collaborates internationally on coal classification systems (e.g., ICCP Working Groups) and has pioneered deep learning applications in coal reflectance measurement. Recent research highlights include studies on lithium-enriched coals in China, Jurassic fluid events in Australian basins, and carbon isotope evidence of methane clathrate release during deglaciation. Her interdisciplinary approach bridges geology, chemistry, and engineering, addressing challenges in resource characterization, environmental management, and energy sustainability.

• Medical Imaging
• MRI Technology
• Superconductivity
• +5 more

Professor Feng Liu is a faculty member at the University of Queensland's School of Electrical Engineering and Computer Science. His expertise spans cutting-edge research in medical imaging technologies, superconductivity applications, and biomedical engineering. He specializes in MRI system design, including cryogen-free superconducting magnet development and advanced imaging techniques like quantitative susceptibility mapping. His work integrates artificial intelligence and deep learning to enhance medical diagnostics and imaging precision. Liu has authored over 500 publications, focusing on topics such as magnetic resonance fingerprinting, sensor technology for energy storage, and genetic studies in population biology. His contributions bridge electrical engineering, physics, and healthcare, with a strong emphasis on translating research into practical medical solutions.

• Geospatial Information Systems
• 3D GIS
• BIM/GIS Integration
• +7 more

Professor Sisi Zlatanova is a Professor at the University of New South Wales (UNSW), affiliated with the School of Arts, Design & Architecture. She holds a PhD in 3D GIS for Urban Development from Graz University of Technology and has held academic positions globally, including at ITC (Netherlands) and Delft University of Technology. Her research focuses on 3D spatial modeling, BIM/GIS integration, emergency response systems, and indoor navigation. Key projects include UNSW precinct modeling, indoor scanning for emergency response, and voxel-based visibility analysis for safety. Her research activities span over 300 publications and 23 edited books, with notable works on 3D GIS applications and disaster management. She leads international initiatives like ISPRS TC IV (Spatial Information Science) and OGC SWG IndoorGML. Her work emphasizes practical applications in urban resilience, sustainable design, and crisis management. Professor Zlatanova's contributions include developing frameworks for smart indoor models, integrating BIM with GIS for emergency response, and leveraging LiDAR and UAV technologies for urban analysis. She collaborates extensively with global organizations, advancing geospatial solutions for urban challenges.

Greg StuartView profile

Professor

• Neuroscience
• Dendritic Integration
• Neuronal Signaling
• +2 more

Professor Greg Stuart leads the Neuronal Signalling Group at Australian National University's John Curtin School of Medical Research. His research investigates information processing in cortical neurons using electrophysiological and imaging techniques to study dendritic integration mechanisms. With over 90 publications on dendritic signaling and neural computation, his work combines experimental approaches with computational modeling to understand neuronal function in learning, visual processing, and decision-making.

Chengxin JiangView profile

Research Fellow

• Seismology
• Tectonic Processes
• Magmatic Systems
• +3 more

Dr. Chengxin Jiang is an ARC DECRA Research Fellow in Geophysics at the Research School of Earth Sciences (RSES), ANU. He holds affiliations with Harvard University and the University of Tokyo, where he has conducted visiting research. His work focuses on seismology, particularly tectonic and magmatic processes, employing techniques like ambient noise interferometry and seismic tomography. Education: Ph.D. in Geophysics, Macquarie University (2012–2016) M.Sc. in Geology, China University of Geosciences (2010–2012) B.Sc. in Geology (Honor Class), China University of Geosciences (2006–2010) Research Interests: Jiang’s research explores shallow Earth imaging, volcanic and groundwater monitoring, lithosphere deformation, and machine learning applications in geophysics. He develops novel techniques for seismic detection and utilizes dense seismic arrays to study subsurface structures. Recent projects include imaging Zealandia’s lithosphere and monitoring groundwater variations in Australia’s Great Artesian Basin. Teaching: Leads courses in Structural Geology and Tectonics (EMSC3002), Special Topics (EMSC3050), and Research Proposal and Presentation (EMSC8032) at ANU. Advising & Grants: Supervises multiple PhD, Master’s, and Honors students. Leads projects funded by ARC DECRA and industry partnerships, focusing on energy resource exploration and environmental monitoring. Labs & Collaborations: Collaborates with international teams on seismic imaging and hosts datasets like the Eyre Peninsula Nodal Array (DOI:10.7914/ac1p-7y35). Active in editorial roles, including Journal of Earth, Planets and Space .

• Machine Learning
• Planning with Machine Learning
• Weakly Supervised Learning
• +3 more

Yingbin Bai is a Research Fellow at Australian National University's School of Computing, collaborating with Professor Sylvie Thiebaux. His research focuses on machine learning applications in planning, weakly supervised learning, and unsupervised representation learning. He holds a PhD supervised by Professors Tongliang Liu and Dadong Wang. His work emphasizes addressing challenges in noisy label environments, early stopping techniques, and biomedical image analysis. Key contributions include QUBIQ (uncertainty quantification in medical imaging) and frameworks for robust representation learning under imperfect data conditions. Publications span topics like symmetry breaking in learning systems, semantic shift mitigation in self-supervised learning, and analysis of biomedical image competition practices. Collaborations involve cross-disciplinary teams tackling both theoretical and applied machine learning problems.