جست‌وجوی استاد و پژوهشگر

• Data Science
• Healthcare
• Agriculture
• +۵ مورد دیگر

Dr. Nasim Ahmed is a Lecturer at the School of Computer Science, The University of Sydney. His research bridges data science, artificial intelligence, and healthcare, with applications in medical diagnostics, agriculture, and data security. He earned a Ph.D. in Computer Science from Massey University (2022) and an MSc in Computer Engineering from University Malaysia Perlis (2009). Data science applications in healthcare and agriculture Big data clusters and performance modeling AI in education policy and industry applications Network security and optical communication systems His recent publications focus on federated learning for lung cancer detection, deep learning for melanoma prediction, and machine learning for chronic kidney disease risk assessment. Articles also explore big data job runtime prediction, optical CDMA/WDM systems, and Spark/Hadoop performance optimization. Nasim Ahmed holds professional memberships, including Senior IEEE and IET, and is a Chartered Engineer (CEng) accredited by the UK Engineering Council.

• Computer Science
• Event-Based Vision
• Light Field Image Processing
• +۵ مورد دیگر

Vera Chung is a Senior Lecturer at the School of Computer Science , The University of Sydney. Her research focuses on Computer Science , particularly in Event-Based Vision , Light Field Image Processing , Deep Learning Applications , and 3D Reconstruction . She leads projects in Image Quality Assessment and Haptic Feedback Systems .

• Neuroimaging
• Medical Imaging
• Data Analysis
• +۳ مورد دیگر

Dr. Thomas Close is a National Imaging Facility Informatics Fellow at the School of Biomedical Engineering, University of Sydney . His work focuses on neuroimaging analysis, computational neuroscience, and developing reproducible research tools. He holds a PhD and has contributed to advancements in medical imaging technologies like MRI/PET motion correction and multimodal data integration. Education: PhD (details not specified). Affiliations: National Imaging Facility, School of Biomedical Engineering, and collaborations with institutions like Monash University. Research Interests: Dr. Close specializes in neuroimaging methodologies, including motion correction algorithms, multimodal imaging (fMRI/PET), and open-source software frameworks for reproducible neuroscience. His work bridges computational techniques and clinical applications, with a focus on cerebellar function and neurodegenerative diseases such as Friedreich ataxia. Publications: His recent research includes Neurodesk (an open science tool for neuroimaging), cortico-cerebellar architecture studies, and advancements in diffusion MRI tractography. His work emphasizes reproducibility, open science practices, and collaborative platforms like Brainhack. Grants/Advising: No specific grants or advisees listed in the provided texts. However, his research collaborations suggest engagement with interdisciplinary teams. Labs/Teams: Associated with the National Imaging Facility, where he contributes to informatics initiatives, and the University of Sydney’s Biomedical Engineering labs focusing on neuroimaging technologies.

• Structural Engineering
• Material Mechanics
• Computational Mechanics
• +۵ مورد دیگر

Associate Professor Daniel Dias-da-Costa is a faculty member in the School of Civil Engineering at the University of Sydney. He holds a BS in Civil Engineering from the University of Porto and MSc/PhD from the University of Coimbra. His research focuses on computational mechanics, structural engineering, and material behavior, with a particular emphasis on fracture simulation, image processing for structural assessment, and sustainable construction materials. He has been affiliated with The Net Zero Institute and The University of Sydney Nano Institute. His research interests include advancing computational frameworks to bridge simulation and monitoring techniques, enhancing structural safety through predictive modeling of material failure (e.g., concrete cracking), and experimental testing of large-scale structures. He has developed novel algorithms and finite element methods to address complex nonlinear problems, such as crack propagation and stiff interface challenges. Dr. Dias-da-Costa has supervised current PhD students focusing on topics like 3D-printed concrete, microbial self-healing materials, and granular material dynamics. He has received awards for teaching excellence (2015–2016) and research contributions (Young Researcher Award in APMTAC, 2015). His work spans interdisciplinary collaborations, integrating AI, machine learning, and advanced materials to address modern infrastructure challenges. Key research outputs include contributions to energy-efficient concrete, fracture mechanics, and structural health monitoring technologies.

• Health Informatics
• Digital Health
• Artificial Intelligence in Healthcare
• +۳ مورد دیگر

Dr. Simon Poon is an Associate Professor at the University of Sydney with a cross-disciplinary focus at the intersection of Health Informatics , Computer Science , and Traditional Chinese Medicine (TCM) data analytics . He holds multiple academic roles including Honorary Associate at the University of Sydney Business School and Adjunct Associate Professor at Chinese University of Hong Kong (CUHK) in the Institute of Integrative Medicine. Co-founder and Director (2014-2019) of Master of Health Technology Innovation (MHTI) program Member of Charles Perkins Centre , Sydney Centre for Healthy Societies , and China Studies Centre International collaborations with institutions in Hong Kong, UK, and USA His research focuses on digital health evaluation and AI development for medical applications, including: Machine learning for stroke risk prediction through blood pressure variability Fall prevention systems via footstep pattern classification Electronic medication management system implementation Integrative medicine data analytics Health technology evaluation frameworks Recent publications show strong emphasis on AI in radiology , clinical process mining , and healthcare workflow optimization . His work spans both technical algorithm development and organizational implementation challenges. Key affiliations include: University of Sydney (School of Computer Science) Chinese University of Hong Kong (Adjunct Associate Professor) NSW Local Health Districts (Honorary Associate) He has supervised students across Computer Science , Biomedical Engineering , Medicine , and Business disciplines.

• Parallel and Distributed Computing
• Networking
• Complex Systems
• +۵ مورد دیگر

Albert Zomaya is the Peter Nicol Russell Chair Professor of Computer Science and Director of the Centre for Distributed and High-Performance Computing at The University of Sydney. He holds a PhD and is a Fellow of multiple prestigious institutions including the Australian Academy of Science and the Royal Society of New South Wales. His research focuses on parallel and distributed computing, networking, and complex systems, with applications in cybersecurity, cloud computing, and IoT. He has authored over 700 publications, 30 books, and edited numerous journals. His awards include the ACM MSWIM Reginald A. Fessenden Award and the New South Wales Premier’s Prize for Engineering. Education: PhD, along with extensive academic and industry experience across multiple institutions including the University of Western Australia and the University of Sydney. Research interests span distributed computing, high-performance systems, energy-efficient scheduling, and bioinformatics applications. His work impacts national security, healthcare, and industrial productivity through advancements in computing technologies. Awards highlight his contributions to scalable computing, parallel processing, and sustainable computing. He serves on editorial boards of major journals like ACM Computing Surveys and IEEE Transactions on Computers. Advising and grants include mentoring numerous PhD students and leading projects like the ARC Professorial Fellowship on energy-aware scheduling. He directs research centers and collaborates with industry partners to translate theoretical advancements into practical solutions. Labs and teams: Leads the Centre for Distributed and High Performance Computing, and collaborates with institutions like the Net Zero Institute and the University of Sydney Nano Institute.

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)

Stephen Gouldمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• Machine Learning
• +۳ مورد دیگر

Stephen Gould is a Professor of Computer Science at the Australian National University (ANU) and an Australian Research Council (ARC) Future Fellow. He holds affiliations with Amazon as an Amazon Scholar and previously led the ARC Centre of Excellence in Robotic Vision. His research focuses on computer and robotic vision, machine learning, and optimization, with a particular emphasis on applying these techniques to geometric and semantic scene understanding. Education: BSc in Mathematics and Computer Science (University of Sydney, 1994), BE in Electrical Engineering (University of Sydney, 1996), MS in Electrical Engineering (Stanford University, 1998), and PhD in Computer Science (Stanford University, 2010). Research interests include deep learning, structured prediction, and optimization. His work bridges theoretical advancements with practical applications, such as industry collaborations and start-up ventures. Recent trends in his publications emphasize vision-language models, 3D shape modeling, and neural networks for robotics. Key awards: ARC Future Fellowship, Microsoft Faculty Fellowship, and leadership roles in major research initiatives. He has supervised over 20 students, many of whom hold prominent positions in academia and industry. Grants include collaborations with Seeing Machines (ARC Linkage grant) and industry-sponsored scholarships. His lab, the Australian Centre for Robotic Vision, focuses on advancing robotic vision through interdisciplinary research.

Charles Grettonمشاهده پروفایل

مدرس

• Artificial Intelligence
• Automated Planning
• Automated Reasoning
• +۵ مورد دیگر

Charles Gretton is a Lecturer at the Australian National University (ANU), specifically within the ANU College of Systems & Society. He is an Artificial Intelligence (AI) expert with over 20 years of experience in both academia and industry, including co-founding HIVERY, a CSIRO Data61 spinout. His research focuses on Automated Planning, Automated Reasoning, and Machine Learning, with applications in computer security, astronomical instrumentation, retail optimization, and robotics control. Affiliations: ANU, HIVERY (co-founder), CSIRO Data61 Industry Experience: Successfully applied AI in fast-moving consumer goods logistics, continental-scale retail strategies, and robotics. His teaching roles include delivering core courses in Theoretical Computer Science and AI at ANU since 2013, alongside leading the ANU TechLauncher program (2018–2024), a capstone initiative fostering industry collaboration with early-career professionals. His research spans cutting-edge topics like physics-informed neural networks for plasma physics, AI in financial markets, and adaptive optics systems for astronomy. Charles has supervised notable students including Patrick Liston (financial agent-based simulations), Xiaodi Zhang (probabilistic planning), and Jeffrey Smith (astronomical instrumentation AI). Key collaborations involve instrumentation scientists like Damien Gratadour and Jesse Cranney. His work bridges theoretical AI with real-world applications, emphasizing human-centric technology. Notable projects include the Dagster parallel search tool, verified AI planning algorithms, and AI-driven solutions for policing and retail. His contributions to planning theory include formally verified algorithms and compositional methods for problem-solving.

Ian McAllisterمشاهده پروفایل

استاد

• Political Science
• Elections and Voting Behavior
• Party Systems
• +۴ مورد دیگر

Professor Ian McAllister is a Distinguished Professor of Political Science at The Australian National University (ANU), where he has been affiliated since 1997. Previously, he held chairs at the University of New South Wales and the University of Manchester, and served as Director of ANU's Research School of Social Sciences (1997–2004). He has also been a key figure in academic leadership, including roles as President of the British Politics Group and editor of the Australian Journal of Political Science. His research focuses on elections, voting behavior, party systems, and democracy, with a particular emphasis on postcommunist societies and comparative politics. Education: Bachelor of Humanities, University of Greenwich (1972) Master of Statistics, University of Strathclyde (1974) PhD in Political Science, University of Strathclyde (1976) Research Interests: Elections and voting behavior Party systems and political competition Democratic transitions and public opinion Comparative politics, particularly in postcommunist societies and Asia His work bridges theoretical and empirical analysis, with a focus on how generational changes, institutional frameworks, and political leadership shape democratic processes. Key Contributions: Leadership of the Comparative Study of Electoral Systems (2003–2008) Authorship of over 340 publications, including seminal works on electoral behavior and democratic governance Recipient of prestigious fellowships, including from the Academy of the Social Sciences in Australia and the Royal Society of Edinburgh Recent Research Trends: Professor McAllister’s recent work examines democratic transitions in Asia, the impact of the pandemic on governance perceptions, and the role of political leadership in Australia. His analyses often highlight the interplay between structural factors (e.g., generational shifts) and individual-level attitudes. Grants & Projects: "Political Trust and Satisfaction with Democracy in Australia" (2021–2026) "Political Engagement Among the Young: The 2016–19 Australian Election Study" Labs & Collaborations: He is affiliated with ANU’s School of Politics & International Relations and contributes to interdisciplinary projects through the Australian Data Archive. His collaborations span global networks, including studies on post-Soviet politics and comparative electoral systems.

Supriya Supriyaمشاهده پروفایل

مدرس

• Health Informatics
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Supriya Supriya is a Lecturer in the Department of Design and Creative Technology at Torrens University Australia, affiliated with the Centre for Artificial Intelligence Research and Optimisation (AIRO). She holds a Ph.D. in Information and Mathematical Science from Victoria University Melbourne (2020) and master's and bachelor's degrees in Computer Science and Information Technology from Punjab Technical University, India (2014 and 2010). Her research focuses on health informatics, biomedical signal analysis (particularly EEG signals), machine learning, and artificial intelligence. Key areas include癫痫 detection, sleep stage classification, and the application of complex networks and graph-based techniques for medical diagnostics. She has also contributed to deep web data extraction and classification. Supriya has published extensively in reputable journals like IEEE Transactions and conferences, with notable works on EEG-based epilepsy detection and complex network frameworks. Her research aligns with UN Sustainable Development Goals related to education and innovation. She currently supervises PhD students in real-time biomedical signal analysis systems. Her technical expertise includes programming in MATLAB, C++, C#, and Python, and she emphasizes empowering women in STEM fields.

• Machine Learning
• Health Informatics
• Cybersecurity
• +۵ مورد دیگر

Prof Xujuan Zhou is a Professor of Information Systems at the School of Business, University of Southern Queensland. She is affiliated with the Centre for Heritage and Culture and the Centre for Health Research. Her work focuses on interdisciplinary applications of machine learning and AI in healthcare, cybersecurity, and agricultural systems. She holds qualifications including a BIT (Honours) and PhD from Queensland University of Technology (QUT). Bachelor of IT (BIT), QUT Bachelor of IT (Honours), QUT PhD in IT, QUT Her research interests span AI-driven medical diagnostics (e.g., depression prediction, thyroid care), cybersecurity solutions for 5G networks, blockchain applications in agricultural traceability, and hybrid ML models for fraud detection. She has published extensively on topics like intrusion detection systems, remote patient monitoring, and X-ray-based disease identification using CycleGAN. Her work emphasizes both technical innovation and ethical considerations in AI deployment. Recent publications explore the intersection of social media analysis and mental health monitoring, blockchain for secure recommendation systems, and federated learning for personalized healthcare monitoring. Over 60,000 views and 13,000 downloads of her research outputs highlight the impact of her interdisciplinary work. Her contributions include developing AI tools for sepsis prediction, pain intensity estimation via facial expressions, and automated classification of lung nodules. She has also pioneered work on quantum-inspired algorithms for optimization problems in medical scheduling.

• Artificial Intelligence
• Machine Learning
• Renewable Energy
• +۷ مورد دیگر

Prof. Ravinesh Deo is a Professor of Mathematics at the University of Southern Queensland (USQ), leading the Advanced Data Analytics Research Laboratory with 57+ researchers. He holds a PhD in Signal Processing from the University of Adelaide and has supervised over 40 PhD/Master's students. His research focuses on AI applications in energy systems, national intelligence, healthcare, and environmental science, supported by $16.1M in grants. He is a Clarivate Highly Cited Researcher (Top 1%) and Stanford Top 2% World Scientist. Education: BSc South Pacific University MSc(Hons) Canterbury University PhD Adelaide University Research Interests: Prof. Deo pioneers AI-driven solutions in renewable energy systems, predictive modeling for UV radiation, flood forecasting, and biomedical applications. His work integrates deep learning, evolutionary computation, and big data analytics. Recent trends in his publications emphasize environmental risk assessment, energy market optimization, and healthcare diagnostics through explainable AI frameworks. Grants & Recognition: He leads projects funded by the Australian Department of Defence, Queensland Government, and EU Horizon 20. Awards include Outstanding Researcher (2022/2023), Excellence in Research (2021), and six fellowships including the Endeavour Award. His lab collaborates with 190+ global institutions across five continents. Teaching & Administration: As Postgraduate Program Director for MADS/MSCN programs, he teaches engineering mathematics and supervises research projects. He also chairs editorial boards for IEEE Access, Stochastic Environmental Research, and ASCE journals. Labs & Teams: Directs USQ's Artificial Intelligence Applications Laboratory and Advanced Data Analytics group, focusing on cross-disciplinary innovation in space technologies, climate science, and national security.

Farah Alkhatibمشاهده پروفایل

مدرس

• Biomechanics
• Finite Element Analysis
• Biomedical Engineering
• +۲ مورد دیگر

Farah Alkhatib is a Lecturer and Research Officer in Mechanical Engineering at the University of Western Australia. She specializes in computational biomechanics and finite element analysis of vascular systems. Her research develops patient-specific modeling approaches for abdominal aortic aneurysms, focusing on rupture risk assessment and surgical planning. She won the Graduate Research School Dean's List award for her PhD thesis and has contributed to open datasets for biomechanical research. Dr. Alkhatib's publications demonstrate expertise in mesh generation techniques, uncertainty quantification in medical image segmentation, and nonlinear biomechanical analysis. She develops computational methods to improve the accuracy of vascular stress predictions and translate biomechanical research into clinical applications.

Tianhai Tianمشاهده پروفایل

دانشیار

• Computational Biology
• Computational Statistics
• Multi-scale Modelling
• +۴ مورد دیگر

Tianhai Tian is an Associate Professor at the School of Mathematics, Monash University. He holds a PhD in Computational Mathematics from the University of Queensland (2001), and has held prestigious positions including Australian Research Fellow at ARC, Lord Kelvin Fellow at University of Glasgow, and ARC Future Fellow. His research focuses on mathematical modeling of biological systems, with expertise spanning computational biology, statistical inference, and multi-scale modeling of complex networks. Research Interests: Mathematical modeling of genetic regulatory networks and cell signalling pathways Computational approaches to cancer therapy optimization Stochastic modeling of biochemical systems Network inference from high-dimensional biological data Multi-scale modeling approaches for complex biological systems His publication trends demonstrate strong focus on cancer modeling, computational immunology, and development of novel algorithms for biological data analysis. Recent works increasingly integrate machine learning with dynamical systems modeling. Awards and Honors: Australian Research Fellowship, ARC (2006) Lord Kelvin Fellowship (2007) ARC Future Fellowship (2011) Research Grants: Stochastic modelling of telomere length regulation in ageing research (ARC, 2012-2017) Stochastic modelling of genetic regulatory networks with burst process (ARC, 2011-2016) Multiscale stochastic modelling of tumour robustness (ARC, 2010-2016) Spatio-temporal modelling of Ras dependent MAP kinase activation (ARC, 2007-2011)