جست‌وجوی استاد و پژوهشگر

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

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

Kalin Stefanovمشاهده پروفایل

پژوهشگر ارشد

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

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

Jeffrey Chanمشاهده پروفایل

استاد

• Machine Learning
• Recommender Systems
• Responsible AI
• +۶ مورد دیگر

Jeffrey Chan is a Professor and Associate Dean of Data Science & Artificial Intelligence at RMIT University's School of Computing Technologies. His research focuses on machine learning, recommender systems, responsible AI, and interdisciplinary applications in sustainability, healthcare, and transportation. He has held roles such as Assistant Associate Dean and led projects on fairness-aware systems, energy-efficient recommendations, and AI-driven circular economies. His work bridges theory and practice, collaborating with industry and non-profits. He teaches courses in algorithms, network analytics, and machine learning, and actively supervises PhD and Honours students. Notable contributions include grants from the Australian Research Council and a Best Paper Award at ACM International Conference on Web Science 2011. His research also addresses ethical challenges in AI, such as fairness and transparency, and explores mobility solutions like e-scooter dynamics and parking optimization. Research Interests: Machine Learning, Recommender Systems, Data-Driven Optimization, Social Network Analysis, Responsible AI, Cloud Security, and Sustainability. Awards: Best Paper Award at ACM International Conference on Web Science 2011 Grants: Australian Research Council Discovery Project (2017–2019), Early Career Researcher Grant (2015), and EU FP7 ROBUST project (2011–2013). Labs/Teams: Leads initiatives in AI ethics, mobility systems, and interdisciplinary data science collaborations.

Asif Karimمشاهده پروفایل

مدرس

• Machine Learning
• Health Informatics
• Blockchain Applications
• +۷ مورد دیگر

Dr. Asif Karim is a Research Active Lecturer in the Department of Information Technology at the Faculty of Science and Technology, Charles Darwin University, Australia. He has been a full-time lecturer since August 2021, following a sessional role from 2018 to 2021. Prior to his current position, he served as a lecturer at Daffodil International University and Uttara University in Bangladesh. His research focuses on the application of machine intelligence in health informatics and blockchain technologies. He has significant industry experience in Software Engineering and actively supervises postgraduate research students. Machine Learning Health Informatics Blockchain Applications Smart Contracts Deep Learning Anomaly Detection The recent publications of Dr. Karim span a diverse range of applications in artificial intelligence, particularly in healthcare and secure computing. His work includes developing efficient deep learning models for medical diagnosis, privacy-preserving classification of diseases from medical images, and anomaly detection in cybersecurity. He also explores applications in mobile cloud computing and agricultural technology, demonstrating a broad interdisciplinary approach to solving real-world problems using machine learning. Dr. Karim actively contributes to research projects and supervises postgraduate students. He led the project "Machine Learning Diagnostics System for Bronchiectasis" and has extensive experience in teaching undergraduate and postgraduate courses in computer science, including Machine Learning, Operating Systems, and Software Engineering. He is involved in organizing academic events, such as a digital awareness workshop for rural indigenous communities, reflecting his commitment to community engagement and technology outreach.

Leibo Liuمشاهده پروفایل

پژوهشگر ارشد

• health data science
• artificial intelligence
• machine learning
• +۳ مورد دیگر

Leibo Liu is a Research Fellow at the Centre for Big Data Research in Health (CBDRH) at the University of New South Wales (UNSW) Sydney and a Data Scientist at The George Institute for Global Health (TGI). Education: PhD in Big Data Research in Health from Centre for Big Data Research in Health (CBDRH), University of New South Wales (UNSW), Australia Research Interests: Liu specializes in applying natural language processing (NLP) and deep learning to extract information from unstructured health data, particularly clinical notes. His key research areas include de-identification and clinical coding of free-text data from electronic medical records (EMRs), and predictive modeling using brain imaging data with convolutional neural networks and transformer-based architectures. He developed an end-to-end deep learning framework for removing personally identifiable information (PII) from health data, implemented in the Cardiac Analytics and Innovation (CardiacAI) project. Current Projects: Next-generation Clinical Registry project at CBDRH Outcome PredicTion in IntraCerebral Hemorrhage Study (OPTICS) at TGI (leads machine learning model development) Professional Background: With expertise spanning machine learning, software engineering, and big data analysis, Liu brings a unique combination of skills to health data science. His publication record includes 1 Book Chapter, 9 Journal articles, 1 Conference Paper, 1 Conference Abstract, and 6 Preprints.

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Dr. Alan Blair is a Senior Lecturer at the School of Computer Science and Engineering, University of New South Wales. His research spans artificial intelligence, machine learning, and neural networks, with applications in medical imaging, evolutionary computation, and cybersecurity. Key research areas: Artificial Intelligence, Machine Learning, Neural Networks, Evolutionary Computation, Medical Imaging, Computational Linguistics Recent publications focus on reinforcement learning for medical diagnosis, dialogue act tagging, and phishing detection using feature-free tools. Collaborations include work with Sowmya A, Iyer S, and Dawes L on 3D organ localization and fracture detection in CT scans. Blair's work integrates hybrid neural-logical reasoning models for visual question answering and hierarchical evolutionary algorithms for optimization tasks. He has contributed to adversarial image generation and game theory applications.

Helia Farhoodمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Machine Learning
• Image Processing
• +۳ مورد دیگر

Dr. Helia Farhood is an Honorary Senior Research Fellow at the School of Computing, Macquarie University, specializing in Artificial Intelligence, Machine Learning, and Image Processing with applications in educational technology and object recognition. Her academic qualifications include a PhD in Computer Systems and Artificial Intelligence from the University of Technology Sydney (awarded November 2021) and a Master's degree in Computer-AI from Amirkabir University of Technology (Tehran Polytechnic, awarded September 2013). Dr. Farhood's research spans interdisciplinary AI applications, with significant contributions in student outcome prediction using generative adversarial networks, explainable AI through LIME heatmaps, and image-based storytelling systems. Her work integrates machine learning with educational data mining to enhance creativity assessment and learning analytics, while maintaining strong technical focus on 3D reconstruction and object recognition. Analysis of her 16 publications (2020-2025) reveals three dominant research trajectories: (1) AI-driven educational analytics for student performance prediction, (2) advanced image processing techniques for object recognition and 3D reconstruction, and (3) systematic reviews establishing methodological foundations in presentation attack detection and image-based storytelling. Her recent work increasingly emphasizes explainability and ethical considerations in AI deployment. Dr. Farhood has participated in externally funded research projects, including the 2022 project "Estimating the Number of Tyres in Stockpiles" (October-December 2022). No information is available regarding students she has advised. No information is available about specific research laboratories or teams led by Dr. Farhood.

Eric Howardمشاهده پروفایل

پژوهشگر ارشد

• Quantum Physics
• Machine Learning
• Gravitational Waves
• +۴ مورد دیگر

Dr. Eric Howard is a Research Fellow at Macquarie University , affiliated with the School of Engineering , School of Mathematical and Physical Sciences , and School of Computing . His research spans interdisciplinary domains at the intersection of quantum physics, machine learning, and AI-driven systems. Key research themes include: Quantum cryptography for Industry 4.0 security Machine learning in IoT temperature sensing Adversarial AI in cybersecurity 6G wireless communication optimization Quantum information processing Deep learning for data imputation Recent publications demonstrate a focus on emerging technologies, with articles on quantum Bayesian inference , 6G signal processing , and smart city IoT systems . His collaborative work extends to blockchain-enabled supply chain visibility and generative AI applications in programming. Research collaborations span institutions in India (AIP Publishing) and Australia, with technical contributions to quantum dynamics, neural network applications, and nanosensor development.

• Cognitive Neuroscience
• Autism Spectrum Disorder
• Substance Use Disorders
• +۵ مورد دیگر

Dr Natalia Albein-Urios is a Visiting Professor at Deakin University's School of Psychology , affiliated with the SEED - Lifespan strategic research center. Her work bridges Cognitive Neuroscience and Clinical Psychology , focusing on Autism Spectrum Disorder (ASD) and Substance Use Disorders (SUD) via Non-invasive Brain Stimulation (NIBS) , fMRI , and EEG . Education : PhD in Neuropsychology (Summa Cum Laude, University of Granada, 2012), Master in Health Psychology (University Miguel Hernandez, 2007), International Master in Clinical Psychology (Spanish Association of Behavioural Psychology, 2009), and BAppSc (Hons) in Psychology (University of Miguel Hernandez, 2007). Research Interests center on: Neurocognitive Mechanisms in ASD and SUD Non-invasive Brain Stimulation (HD-tDCS, TMS, TBS) Neuroimaging (fMRI, EEG) and Behavioral Interventions Emotion Regulation and Executive Dysfunction Her Recent Publications highlight advancements in: Transdiagnostic Approaches to mental health via computational modeling Brain Stimulation Efficacy in ASD and addiction Neuroimaging Biomarkers for dual disorders Scientific Awards include: Best Thesis Prize in Health Sciences, University of Granada (2016) Health Research Postdoctoral Fellowship , Deakin University (2015-2017) First Prizes for Best Poster (ECNP 2018) and Oral Presentation (ICDBMD 2011) Supervision spans 14 Honours Students since 2015 and PhD students like Gillian Clark (2017) and Dinisha Parmar (2021). She has secured grants such as the Research Development Grant (Deakin University, 2014). Her work contributes to the SEED - Lifespan team's mission in translational neuroscience.

• Geotechnical Engineering
• Transport Engineering
• Geo-environmental Engineering
• +۵ مورد دیگر

Professor Hadi Khabbaz is a distinguished academic at the School of Civil and Environmental Engineering, University of Technology Sydney (UTS), where he has served since 2008. Holding a PhD in Geotechnical Engineering from UNSW (1997), MSc in Geo-environmental Engineering, and BSc (Honours) in Civil Engineering from Shiraz University, he serves as Head of Discipline for Geotechnical & Transport Engineering and Deputy Head of School (Research). Current academic appointment: Professor at UTS Key roles: Head of Discipline, Deputy Head of School (Research) Professional memberships: Chair of Australian Geomechanics Society (Sydney) His research program focuses on unsaturated soil mechanics , ground improvement techniques , and railway geomechanics , with particular emphasis on soil behavior modeling and problematic soils . Recent work explores machine learning applications in geotechnical analysis and smart infrastructure development . Article trends reveal expertise in geotechnical modeling using advanced computational methods, recycled material applications for sustainable infrastructure, and machine learning integration in geotechnical engineering. His work spans landfill mechanics , pile foundation analysis , and environmental geotechnics . Scientific recognition includes 1999 Joint Section Award for outstanding paper Three best paper awards with colleagues and students As an academic leader, he supervises research students and has developed courses like Advanced Soil Mechanics and Foundation Design and Geotechnical Aspects of Landfill Design . His funded research portfolio includes projects with ARC Discovery Grants and industry partnerships addressing infrastructure challenges.

• Catchment Modelling
• Urban Hydrology
• Climate Change Impact on Flood Estimation
• +۳ مورد دیگر

James Ball is an Adjunct Professor at the School of Civil and Environmental Engineering , University of Technology Sydney. With a career spanning academia, consulting, and government roles, his expertise focuses on catchment modeling , urban drainage systems , and climate change impacts on hydrology. He has held positions at UNSW Australia and contributed to foundational guidelines like Australian Rainfall and Runoff . Education: Doctor of Philosophy, Master of Engineering, and Bachelor of Civil Engineering from the University of Newcastle, Australia. Research Interests: Development of catchment modeling systems for urban environments, integration of deep learning in land use/land cover classification, hydrological software calibration with genetic algorithms, and stormwater-green infrastructure interactions such as green roofs. Recent Trends: His 2024–2021 publications emphasize agent-based groundwater-society interactions , deep learning for impervious surface estimation , and hybrid machine learning methods for dam reservoir evaporation under climate change scenarios. Articles also address flood modeling accuracy , scour mitigation structures for bridges, and socio-hydrology in resource conflicts. Leadership: Editor-in-Chief of the International Journal of River Basin Management , editorial roles in Urban Water Journal , Hydrology , and Water Science and Technology . Past Vice-President of IAHR and technical editor for Australian Rainfall and Runoff . Supervision: Available for Masters and PhD student supervision . Funded Research: Projects include stormwater modeling , hydrological ensemble prediction , phosphorus release studies , and stochastic rainfall generation for flood estimation.

Wen Huمشاهده پروفایل

استاد

• Cyber-physical systems
• Internet of Things
• Sensor network systems
• +۳ مورد دیگر

Professor Wen Hu is a distinguished academic at the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he holds a professorship focusing on cutting-edge research in cyber-physical systems and the Internet of Things (IoT). With extensive experience in both academia and industry, Professor Hu has established himself as a leading researcher in sensor network systems, low-power communications, security, and compressive sensing. His work bridges theoretical research with practical applications, as evidenced by his active commercialization efforts through roles as Chief Technology Officer at Parking Spotz and former Chief Scientist at WBS Tech. Professor Hu's research interests span multiple domains within cyber-physical systems and IoT. His work particularly focuses on low-power wireless communications , sensor network security , compressive sensing techniques , and novel applications of IoT technologies . His research group explores how these technologies can be applied to solve real-world problems in areas such as smart buildings, environmental monitoring, and human-computer interaction. The research integrates hardware design, communication protocols, security mechanisms, and data analytics to create efficient and robust IoT systems. Professor Hu's publication record demonstrates a consistent focus on advancing the state-of-the-art in wireless sensor networks and IoT. His recent work shows increasing integration of machine learning techniques with traditional sensor systems, exploring applications in areas ranging from healthcare monitoring to agricultural technology. There's a clear trend toward more sophisticated signal processing approaches, particularly using mmWave technology and visible light communications, while maintaining the core focus on energy efficiency and security that has characterized his earlier work. CSIRO Office of Chief Executive (OCE) Julius Career Award (2012-2015) Professor Hu has successfully secured multiple research grants from the Australian Research Council, CSIRO, and industry partners, enabling his team to pursue ambitious research projects with both theoretical significance and practical applications. His editorial leadership as Editor-in-Chief of ACM TOSN and organizational roles in major conferences like CPS-IoT Week 2020 and ACM/IEEE IPSN 2023 demonstrate his significant contributions to the research community. As a senior member of both ACM and IEEE, he actively mentors students and early-career researchers while fostering collaborations across academia and industry. Professor Hu leads a vibrant research group at UNSW that combines theoretical research with practical implementation. His team works closely with industry partners to ensure research relevance and facilitate technology transfer. The group maintains strong connections with international research communities and participates in collaborative projects that address global challenges in IoT and cyber-physical systems.

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

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

• Machine Learning
• Big Data Analytics
• Computational Intelligence
• +۴ مورد دیگر

Vahid Behbood is a Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He belongs to the Decision Systems and e-Service Intelligence Research Laboratory in the Centre for Quantum Computation and Intelligent Systems. Ph.D. in Software Engineering from UTS (2014) His research spans machine learning , big data analytics , computational intelligence , and transfer learning , with a focus on addressing data shortage challenges through fuzzy systems and domain adaptation. His work includes: Advancing fuzzy regression domain adaptation for cross-domain prediction Developing granular computing techniques for financial failure prediction Exploring SQL query error patterns in computer science education Designing IoT architectural frameworks via systematic reviews Publications demonstrate expertise in neural networks , fuzzy logic , Bayesian methods , and financial data analytics . He applies these techniques to banking ecosystems, real estate valuation, and educational data mining.

• Machine Learning
• Computer Vision
• 3D Geometry
• +۲ مورد دیگر

Dr. Jun Li is a Senior Lecturer at the School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Australia. He received his Ph.D. in Computer Science from Queen Mary University of London in 2009 and is affiliated with the Australian Artificial Intelligence Institute (AAII) at UTS. His research spans multiple domains within artificial intelligence, with primary focus on Machine Learning applications in computer vision and 3D geometry. Dr. Li has published extensively in high-impact journals including IEEE Transactions (TPAMI, TIP, TNNSLS) and Pattern Recognition, with recent work expanding into interdisciplinary research in earth science and marine applications. His research output demonstrates consistent productivity with numerous publications each year across diverse AI application areas. Dr. Li's work shows strong thematic progression from foundational computer vision techniques to applied interdisciplinary research. Early work focused on face hallucination and video super-resolution, while more recent publications address environmental applications using Graph Neural Networks for wave prediction and damage classification for disaster response. His research consistently bridges theoretical AI advances with practical real-world applications across healthcare, autonomous systems, and environmental science. AI to assist disaster emergency response (2023-2026) Applying Generative Adversarial Network in Medical Image Analysis (2020-2021) Big Massive Open Online Course (MOOC) Data Retrieval (2017-2020) As an educator, Dr. Li teaches core courses including '31005 Machine Learning' and '32513 Advanced Data Analytics Algorithms' at UTS, and is available for Masters Research and PhD student supervision, contributing to the development of next-generation AI researchers.