جست‌وجوی استاد و پژوهشگر

• Neural Signal Processing
• Machine Learning for Neuroscience
• Brain Network Modeling
• +۵ مورد دیگر

Dr. Quanying Liu serves as Associate Professor in the Department of Biomedical Engineering at Southern University of Science and Technology (SUSTech), where she leads the Neural Computing and Control Laboratory (NCC lab) as Principal Investigator and Doctoral Supervisor. Her academic journey includes a PhD from ETH Zurich in Biomedical Engineering, postdoctoral training at Caltech, and research positions at Huntington Medical Research Institute, KU Leuven, and Oxford University. PhD in Biomedical Engineering, ETH Zurich (2013-2017) Master in Computer Science, Lanzhou University (2010-2013) Bachelor in Electrical Engineering, Lanzhou University (2006-2010) Dr. Liu's research bridges neuroscience, machine learning, and control theory with focus on multi-modal neural signal processing (EEG, sEEG, fMRI, DTI), explainable AI for brain interpretation, and optimization frameworks for neuromodulation (tES, TMS). Her work develops high-density EEG source imaging algorithms, data-driven brain network modeling, and control-theoretic approaches for neural stimulation. The NCC lab specializes in machine learning algorithms, neural computation, multimodal data fusion, network control theory, and bidirectional brain-computer interfaces. Analysis of her recent publications reveals strong trends in EEG-based visual decoding, multimodal neural embeddings, and AI-driven brain network modeling. Her work increasingly integrates generative models for neural data synthesis, control theory for precise neuromodulation, and cross-modal approaches connecting neural signals with cognitive functions. The New Brain 30 (2023) AAIC travel award (2019) Estes Stars Award (2018) Shenzhen Peacock Talent Plan C Dr. Liu actively mentors students through SUSTech's doctoral program and leads multiple significant research initiatives including a National Natural Science Foundation Youth Project, a National Key R&D Program in Bio-Information Fusion, and several Shenzhen municipal projects. Her lab receives funding from Guangdong Provincial Basic Research Fund, Shenzhen Science and Technology Innovation Commission, and international fellowships including Boswell Postdoctoral Fellowship and Swiss National Science Foundation grants. The NCC lab maintains strong international collaborations with Caltech, ETH Zurich, and Oxford University while developing novel platforms like WheelCon for sensorimotor control studies. The Neural Computing and Control Laboratory operates as an interdisciplinary hub integrating computational neuroscience, machine learning, and control engineering. The lab develops specialized hardware-software systems for closed-loop neurostimulation, maintains the EEGdenoiseNET benchmark dataset, and pioneers methods like MOVEA for transcranial electrical stimulation optimization. Current research directions include generative models for neural decoding, network control theory applications, and AI-human cognitive interaction frameworks.

• Artificial Intelligence
• Smart Grids
• Renewable Energy Systems
• +۶ مورد دیگر

Md Morshed Alam is a Part-Time Lecturer at the Faculty of Engineering and Information Technology , University of Technology Sydney. He holds a B.Sc. in Electrical and Electronic Engineering from Khulna University of Engineering and Technology, an ERASMUS MUNDUS certificate in Power Systems from University do Porto, and an M.Sc. in Electronics Engineering from Kookmin University. Focuses on Artificial Intelligence integration in Smart Grids Specializes in Energy Consumption Forecasting and Renewable Energy Optimization Proficient in Python, MATLAB/Simulink , and Arduino His 15 most recent publications analyze topics like AI-driven microgrid control, drone detection systems, and air quality prediction models, with significant citations in energy storage and wireless communication domains. While no scientific awards are explicitly listed, his work spans diverse applications from Terahertz communication to IIoT security frameworks . He contributed to Wireless Communication and AI Lab projects during his time at Kookmin University.

Paul Kennedyمشاهده پروفایل

استاد

• Biomedical Data Analytics
• Bioinformatics
• Machine Learning
• +۴ مورد دیگر

Professor Paul Kennedy is the Head of the School of Computer Science at the University of Technology Sydney (UTS) Faculty of Engineering and Information Technology. He has made significant contributions to data analytics education, receiving a 2013 OLT Citation for Outstanding Contributions to Student Learning and multiple UTS teaching awards. His research focuses on biomedical data analytics, particularly in pediatric cancer treatment outcomes, bioinformatics pipelines for vaccine discovery, and visual analytics for explainable AI in healthcare. His recent publications highlight advancements in multi-criteria decision-making for vaccine candidates, edge-cloud frameworks for omics data, and immersive VR environments for genomic data visualization. He leads the Biomedical Data Science Laboratory at UTS Australian Artificial Intelligence Institute and co-directs the UTS-Queens University AI-driven clinical tools initiative. A key collaborator with the Children’s Hospital at Westmead, he has secured over $2.4 million in research funding and served on international data mining conference committees since 2006. Scientific Awards: 2013 OLT Citation for Outstanding Contributions to Student Learning 2012 UTS Learning and Teaching Award 2020 Team Teaching Award 2019 Team Teaching Citation His methodological innovations span topological data analysis for stress detection, feature-ranking in RNA sequencing, and domain-adaptation techniques in machine learning. He also contributes to global AI ethics standards through ISO/IEC SC42 committee membership.

Murat Tahtaliمشاهده پروفایل

مدرس ارشد

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

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

• Machine Learning
• Data Analysis
• Data Mining
• +۱۲ مورد دیگر

Hua Wang is a Professor at Victoria University, Melbourne, Australia, affiliated with the Institute for Sustainable Industries and Liveable Cities (ISILC) and the Oceania Cyber Security Centre (OCSC). His research spans machine learning, data analysis, cyber security, and privacy-preserving technologies, with applications in healthcare, IoT, and social media analytics. PhD in Computer Science (University of Southern Queensland, 2004) Research focuses on privacy-preserving data publishing , deep learning for cyber security , graph neural networks for IoT , and EEG-based neurological disorder detection . His work integrates federated learning and transformer models for secure, efficient data processing. Recent publications emphasize multi-objective optimization for privacy, adaptive neural networks , and domain-generalized tracking systems . Collaborations include Japan Society for the Promotion of Science (JSPS) and Data61 University Planning Committee. Hua supervises 14 PhD students, serves as ARC College of Expert (2021–present), and leads editorial roles for EAI Transactions on Scalable Information Systems , PLOS ONE , and World Wide Web Journal . He holds 14 grants totaling $3.1M, including ARC Discovery and Linkage projects.

Shu-Kay Angus Ngمشاهده پروفایل

استاد

• Biostatistics
• Statistical Modeling
• Mixture Models
• +۹ مورد دیگر

Professor Shu-Kay Angus Ng is a Professor of Biostatistics at Griffith University's School of Medicine and Dentistry, where he has been a faculty member since January 2018. He is an active researcher with the Centre for Applied Health Economics (CAHE) and serves as an Associate Editor for the Journal of Statistical Computation and Simulation. Professor Ng earned his PhD in Statistics from the University of Queensland in 1999 and has since established an international reputation in statistical methodology. His research expertise spans both theoretical and applied statistics: Mixture models and cluster analysis for complex data Random effects modeling for correlated data structures Machine learning applications in medical contexts Survival analysis and longitudinal data methods Image analysis and pattern recognition techniques Professor Ng's work addresses critical challenges across multiple health domains including oncology, comorbidity research, medical imaging, and health economics. His methodological contributions have significantly advanced evidence-based practice in medical and health research. With over 160 publications accumulating more than 9,000 citations (H-index of 37), Professor Ng's scholarly impact is substantial. His 2019 monograph 'Mixture Modelling for Medical and Health Sciences' represents a landmark contribution to the field. He has secured significant research funding, including seven ARC Project grants and participation in the NHMRC Centre of Research Excellence in Better Health Outcomes for Compensable Injury. Professor Ng's major recognitions include: Membership in the ARC College of Experts (2022-2025) Editorial role at the Journal of Statistical Computation and Simulation Successful leadership of multiple ARC and NHMRC-funded projects Extensive citation record reflecting scholarly influence As an educator and mentor, Professor Ng has supervised five PhD students to completion since 2017 while currently guiding four additional doctoral candidates. His supervision spans diverse topics from comorbidity patterns to advanced statistical techniques for big data analysis in health contexts. He contributes to research training through professional advice on study designs, randomized controlled trials, and analytical strategies across multiple health disciplines.

Martin Masekمشاهده پروفایل

دانشیار

• Computer Vision
• Image Processing
• Artificial Intelligence
• +۹ مورد دیگر

Martin Masek is an Associate Professor of Computer Science in the School of Science at Edith Cowan University (ECU), where he leads the Centre for Artificial Intelligence and Machine Learning (CAIML). He has been with ECU since 2005, having completed his PhD at The University of Western Australia in 2004. His academic journey began with a Bachelor of Engineering with 2nd Class Honours in IT from The University of Western Australia in 1998. Dr. Masek's research spans multiple domains of computer science with a focus on artificial intelligence and its applications. His primary research interests include computer vision and image processing, artificial intelligence and machine learning (particularly Evolutionary Algorithms and Neural Networks), and applications of games technology for modeling, simulation, and visualization. His work demonstrates significant interdisciplinary reach, connecting computer science with health, education, and defense applications. His recent publications reveal a strong trend toward applying AI techniques to solve real-world problems across diverse domains. A significant portion of his work focuses on serious games and virtual reality for health education and training, including applications for meningococcal disease education, clinical leadership development, and aggression de-escalation training. Another major theme involves maritime traffic analysis and anomaly detection, often incorporating seasonal context. His research also includes medical imaging applications, particularly for diabetic foot osteomyelitis detection and radiology report generation, as well as military applications involving strategy discovery and virtual training environments. Dr. Masek has successfully secured numerous competitive research grants totaling millions of dollars, with recent projects including "Immersive Violence and Aggression De-escalation Experience (IVADE)" funded by the Department of Health WA ($408,928), "AI-driven virtual character simulation for aggression and violence de-escalation training in virtual reality" ($499,660), and "Virtual-reality-enhanced advanced tactical parachute training" funded by the Department of Jobs, Tourism, Science and Innovation ($346,145). He has supervised numerous research students to completion, including doctoral candidates working on topics such as "Evolving gameplay elements into virtual terrains," "A Cooperative Co-evolutionary Approach to the Automated Discovery of Team Behaviour in Real-time Strategy Games," and "Detecting diabetic foot osteomyelitis in plain radiographic images using deep learning." His current supervision includes projects on "Anomaly Detection of Seasonal Vessel Activity" and "Representation Learning for Asychronous Data Streams." Dr. Masek leads the Centre for Artificial Intelligence and Machine Learning (CAIML) at ECU, which appears to focus on interdisciplinary AI research with applications across health, defense, and other sectors. His work demonstrates a strong commitment to translating theoretical AI research into practical applications that address real-world challenges.

• Combustion
• Multiphase Flows
• Particle-Laden Flows
• +۱۱ مورد دیگر

Dr Zhiwei Sun is a Senior Lecturer at the School of Electrical and Mechanical Engineering , University of Adelaide. He specializes in advanced experimental methods for combustion and multiphase flows (e.g., particle-laden flows, fluidized beds, bubble flows), utilizing optical/laser-based diagnostics with high spatial-temporal resolution. His research intersects with artificial intelligence (AI) , computational fluid dynamics (CFD) , and renewable thermal energy (hydrogen, solar thermal, biomass). His work focuses on: Optical diagnostics development for complex flows Renewable energy systems for decarbonization High-temperature industrial process innovation He supervises Masters and PhD candidates and applies cutting-edge techniques to study laminar/turbulent flows across energy and environmental applications.

Yu Guang Wangمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Computational Mathematics
• Statistics
• +۱۰ مورد دیگر

Yu Guang Wang is an Associate Professor at Shanghai Jiao Tong University, holding positions across multiple institutes including the Institute of Natural Sciences, School of Mathematical Sciences, Department of Computer Science and Engineering, and AI Biomedicine Center of Zhangjiang Institute for Advanced Study. He also serves as Adjunct Associate Professor at both UNSW Sydney and Shanghai AI Laboratory, demonstrating his significant interdisciplinary reach. His research spans artificial intelligence, computational mathematics, statistics, and data science, with particular focus on geometric deep learning, graph neural networks, applied harmonic analysis, and Bayesian inference. His work bridges theoretical foundations with practical applications in biomedicine and protein design, creating impactful connections between mathematics and real-world problems. Wang's publication record shows a clear progression toward increasingly sophisticated applications of geometric deep learning, particularly in graph neural networks for protein engineering and biomedical applications. His recent work demonstrates strong interdisciplinary collaboration, combining topological data analysis with machine learning for complex biological systems. ICERM Semester Postdoctoral Fellowship of Brown University (2018) IPAM visitor of UCLA (2019) Long-term visitor of AI Group of Prof Pietro Lio at University of Cambridge (2022) ICML Top Reviewer (2020) University International Postgraduate Award, UNSW (2011-2015) As an advisor, Wang has successfully mentored multiple PhD and Master's students working on graph neural networks, geometric deep learning, and applications to biomedicine. His research has been supported by prestigious funding from Shanghai Jiao Tong University, Huawei Central Research Institute, Ministry of Education Key Lab in Scientific and Engineering Computing, Shanghai National Center for Applied Mathematics, National Natural Science Foundation of China, and the European Research Council. He leads an active research group focused on geometric deep learning and its applications to science and medicine.

• Artificial Intelligence
• Machine Learning
• Structural Engineering
• +۷ مورد دیگر

Niusha Shafiabady is an Associate Professor at the Peter Faber Business School, part of the Faculty of Law and Business at Australian Catholic University. Her research focuses on interdisciplinary applications of Artificial Intelligence (AI), including Explainable AI (XAI), machine learning, structural health monitoring, medical diagnosis, and educational technology. She has published extensively in journals like Journal of Building Engineering , IEEE Transactions on Artificial Intelligence , and PLoS ONE . Her work spans diverse domains: optimizing smart buildings for energy efficiency, diagnosing diseases via deep learning, and predicting student engagement in higher education. She also explores AI’s societal impacts, including its role in organizational agility and ethical considerations in technology adoption. Recent research highlights include developing novel algorithms for structural monitoring of bridges, hybrid models for respiratory disease diagnosis, and comparative studies of machine learning in gas safety systems. Collaborations span institutions globally, reflecting her commitment to collaborative, applied research.

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

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

Alzayat Salehمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Aquaculture Technology
• Marine Science
• +۳ مورد دیگر

Alzayat Saleh is a Postdoctoral Research Fellow at James Cook University specializing in deep learning applications for aquaculture and marine sciences. His research focuses on developing novel methods for underwater fish detection, segmentation, tracking, and biomedical image analysis. He has published 15 articles in prestigious journals/conferences with over 200 citations, and received awards like the Australian Research Training Program Scholarship and Food Agility HDR Top-Up Scholarship. Research Interests: Deep learning frameworks for underwater visual analysis Applications in aquaculture automation and environmental robotics Self-supervised learning for annotation-poor datasets Biomedical image analysis using deep learning techniques Article Trends: His work bridges computer vision advancements with real-world challenges in agriculture and marine biology. Recent efforts emphasize lightweight networks for real-time applications, self-supervised methods to reduce annotation dependency, and robotic systems for sustainable farming practices. Key contributions include FieldNet for shadow removal, WeedCLR for long-tailed datasets, and MFLD-net for aquatic morphometry. Awards: Australian Research Training Program Scholarship Food Agility HDR Top-Up Scholarship Collaborations: Actively engages in cross-disciplinary projects with industry partners and academic institutions to advance AI-driven solutions for environmental and agricultural challenges.

• Structural Health Monitoring (SHM)
• Nondestructive Evaluation (NDE)
• Smart Sensing Technologies
• +۷ مورد دیگر

Professor Alex Ching-Tai Ng is a faculty member at the University of Adelaide, serving as a Professor in the School of Architecture and Civil Engineering within the Faculty of Sciences, Engineering and Technology. He joined the university in 2011 as a Lecturer and was promoted to Professor, holding administrative roles such as Associate Dean of Graduate Studies and Director of Graduate Studies (2018–2022). Education: B.Eng. (First-class Honours, City University of Hong Kong), M.Phil. (City University of Hong Kong), Ph.D. (University of Queensland) His research interests span Structural Health Monitoring (SHM), Nondestructive Evaluation (NDE), smart sensing technologies, and vibration testing. He focuses on developing advanced SHM/NDE techniques for structural integrity assessment, with applications in engineering reliability and decarbonization of construction. His publications highlight innovations in guided wave-based damage detection, nonlinear ultrasonic methods for composite materials, and machine learning integration in structural diagnostics. Key trends include FRP-strengthened structures, graphene composites, and real-time hybrid simulation for wind response analysis. Scientific awards include: The Nishino Prize (EASEC, 2016) Australian Research Council Discovery Early Career Researcher Award (2013) He actively supervises Masters and PhD candidates and has served as an assessor for international grants (ARC, RGC, ERC, SNSF, NCN, CONICYT). Professional leadership includes Co-Editor-in-Chief of Engineering Structures and roles in technical committees like ISHMII and ANSHM.

Tingting Biمشاهده پروفایل

مدرس

• Software Architecture
• Knowledge Engineering
• Responsible AI
• +۳ مورد دیگر

Tingting Bi is a Lecturer in the School of Computing and Information Systems at the University of Melbourne, equivalent to an Assistant Professor in the U.S. academic system. She concurrently holds an adjunct position at the University of Western Australia's School of Physics, Maths and Computing. Her research focuses on software architecture for AI-based systems, LLM applications in knowledge engineering, and responsible AI development. Bi earned her PhD from Monash University under Prof. John Grundy, and previously worked as a Research Scientist at CSIRO's Data61. She actively contributes to top-tier conferences such as ICSE and journals like TSE, serving on program committees. Her research interests span knowledge-based engineering, vulnerability detection in smart contracts, and decentralized intelligence systems. Notable awards include Distinguished Reviewer recognitions (2023-2024) and teaching excellence awards.

Matthew Daggittمشاهده پروفایل

مدرس

• Formal Verification
• Neural Networks
• Automated Reasoning
• +۴ مورد دیگر

Matthew Daggitt is a Lecturer in the Department of Computer Science and Software Engineering at the University of Western Australia (UWA), within the School of Physics, Maths and Computing. He holds a PhD in Computer Science from the University of Cambridge (2019), focusing on algebraic routing theory. Previously, he worked at Curtin University and Heriot-Watt University. His research emphasizes formal verification of software correctness, particularly in neural-symbolic systems and distributed algorithms. He leads the development of Vehicle , a tool for end-to-end verification of hybrid neural-symbolic programs. He also maintains the Agda theorem prover’s standard library and explores computational geology, such as zircon analysis for geological dating. Education: PhD in Computer Science (University of Cambridge, 2019). Research Interests: Formal verification, neural networks, automated reasoning, neuro-symbolic systems, distributed algorithms, and routing protocols. His work bridges theoretical foundations with practical tools like Vehicle, enabling rigorous verification of machine learning and classical software integration. Research Trends: Recent publications focus on neural network verification challenges, geological applications of zircon analysis, and advances in formal methods for AI safety. Collaborations span computer science and geology, reflecting his interdisciplinary approach. Advising & Projects: Accepts PhD students in formal verification and neural-symbolic systems. Active in open-source projects and tool development.