جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Computer Vision
• Data Mining
• +۶ مورد دیگر

Dr. Chang Xu is an Associate Professor in Machine Learning and Computer Vision at the University of Sydney's School of Computer Science. He holds a Bachelor of Engineering from Tianjin University and a PhD from Peking University. His research focuses on machine learning, data mining, and their applications in AI and computer vision, including multi-view learning, visual search, and face recognition. He is an ARC Future Fellow and a member of the Sydney Southeast Asia Centre and The Net Zero Institute. Education: B.E. in Engineering (Tianjin University), Ph.D. in Computer Science (Peking University). His research interests emphasize handling heterogeneous data, exploring data variety, and developing algorithms for robust AI systems. His work includes adversarial robustness, neural architecture search, and efficient deep learning models. Research trends in his articles include adversarial robustness in neural architectures, efficient vision transformers, multimodal 3D style transfer, and underwater image restoration. Key contributions span image restoration, video super-resolution, and lightweight network design. He has advised multiple PhD and master's students on topics like diffusion models, radar image synthesis, and graph similarity. Awards: ARC Future Fellow. Collaborations focus on cross-domain data integration and AI applications. His labs and teams explore generative models, robust learning, and scalable robotics policies. Recent work includes diffusion models for action segmentation and robust vision-language systems.

Tien Tsin Wongمشاهده پروفایل

استاد

• Generative AI
• Computer Graphics
• Computer Vision
• +۴ مورد دیگر

Tien Tsin Wong is a Professor in the Department of Data Science & AI at Monash University, Australia. Previously, he served as a Professor at the Chinese University of Hong Kong (1999–2024) and held a Visiting Assistant Professor position at the Hong Kong University of Science and Technology (1998–1999). His research focuses on Generative AI, Computer Graphics, Computer Vision, and Computational Manga, with significant contributions to GPU techniques, image-based rendering, and multimedia compression. Education: He earned a B.Sc. (1992), MPhil (1994), and PhD (1998) in Computer Science from the Chinese University of Hong Kong. Research Interests: His work bridges computational techniques with artistic applications, particularly in manga and animation. Notable areas include generative models, diffusion-based video synthesis, and physically plausible scene generation. His research aligns with UN Sustainable Development Goals through innovations in education and digital accessibility. Awards : He has received the 2004 Young Researcher Award, 2005 IEEE Transactions on Multimedia Prize Paper Award, and two international invention medals (Geneva 2018, Asia Hong Kong 2019). Editorial Roles : He serves as an Associate Editor for Computer Graphics Forum , IEEE Transactions on Visualization and Computer Graphics , and Computational Visual Media . His editorial work underscores his influence in advancing visualization and graphics research. Labs/Teams : While not explicitly named, his collaborations span global institutions, focusing on computational manga, generative AI, and GPU-optimized techniques. His work often involves interdisciplinary teams addressing challenges in digital media and AI.

Renjie Fengمشاهده پروفایل

پژوهشگر ارشد

• Probability Theory
• Random Matrix Theory
• Machine Learning
• +۵ مورد دیگر

Renjie Feng is a Research Fellow in Mathematics and AI at the School of Mathematics and Statistics and the Sydney Mathematical Research Institute , University of Sydney. His work bridges probability theory, statistics, and applications in machine learning, deep learning, and artificial intelligence. His research interests focus on probability theory and its applications to machine learning , random matrix theory , and statistical physics . He investigates extreme value problems, spectral properties of random matrices, and topological features of random fields over Riemannian manifolds. Recent publications highlight trends in random matrix theory (GUE, GOE, GSE), extreme gap problems , determinantal point processes , and Wiener chaos . Collaborative works with F. Götze, D. Yao, and R. Adler emphasize U-statistics , multivariate linear statistics , and random topology inspired by Poisson point process studies.

• Data Science
• Machine Learning
• Blockchain
• +۴ مورد دیگر

Halil Ali is a Lecturer in Data Science (Education Focused) at the School of Computing Technologies, RMIT University. His research spans privacy-preserving machine learning, blockchain technologies, and cybersecurity. Key research areas include federated learning , quantum-enhanced AI , secure biometrics , edge unlearning , and privacy in healthcare data . His recent publications focus on resilient AI systems , blockchain applications , and ethical data handling in emerging technologies. His work demonstrates expertise in integrating machine learning with blockchain security across domains like IoT, smart grids, and metaverse healthcare. He contributes to practical frameworks for zero-trust architectures , lightweight consensus protocols , and quantum-classical hybrid models .

• 3D Computer Vision
• Machine Learning
• VR and AR
• +۴ مورد دیگر

Professor Hongdong Li is a Tenured Professor at the School of Computing, Australian National University (ANU), within the College of Engineering and Computer Science. His research focuses on 3D Computer Vision, Machine Learning, and their applications in dynamic environments. He has held visiting roles at Carnegie Mellon University and has contributed to significant projects like the Australia Bionic Eyes initiative. Education: PhD (Electrical Engineering). Research Interests : 3D Computer Vision fundamentals and applied AI systems Learning-based 3D perception for plant sciences Robot navigation in unfamiliar environments Awards : Marr Prize Honourable Mention CVPR Best Paper Award Advising & Grants : Supervised 40+ PhD students, with funding from ARC, CSIRO, Microsoft, and firms like OPPO/Tencent. Active in projects such as bushfire detection via video analytics and sign language translation systems. Labs/Teams : Co-founder of the Australian Centre for Robotic Vision (ACRV). Collaborates globally on cross-view localization and autonomous systems.

• Mechanical Engineering
• Autonomous Vehicles
• Artificial Intelligence
• +۳ مورد دیگر

Professor Mohammad FARD is a faculty member at RMIT University's School of Engineering, specializing in Mechanical Engineering and Intelligent Systems. He leads research in autonomous vehicles, crash safety, and driver monitoring using AI. His industry experience includes six years at Nissan Technical Centre, focusing on vehicle body design. He holds a PhD from Tohoku University and has collaborated across Engineering, Health, and Science disciplines, achieving international media coverage for work on driver drowsiness and road safety. Research Interests: Autonomous Vehicles Advanced Crash Safety Driver State Monitoring AI in Noise/Vibration Human Factors/Ergonomics Teaching & Projects: Teaches Advanced CAE, Vehicle NVH, and research supervision in areas like crash simulation and vibration control. Current projects include Formula One safety barriers and driver education for autonomous vehicles. Awards & Labs: No awards listed. Active in cross-disciplinary teams and labs addressing automotive innovation and safety.

Ajmal Mianمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۸ مورد دیگر

Ajmal Mian is a Professor of Computer Science at the University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing. He holds an Australian Research Council Future Fellowship (2022) and leads research in Artificial Intelligence, Computer Vision, and Machine Learning. His work focuses on 3D computer vision, adversarial AI defense, and explainable AI. His research interests include 3D point cloud analysis, face recognition, human action recognition, and remote sensing. He has published over 300 papers and secured major grants from ARC, NHMRC, and DARPA, totaling millions in funding. He has supervised 29 PhD students and mentored 12 postdoctoral researchers. Key projects include 3D diffusion models for scene generation, robust 3D vision systems, and defense against AI deception attacks. He serves as a fellow of IAPR, an ACM Distinguished Speaker, and has editorial roles at IEEE Transactions on Neural Networks and Pattern Recognition. Research Awards: HBF Mid-Career Scientist of the Year, West Australian Early Career Scientist of the Year, IAPR Best Scientific Paper Award. Grants: ARC Discovery Projects, National Intelligence & Security Discovery grants, DARPA grants for AI security. His teaching spans computer vision, machine learning, and programming courses. Collaborations include defense, medical, and agricultural applications.

Qi Wuمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Vision-to-Language
• +۴ مورد دیگر

Qi Wu is an Associate Professor in the Department of Computer Science, School of Computer and Mathematical Sciences, Faculty of Sciences, Engineering and Technology at the University of Adelaide, based at the Australian Institute for Machine Learning on North Terrace campus. His decade-long career in computer vision includes leadership in Vision-and-Language research and prior roles as an ARC Senior Research Associate at the Australian Centre for Robotic Vision (ACRV) and a Postdoc Researcher at the Australian Centre for Visual Technologies (ACVT). His educational qualifications consist of a Master of Science (MSc) in Global Computing and Media Technology (2011) and a PhD in Computer Science (2015), both from the University of Bath, United Kingdom. Research expertise centers on computer vision and machine learning , with pioneering work in Vision-to-Language systems. Key contributions include state-of-the-art models for image captioning and visual question answering (VQA) , contextual object modeling across depictive styles, and object detection using environmental cues. His focus on neural architectures for image-text interaction has established him as a leader in multimodal AI. Scientific recognition includes: Microsoft COCO Image Captioning Challenge winner (2015) Visual Question Answering Challenge winner (2015) Dr Wu is eligible to supervise Masters and PhD students but is currently at capacity; prospective students must email directly for availability discussions. His research has been supported by Australian Research Council (ARC) funding through prior Senior Research Associate and Postdoctoral roles. Current work involves leading a dedicated Vision-and-Language team at the University of Adelaide, building on his extensive publication record in top venues. He maintains active affiliations with the Australian Institute for Machine Learning and previously contributed to the Australian Centre for Robotic Vision (ACRV) and Australian Centre for Visual Technologies (ACVT), reflecting deep integration with national AI research infrastructure.

Lexing Xieمشاهده پروفایل

استاد

• Machine Learning
• Social Media Analysis
• Computer Vision
• +۷ مورد دیگر

Lexing Xie is a Professor in Computer Science at the Australian National University. He leads the ANU Computational Media Lab ( http://cm.cecs.anu.edu.au ) and the ANU Integrated AI Network. His work focuses on the intersection of machine learning, social media analysis, and multimedia understanding. Dr. Xie's research broadly focuses on innovative design and use of machine learning algorithms, especially on large-scale graph data and collective behaviour. His recent work spans several key areas: Popularity in social media -- understanding, predicting, and optimization Multimedia knowledge graphs, vision and language integration Humanising machine intelligence through better understanding of social dynamics His publications reveal a strong trend toward understanding information diffusion patterns in social media, particularly through visual content. He has made significant contributions to the study of visual memes, popularity prediction using point processes, and multimodal learning that connects vision with language. His work often bridges theoretical machine learning with practical applications in social media analysis. Dr. Xie has received recognition for his research, including an Honourable Mention at CSCW 2019 for his work on attention flow in online video networks. His research has been supported by collaborations with major institutions including IBM Research and Columbia University. As an advisor, Dr. Xie has mentored numerous students who have gone on to contribute significantly to publications in top-tier conferences. His lab, the ANU Computational Media Lab, serves as a hub for interdisciplinary research connecting computer science with social sciences.

Bisan Alsalibiمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Dr. Bisan Alsalibi is a Lecturer at the School of Information Technology, Monash University (Malaysia campus). She holds a PhD (2017) and MSc (2013) in computer science from Universiti Sains Malaysia, specializing in Artificial Intelligence. Previously, she served as Senior Lecturer at Taylor's University (2022–2023) and Teaching Fellow at Universiti Sains Malaysia (2018–2020). Education: PhD in Computer Vision and Optimization (2014–2017), Universiti Sains Malaysia MSc in Artificial Intelligence (2011–2013), Universiti Sains Malaysia Research focuses on AI-driven solutions in feature selection, bio-inspired optimization techniques, and computer vision applications. Her work bridges membrane computing and real-world problem-solving, including privacy-preserving recommender systems for social networks. She currently supervises PhD students exploring bio-inspired algorithms for complex challenges. Advising & Grants: Acknowledged PhD project areas: Bio-inspired optimization techniques and evolutionary machine learning

Jiyuan Tuمشاهده پروفایل

استاد

• Computational Fluid Dynamics (CFD)
• Multiphase Flows
• Thermal Sciences
• +۴ مورد دیگر

Professor Jiyuan Tu is a Professor in the Department of Mechanical and Automotive Engineering at RMIT University's School of Engineering. He specializes in computational fluid dynamics (CFD), multiphase flows, and their applications in renewable/nuclear energy, biomedical engineering, and built environment systems. His research has led to over 500 peer-reviewed articles, 9 books, and $10M+ in ARC grants. He has supervised over 50 postgraduate students and received prestigious awards such as the RMIT Research Excellence Award (2012) and Fulbright Senior Scholar Award (2008). Research interests include CFD modelling of bioaerosol transport, drug delivery systems, and thermal energy storage. He pioneered numerical models for multiphase flows, contributing to software implementations in industries. Notable works include books on CFD and multiphase flow analysis, and leadership in international conferences like COBEE 2018. He holds honorary professorships at Tsinghua University and is Editor-in-Chief of the Experimental and Computational of Multiphase Flow journal. Industry experience includes roles at ANSTO (1996-2001). Awards span fellowships from JSPS, KOSEF, and Fulbright programs. Grants include ARC Discovery, Linkage, and LIEF projects. His work ranks him among the world’s top researchers in pebble bed reactors and airborne infection studies (SciVal 2016-2025).

Scarlett Raineمشاهده پروفایل

مدرس

• Artificial Intelligence
• Computer Vision
• Robotics
• +۷ مورد دیگر

Dr. Scarlett Raine is a Lecturer in the School of Electrical Engineering and Robotics at Queensland University of Technology (QUT), where she serves as a Chief Investigator at the QUT Centre for Robotics and an Associate Investigator at the QUT Centre for Data Science and QUT Centre for Environment and Society. Her work focuses on applying Artificial Intelligence to automate underwater image analysis for marine ecosystem monitoring.

Massimo Piccardiمشاهده پروفایل

استاد

• Natural Language Processing
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Massimo Piccardi is a Professor of Natural Language Processing (NLP), Computer Vision, and Machine Learning at the University of Technology Sydney (UTS) , where he has been since 2002. He currently serves as the Head of the School of Electrical and Data Engineering and leads the Big Data Analytics program at the Global Big Data Technologies Centre. His research focuses on advancing NLP, machine learning applications in healthcare, and cybersecurity in IoT systems. He has authored over 200 journal papers and conference proceedings, secured significant ARC and CRC grants, and holds the IEEE Computer Society Distinguished Contributor Award (2022). Education & Professional Roles: Joined UTS in 2002, progressing from Associate Professor (2002–2007) to Professor (2008–present). Serves as Associate Editor for IEEE Transactions on Big Data and Editor for Artificial Intelligence in Medicine. Active in professional societies including IEEE, ACL, and ALTA (President, 2023–2024). Research Interests: Core areas include NLP (translation, summarization, adversarial attacks), healthcare informatics (clinical NLP, health service analysis), and cybersecurity (IoT security, privacy-preserving systems). Cross-cutting themes include generative models, cross-lingual systems, and ethical AI. Grants & Projects: Principal Investigator on ARC Discovery/Linkage projects and CRC grants. Recent projects include controllable machine translation (Amazon), privacy-preserving digital agriculture, and STEM innovation (ASTRID project with NBN Co). Labs & Collaborations: Leads the UTS Global Big Data Technologies Centre, collaborating on projects like adversarial NLP attacks, medical machine translation, and secure IoT frameworks.

Ly Fie Sugiantoمشاهده پروفایل

دانشیار

• Accounting Information Systems
• Data Analytics
• Artificial Intelligence
• +۸ مورد دیگر

Dr. Ly Fie Sugianto is an Associate Professor in the Department of Accounting at Monash Business School, Monash University. Her research focuses on the integration of data analytics, artificial intelligence, and machine learning in accounting and business systems, with applications in the energy sector and organizational behavior. Monash Business School, Monash University Department of Accounting Specialization: Accounting Information Systems, Data Analytics, AI Her research interests span Accounting Information Systems , Agent-Based Simulation , Decision Support Systems , and Technology Adoption . She applies computational methods to study competitive dynamics in deregulated electricity markets and the impact of digital tools on employee well-being and organizational resilience. The recent publications reflect a strong trend in using AI and simulation to analyze complex socio-technical systems, particularly in energy markets and leadership dynamics. Keywords across her work include agent-based modeling , data analytics , servant leadership , and enterprise social media , indicating interdisciplinary research at the intersection of information systems, management, and public policy. Her scientific awards include competitive grants from the ARC (SPIRT/Linkage) , the Australia Indonesia Governance Research Partnership (AIGRP) , and the Sumitomo Foundation . ARC Grant: Dispatch Optimisation in the Australian National Electricity Market Sumitomo Foundation: Technology Use and Employee Well-Being AIGRP: Governance and MSME Resilience during Pandemic Dr. Sugianto has advised research projects and collaborated with industry partners such as Western Power , Ecogen Energy , and AEMO . She is currently accepting PhD students and leads externally funded research initiatives. Her work contributes to UN Sustainable Development Goals related to industry innovation and responsible consumption. She is affiliated with research teams focusing on intelligent decision support systems and digital transformation in business , with active collaborations in Australia and Indonesia.

• Structural Dynamics
• Earthquake Engineering
• Blast and Impact Engineering
• +۳ مورد دیگر

Professor Hong Hao is a John Curtin Distinguished Professor at Curtin University, affiliated with the School of Civil and Mechanical Engineering and the Curtin Research Centre for Infrastructural Monitoring & Protection. His expertise spans Structural Dynamics, Earthquake Engineering, Blast and Impact Engineering, and Structural Health Monitoring. He holds prestigious roles like Fellow of ATSE, ISEAM, and ASCE, and has led organizations such as the International Association of Protective Structures and the Australian Earthquake Engineering Society. Education: BE (Tianjin University, 1982), MSc (UC Berkeley, 1985), PhD (UC Berkeley, 1989). Awards include the Tan Chin Tuan Fellowship and multiple Ko Medals. He has authored over 200 journal articles, with recent work focusing on blast-resistant materials, seismic fragility, and AI-driven structural health monitoring. His research emphasizes resilient infrastructure, including metaconcrete structures, corrosion-resistant materials, and sensor-based damage detection. Ongoing projects involve smart tunnel safety under BLEVE explosions and modular building systems.