جست‌وجوی استاد و پژوهشگر

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.

• Machine Learning
• Statistical Learning Theory
• Optimization
• +۳ مورد دیگر

Professor Yiming Ying is a faculty member in the Faculty of Science at the University of Sydney, where he joined in December 2023. Previously, he held tenured positions at SUNY Albany (Departments of Mathematics & Statistics and Computer Science) and was a Lecturer at the University of Exeter. He completed his PhD in Mathematics at Zhejiang University (2002) and postdoctoral training at CityU Hong Kong, UCL, and University of Bristol. Research Focus His research spans statistical learning theory, optimization algorithms, trustworthy AI, and data science mathematics. Key applications include cancer informatics for early detection. His work aligns with Faculty research strengths in Data and Decisions and Decision-Making for a Sustainable Future. Recent Research Trends Analysis of recent publications shows strong focus on theoretical foundations of machine learning: differential privacy, fairness algorithms, optimization methods for AUC maximization, generalization guarantees, and robust learning techniques for adversarial settings and biological data. Awards and Honors SUNY Chancellor’s Award for Excellence (2023) University at Albany Presidential Research Award (2022) University of Exeter Merit Award (2012) Grants and Advising Significant funding includes current ARC DP250101359 (2025-2028) and multiple past NSF grants. He founded the UALBANY Machine Learning Group and currently advises PhD student Peilin LIU on operator learning.

Dylan Campbellمشاهده پروفایل

مدرس

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

Dylan Campbell is a Lecturer in Computing at the Australian National University (ANU), affiliated with the ANU College of Systems & Society. His research focuses on computer vision, optimization, and robotics, particularly in 3D vision and deep learning applications. He has held prior roles as a Research Fellow at the University of Oxford’s Visual Geometry Group and ANU’s Australian Centre for Robotic Vision. Campbell holds a PhD from ANU (2018) and a BE in Mechatronic Engineering from UNSW (2012). Research interests include geometric sensor alignment, neural radiance fields, and differentiable optimization layers. He actively supervises students (7 PhD/DPhil, 3 MEng, 9 honours) and teaches advanced courses in computer vision and robotics. Notable awards include the Marr Prize Honourable Mention (2017) and the IEEE Australia Council Postgraduate Student Paper Competition (2018). He has organized workshops at ECCV and CVPR, served as a reviewer for top conferences like CVPR/ICCV/ECCV, and contributed to datasets like SEED4D and RefRef. His work emphasizes efficient training of neural networks and leveraging symmetries in data for long-range connections.

• Computer Vision
• Machine Learning
• Robotics
• +۶ مورد دیگر

Dr. Feras Dayoub is a Senior Lecturer at the School of Computer and Mathematical Sciences (Faculty of Sciences, Engineering and Technology) at the University of Adelaide , specializing in Embodied AI and Robotic Vision within the Australian Institute for Machine Learning (AIML) . He co-directs the CROSSING French-Australian laboratory for human-autonomous agent teaming and holds an Adjunct position at the Queensland University of Technology (QUT) , serving as an Associate Investigator at its Centre for Robotics . Previously, he was a Chief Investigator at the ARC Centre of Excellence for Robotic Vision . His research focuses on advancing reliable deployment of computer vision and machine learning on mobile robots in real-world environments. Applied projects include agricultural automation , environmental conservation , and autonomous infrastructure monitoring . He has published extensively on topics like object detection , domain adaptation , 3D representation learning , and vision-language navigation , with a particular emphasis on robustness in dynamic and partially observed environments. Dr. Dayoub is also an educator specializing in programming , computer vision , and robotic perception . He contributes to open-source robotics research through tools like AARK (Autonomous Racing Toolkit) and has led teams developing solutions for precision agriculture (e.g., Deepfruits fruit detection system) and environmental monitoring (e.g., Crown-Of-Thorns starfish detection ). Key Collaborations : CROSSING Lab, QUT Centre for Robotics Research Themes : Embodied AI, Robust Perception, Domain Adaptation

• Trustworthy Machine Learning
• Adversarial Learning
• Causal Representation Learning
• +۶ مورد دیگر

Associate Professor Tongliang Liu is affiliated with the School of Computer Science at the University of Sydney, serving as Director of the Sydney Artificial Intelligence Centre and Trustworthy Machine Learning Lab. He holds a BEng and PhD, and is an ARC Future Fellow. His research focuses on trustworthy machine learning, including adversarial defense, causal representation learning, and robust AI systems. He has authored over 200 papers in top venues like NeurIPS and ICML, and serves as co-Editor-in-Chief of Neural Networks. Research Interests: Developing reliable algorithms for machine learning, emphasizing generalizability and safety. Specific areas include learning with noisy labels, causal inference, and foundational model ethics. He aims to bridge theoretical guarantees and practical applications in computer vision and data mining. Awards: 2024 CORE Award, 2023 IEEE AI's 10 to Watch, 2022 ARC Future Fellowship. Notable recognitions include Eureka Prize shortlist and DECRA. Advising & Grants: Supervises 12 PhD/Master’s students on topics like trustworthy AI, causal discovery, and quantum machine learning. Leads grants on robust learning and AI safety. Labs: Sydney AI Centre and Trustworthy Machine Learning Lab.

Ibrahim Khalilمشاهده پروفایل

استاد

• Security
• Privacy
• Federated Learning
• +۴ مورد دیگر

Professor Ibrahim Khalil is a faculty member in the School of Computing Technologies at RMIT University, Melbourne, Australia. He holds a PhD in Computer Science from the University of Bern (2003) and has extensive industry experience in Silicon Valley focusing on secure network protocols. His research spans Security, Privacy, Federated Learning, Blockchain, Quantum Computing, and Distributed Systems. He leads high-impact projects funded by ARC grants (DP250100582, DP220100215, etc.) and international initiatives like the EU’s SELFY project. His work addresses challenges in secure AI data analytics, privacy-preserving systems, and critical infrastructure protection. Khalil supervises PhD/Masters students on topics ranging from federated learning security to quantum-enhanced machine learning. Education: PhD in Computer Science (University of Bern, 2003); prior roles at EPFL, Osaka University, and industry tech hubs. Research Interests: Privacy-Preserving Technologies Blockchain Applications in Healthcare and Supply Chains Quantum Computing for Machine Learning Secure Edge Computing and Federated Learning IoT Security and Critical Infrastructure Protection Grants & Collaborations: Over 10 major grants since 2017, including ARC Discovery/Linkage Projects and international partnerships (QNRF, EU). Notable projects include Privacy-Aware Digital Twins for Critical Infrastructure and Federated Learning frameworks for GenAI models. Advising & Labs: Active supervisor of 25+ research projects since 2013, focusing on anomaly detection, secure data analytics, and blockchain-based systems. Collaborates with industry partners on defense and healthcare tech.

Mahsa Baktashmotlaghمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Domain Adaptation
• +۳ مورد دیگر

Associate Professor Mahsa Baktashmotlagh is an ARC Future Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. Her research focuses on machine learning techniques applied to visual data analysis, biomedical data (e.g., antibacterial activity prediction), and cybersecurity. She holds a PhD from the University of Queensland (2014) and has contributed to over 50 peer-reviewed publications. Her research interests include domain adaptation, deep learning, and robust generalization across domains. Notable contributions include the development of DI-NIDS (a domain-invariant network intrusion detection system) and advancements in open-set domain adaptation. Her work bridges theoretical machine learning with practical applications in healthcare and computer vision. Education: PhD in Machine Learning, The University of Queensland (2014) Awards: ARC Future Fellowship (202X) Research Themes: Domain Adaptation, Cybersecurity, Biomedical AI Her recent work explores challenges in cross-domain generalization, adversarial machine learning, and scalable 3D object detection. She is actively involved in supervising graduate students and collaborates on interdisciplinary projects involving robotics and medical imaging.

• Business Analytics
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Adjunct Professor Evgeny Osipov is affiliated with La Trobe University's Business Analytics department. His research spans artificial intelligence, hyperdimensional computing, and neural network architectures. Academic Rank: Adjunct Professor Department: Business Analytics Email: E.Osipov@latrobe.edu.au Research interests include: Hyperdimensional computing and vector symbolic architectures Spiking neural networks and reservoir computing Hardware-efficient AI implementations Causal reasoning in large language models Self-organizing maps and spatiotemporal sequence learning Applications in smart cities and robotic navigation Recent research outputs demonstrate expertise in: Developing unsupervised learning frameworks using hypervectors Optimizing reservoir computing with cellular automata Creating memory-efficient neural network models Advancing hyperdimensional classification techniques Exploring causal graph integration in language models

Mehrtash Tafazzoli Harandiمشاهده پروفایل

دانشیار

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

Mehrtash Tafazzoli Harandi is an Associate Professor in the Department of Electrical and Computer Systems Engineering at Monash University, part of the Faculty of Engineering. His research focuses on machine learning and computer vision, particularly visual data analysis, with contributions to geometric deep learning, continual learning, and medical imaging. He holds editorial roles at IET Computer Vision , Frontiers in Imaging , and Journal of Imaging . Education & Previous Affiliations: Prior to Monash, he worked at NICTA (Canberra & Queensland Research Labs) and CSIRO-Data61. His Erdős number is 4 via a collaboration path through Richard Hartley. Research Interests: His work spans geometric learning, diffusion models, medical image analysis, and sustainable AI applications. Key areas include unlearning mechanisms in AI, 3D reconstruction compression, and robust MRI reconstruction using contrastive learning. Grants & Projects: He leads projects funded by ARC, US Air Force, and industry collaborations, including 'Can Machines Unlearn?' (ARC, A$790k) and 'Exploiting Geometries of Learning' (ARC, A$420k). His work addresses challenges in lifelong learning, model adaptation, and trustworthy AI from limited data. Awards: Recipient of Best Recognition Paper (IEEE DICTA 2013), NICTA Impact Award (2015), and multiple outstanding reviewer recognitions at top conferences. Teaching: Teaches courses on neural networks, computer vision, and advanced data analysis at Monash University. Supervises PhD students with a focus on mathematical and computational proficiency. Labs/Teams: Collaborates with the Australian Center for Robotic Vision (ACRV) and contributes to interdisciplinary projects at CSIRO-Data61. His research group explores cutting-edge AI applications in healthcare, manufacturing, and environmental sustainability.

Jian Zhangمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Data Analytics
• +۳ مورد دیگر

Prof. Jian Zhang is a Professor in the School of Electrical and Data Engineering at the University of Technology Sydney (UTS), specializing in computer vision, pattern recognition, and multimedia signal processing. He leads the Multimedia Data Analytics Lab at the Global Big Data Technologies Centre, focusing on agri-food sector applications such as livestock monitoring and AI-driven solutions for agricultural efficiency. Education : PhD, School of Information Technology and Electrical Engineering, University of New South Wales, 1999 MSc, The Flinders University of South Australia, 1994 BSc, East China Normal University, 1982 Research Interests : His work spans 2D/3D computer vision, large-scale image/video analytics, and cross-disciplinary projects in agriculture and remote sensing. He has pioneered AI systems for livestock counting, poultry welfare monitoring, and fish quality assessment, funded by organizations like Meat & Livestock Australia and Australian Eggs. Grants & Projects : Current projects include AI-based hen health monitoring ($5M+ funding since 2011) Collaborations with industry partners like Sydney Fish Market and Fremantle Port Students & Academic Leadership : Supervised 19 PhD graduates and 5 research fellows Recruiting new PhD candidates in computer vision and data analytics Labs & Teams : Director of the Multimedia Data Analytics Lab, collaborating with global experts through UTS's Distinguished Visiting Scholars program.

• Machine Learning
• Data Mining
• Computer Vision
• +۳ مورد دیگر

Professor Xue Li is a faculty member in the School of Electrical Engineering and Computer Science at the University of Queensland. His research focuses on machine learning, data mining, and their applications in healthcare, materials science, and computer vision. He has authored over 300 publications, including seminal works on knowledge graph completion, video quality enhancement, and alloy design using machine learning. His work bridges theoretical advancements with real-world applications, such as clinical diagnosis andTinyML systems. Key research interests include graph representation learning, medical informatics, and efficient algorithms for multimedia data. Notable contributions include developing commonsense-enhanced relation extraction models and frameworks for compressed video reconstruction. His research also addresses challenges in federated learning and privacy-preserving genomics. Prof. Li has collaborated extensively with industry and academia, contributing to projects in RFID systems, electronic nose pattern recognition, and cybersecurity. His work is published in top-tier venues like IEEE Transactions and ACM conferences. Despite no listed awards, his prolific output underscores academic impact.

Marius Portmannمشاهده پروفایل

استاد

• Cybersecurity
• Computer Networks
• Internet of Things (IoT)
• +۳ مورد دیگر

Professor Marius Portmann is the UQ-Cisco Chair of Network Security at the School of Electrical Engineering and Computer Science (EECS), University of Queensland. His expertise spans Cybersecurity, IoT, and Applied AI. He holds a PhD from ETH Zurich (2003) and has led research in Software Defined Networking (SDN), blockchain, and energy-harvesting IoT systems. Education: PhD in Electrical Engineering from Swiss Federal Institute of Technology (ETH Zurich), 2003. Research focuses on securing IoT networks, AI-driven intrusion detection, and sustainable sensor systems. He has pioneered self-powered IoT systems using energy harvesters and developed frameworks like FlowTransformer for network analysis. His work bridges theoretical advancements with practical applications in smart tourism, energy efficiency, and edge computing. Recent publications highlight innovations in DDoS detection (P4-Secure), sensor-based environmental monitoring (EcoShower), and graph-based anomaly detection (XG-BoT). His datasets (e.g., NF-ToN-IoT-v3) are widely used in ML-based cybersecurity research. Collaborations include industry partners like Cisco and institutions like RMIT. Grants and leadership roles in interdisciplinary projects underscore his impact. He advises on IoT security standards and contributes to open-source tools for network research. Current projects explore edge-AI integration and sustainable sensor networks.

• Artificial Intelligence
• Machine Learning
• Game Theory
• +۷ مورد دیگر

Prof. Tansu Alpcan is a Professor and Reader in the Department of Electrical and Electronic Engineering at The University of Melbourne, Australia. He holds a PhD from the University of Illinois at Urbana-Champaign (UIUC) and has held academic positions at Technical University Berlin and Deutsche Telekom Laboratories. His research focuses on AI/ML applications in engineering, game theory, cybersecurity, Industry 4.0, quantum machine learning, smart grids, and communication networks. Education: PhD in Electrical and Computer Engineering (UIUC, 2006); MSc (UIUC, 2003); BEng (Bogazici University, 1999). Research interests include adversarial machine learning, cybersecurity games, quantum computing, and renewable energy systems. Authored over 200 papers and two books, including Network Security: A Decision and Game Theoretic Approach (Cambridge, 2011). Recipient of IEEE Senior Membership (2012) and multiple best paper awards. He leads the WILAB and has secured grants such as the ARC Training Centre in Optimisation Technologies. Current projects include quantum machine learning, adversarial reinforcement learning, and smart grid modeling. Supervised 17 PhD and 3 Master’s students.

Tongtong Wuمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Natural Language Processing
• Knowledge Extraction
• +۷ مورد دیگر

Tongtong Wu is a Research Fellow in the Department of Data Science & AI at Monash University, actively contributing to cutting-edge research in artificial intelligence and natural language processing. She collaborates with leading researchers such as Gholamreza Haffari and Yuefeng Li on projects involving knowledge extraction, continual learning, and generative modeling. Education: Ph.D. in Artificial Intelligence, Southeast University (Jiangsu, China), awarded December 20, 2023. Thesis: Structured Knowledge Extraction with Limited Data . Her research focuses on developing advanced AI models for structured knowledge extraction, with emphasis on generative event extraction, weakly supervised learning, and continual adaptation of language models. She leverages deep learning and probabilistic methods to improve model robustness and generalization in low-data regimes. The recent publications demonstrate a strong trend toward integrating external knowledge into generative frameworks and advancing weakly supervised techniques for real-world NLP tasks. Her work spans event detection, topic modeling, and socio-cultural norm discovery, often using pretrained language models and mutual information-based regularization. Scientific Awards: No awards listed in the provided text. She is currently a Chief Investigator on the active project Lifelong Version-controlled Code Generation (2025–2026), indicating involvement in grant-funded research. While there is no mention of formal student supervision, her collaborative output suggests integration within a vibrant research team. She is affiliated with a research network focused on AI and data science at Monash, contributing to both journal articles and top-tier conference proceedings.

• Hyperspectral Imaging
• Computer Vision
• Pattern Recognition
• +۳ مورد دیگر

Jun Zhou is Professor and Deputy Head of School (Research) at Griffith University's School of Information and Communication Technology. His research specializes in hyperspectral imaging, computer vision, and pattern recognition with applications in agriculture, environmental monitoring, and remote sensing. Zhou leads significant projects including the ARC Research Hub for Driving Farming Productivity and Disease Prevention. His work develops innovative computer vision systems for agricultural automation, environmental conservation, and industrial quality control. He has received the ARC Discovery Early Career Researcher Award and secured extensive research funding from ARC, CSIRO, and industry partners. Zhou's publications demonstrate consistent contributions to hyperspectral image analysis, object tracking, and deep learning applications. As Deputy Director of the ARC Industrial Transformation Research Hub, he coordinates multi-institutional research teams developing AI-powered solutions for farming productivity and disease prevention.