جست‌وجوی استاد و پژوهشگر

• Cryptography
• Blockchain
• Cybersecurity
• +۲ مورد دیگر

Fatemeh Rezaeibagha is Senior Lecturer in Cyber Security at Murdoch University's School of Information Technology. Her research develops cryptographic solutions for blockchain systems, IoT security, and privacy-preserving protocols. Awards include the Associate Fellow teaching certificate (2019) and Murdoch Fellowship (2023). Recent publications focus on: privacy-enhanced data sharing for medical IoT; redactable blockchain architectures; attribute-based access control; and lattice-based cryptography. Applied contributions include traceability-revocation schemes for industrial IoT and accountable systems for vehicular networks. Research emphasizes efficiency and post-quantum security in distributed environments.

Jin Hongمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Network Security
• Moving Target Defense
• +۳ مورد دیگر

Dr. Jin Hong is a Senior Lecturer at the University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing and the Department of Computer Science and Software Engineering. Previously, he served as a Postdoctoral Research Fellow at the University of Canterbury (NZ) from 2016 to 2018, where he also earned his PhD in 2015. His research focuses on security modeling, intelligent security solutions, human factors in cybersecurity, and education. Education: PhD in Computer Science, University of Canterbury, 2015 Postdoctoral Research Fellow, University of Canterbury, 2016–2018 Research Interests: Security modeling and analysis Moving Target Defense (MTD) techniques Privacy-preserving technologies Cybersecurity in robotics and AI Cybersecurity education Awards: Claude McCarthy Fellowship ($8,000 NZD, 2014–2015) Grants: Australia DST Research Grant ($200,000 AUD, 2020–2021) Qatar National Research Fund ($726,000 USD, 2016–2019) Multiple DSC Collaborative Research Grants (totaling $300,000 AUD, 2023–2025) Teaching: CITS2006 – Defensive Cybersecurity CITS3006 – Penetration Testing Past courses include CITS1003 (Intro to Cybersecurity) and CITS2401 (Computer Analysis & Visualization)

Joseph Liuمشاهده پروفایل

استاد

• Cyber Security
• Blockchain Technology
• Applied Cryptography
• +۵ مورد دیگر

Joseph Liu is a Full Professor at Monash University's Faculty of Information Technology, leading the Monash Cybersecurity Discipline Group and founding director of the Monash Blockchain Technology Centre. He holds a PhD from the Chinese University of Hong Kong (2004) and has pioneered research in blockchain security, linkable ring signatures (underpinning Monero cryptocurrency), and applied cryptography. His work spans cloud security, healthcare privacy, and lightweight cryptographic algorithms. He has received over 10,000 citations and 200+ publications in top venues like CRYPTO and ACM CCS. Awards include the 2018 ICT Researcher of the Year (ACS) and 2021 IEEE Technical Achievement Award. Education: PhD Information Engineering, Chinese University of Hong Kong (2004) MPhil Information Engineering, Chinese University of Hong Kong (2001) BEng Information Engineering, Chinese University of Hong Kong (1999) Research focuses on bridging cryptographic theory with real-world applications, emphasizing privacy-preserving technologies and post-quantum security. Recent projects include the Algorand Centre of Excellence for Sustainability Informatics and ethical frameworks for blockchain-based digital credentials. He has led 22+ research projects and contributed to ISO/IEC blockchain standards. Awards: ACS ICT Researcher of the Year 2018 IEEE Technical Achievement Award 2021 Best Paper Awards (2021–2022) Teaching includes courses like FIT2034 and FIT5043. He co-chairs the IEEE Blockchain Australia Chapter and has organized major conferences (e.g., ProvSec, Pairing). Current initiatives include privacy-preserving AI in materials discovery and scalable blockchain systems.

• Biometric Security
• Cybersecurity
• Internet of Things (IoT)
• +۳ مورد دیگر

Song Wang is a Senior Lecturer in the Department of Engineering at La Trobe University, Melbourne, Australia. She holds a B.Eng. from Xi'an Jiaotong University and a Ph.D. in Control Theory from the University of Melbourne. Her research focuses on biometric security, IoT security, and privacy-preserving authentication systems, with a particular emphasis on applying machine learning and deep learning to solve challenges in healthcare, cybersecurity, and multimedia security. Dr. Wang's academic journey includes roles as a design engineer at NEC Australia (2000–2004) and as a Lecturer (2005–2008) before becoming a Senior Lecturer at La Trobe University (2009–present). Her work has explored cancellable biometrics, secure IoT authentication, and model inversion attacks in AI systems. She has collaborated extensively on projects involving medical imaging (e.g., Alzheimer’s disease prediction via structural MRI) and privacy-preserving frameworks for EEG and fingerprint authentication. Her recent publications highlight advancements in graph convolutional networks for driver drowsiness detection, homomorphic encryption for biometric data, and lightweight authentication systems for resource-constrained IoT devices. These contributions underscore her expertise in bridging theoretical research with practical applications in cybersecurity and healthcare. No scientific awards are explicitly mentioned in the provided materials. Her research has been supported by collaborations with institutions like the University of Melbourne and industry partners, though specific grants are not detailed.

• Cryptography
• Machine Learning
• Database Systems
• +۳ مورد دیگر

Michael Brand is an Adjunct Professor at RMIT University's School of Computing Technologies. His research focuses on cryptography, privacy-preserving technologies, machine learning, and algorithm design. Key areas include zero-knowledge proofs, secure financial systems like FinTracer, and computational theory. His work bridges theoretical foundations with practical applications in data security and real-time processing systems. Research interests span advanced cryptographic protocols, privacy-enhancing machine learning techniques, and distributed computing paradigms. Notable contributions include innovations in blockchain tracing mechanisms and foundational work in statistical estimation theory. He actively publishes in top-tier venues, addressing challenges in secure computation, network analysis, and database systems. Collaborations emphasize interdisciplinary solutions to modern computational and security challenges.

Kam-Fung (Henry) Cheungمشاهده پروفایل

مدرس

• Cybersecurity
• Transport and Logistics
• Operations Research
• +۳ مورد دیگر

Kam-Fung (Henry) Cheung is a Lecturer at the School of Information Systems and Technology Management (ISTM), UNSW Business School. He holds a PhD in Transport and Logistics Studies from the University of Sydney (2021) and dual bachelor's degrees in Information Engineering (with a Computer Science minor) and Mathematics from the Chinese University of Hong Kong (2013–2014). His research focuses on cybersecurity, transport logistics, operations research, and network vulnerability analysis. Prior to academia, he worked as a Senior Consultant (Cybersecurity) at Ernst & Young (2021–2022) and served as a Research Assistant at institutions including the Institute of Transport and Logistics Studies (ITLS) and the Chinese University of Hong Kong's Department of Systems Engineering. Henry has secured over $280k in research funding from Australian organizations such as the Digital Grid Futures Institute and Woodside Energy. His awards include the 2024 Rising Star Award finalist (Australian Logistics Council), multiple teaching excellence awards (University of Sydney), and scholarships spanning his academic career. He has contributed over 20 peer-reviewed publications, focusing on cybersecurity frameworks, network analysis, and supply chain resilience. Research Interests: Cybersecurity in logistics, network vulnerability mitigation, attacker-defender models, and system dynamics in transport networks Teaching: Courses include Networking & Cyber Security (INFS3617), Programming for Data Analytics (INFS2822), and Managing Cloud Security (INFS5917) Key Projects: Developed decision support systems for transport companies, analyzed global shipping network connectivity, and pioneered algebraic methods for compromised network recovery

Kristin E. Lauterمشاهده پروفایل

استاد مدعو

• Cryptography
• Cryptology
• Homomorphic Encryption
• +۷ مورد دیگر

Kristin E. Lauter is a Senior Director of FAIR Labs North America at Meta AI (2022–present), based in Seattle. She also holds an Affiliate Professor position in the Department of Mathematics at the University of Washington (2008–present). Her research focuses on cryptography, particularly Elliptic Curve Cryptography, Homomorphic Encryption (via SEALcrypto.org), and the intersection of AI and cryptography (AI4Crypto/Private AI). Previously, she spent 22 years at Microsoft Research (1999–2021), where she led the Cryptography Group. She earned her BA, MS, and PhD in Mathematics from the University of Chicago (1990–1996). Her key contributions include work on Supersingular Isogeny Graphs, Homomorphic Encryption standards, and machine learning attacks on lattice-based cryptography. Awards include Fellowships from the AMS, SIAM, and AAAS, and honorary membership in the Royal Spanish Mathematical Society. Lauter has also served as President of the Association for Women in Mathematics (2015–2017). Research interests span both theoretical and applied cryptography, with emphasis on post-quantum systems and privacy-preserving AI. Recent work includes benchmarking cryptographic systems against ML-driven attacks and developing efficient lattice reduction algorithms.

• Privacy-Preserving Machine Learning
• Reinforcement Learning
• Data Privacy and Security
• +۵ مورد دیگر

Associate Professor Kee Siong Ng is affiliated with the School of Computing at the Australian National University (ANU). His research focuses on privacy-preserving technologies, reinforcement learning, distributed systems, and blockchain applications. He has contributed extensively to areas such as privacy-preserving machine learning, federated learning, entity resolution, and scalable database systems. His work emphasizes balancing computational efficiency with privacy guarantees in data-driven environments. Key research contributions include methodologies for secure data processing in federated learning frameworks, privacy-preserving reinforcement learning for population-level systems, and blockchain-based digital identity solutions. He has led projects like Integrated Graph Analytics and contributed to initiatives involving the Australian Medicare dataset. His research often intersects theoretical foundations with practical implementations, addressing challenges in scalability and real-world applicability. Ng has published over 24 peer-reviewed articles, with notable works appearing in venues like IEEE Transactions on Parallel and Distributed Systems and Transactions on Machine Learning Research . His articles frequently explore cutting-edge topics such as differential privacy, approximation algorithms, and multi-agent systems. Collaborations include industry partnerships and interdisciplinary efforts involving health informatics and financial intelligence. His projects include Integrated Graph Analytics (2018–2021): Focused on scalable graph-based data analysis. Translational Fellowship (2018–2022): Bridging theoretical research with practical applications. Research on Data Sets for Health and Pharmaceutical Schemes (2020): Analyzing Medicare and pharmaceutical data with privacy safeguards. Ng's work prioritizes ethical AI and privacy-by-design principles, with a focus on real-world deployment challenges in distributed and federated systems.