جستجوی استاد راهنمای دانشگاهی

• Quantum-safe cryptography
• Lattice-Based Cryptography
• Post-Quantum Cryptography
• +۴ مورد دیگر

Ron Steinfeld is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University, Australia. He holds editorial roles in Designs Codes and Cryptography (since 2017) and has served on technical committees for top-tier conferences like ASIACRYPT, CRYPTO, and EUROCRYPT. His research focuses on quantum-safe cryptography, lattice-based cryptography, and blockchain security, with over 80 refereed publications and AUD$4M+ in research funding. Education: BSc Mathematics and Physics (Monash University, 1998) BE (Hons., First Class) Electrical and Computer Systems (Monash University, 2000) PhD Computer Science (Monash University, 2003) Research Interests: Design and analysis of cryptographic algorithms, quantum-safe protocols, lattice-based security foundations, and blockchain applications. His work underpins NIST standard algorithms like Kyber and Dilithium through structured lattice problem research. Recent Articles Trends: Focus on privacy-preserving blockchain protocols, post-quantum signature schemes, encrypted data search, and cryptographic applications in adversarial AI. Recent work includes fair Bitcoin watchtower schemes and genomic database privacy solutions. Awards: ASIACRYPT 2015 Best Paper Award Advising & Grants: Supervises PhD projects on quantum-safe cryptography and blockchain security. Leads initiatives like the AUD$4M ARC-funded Quantum Information Technology project. Collaborates with industry partners including CSIRO/Data61. Labs/Teams: Key contributor to Monash’s cybersecurity and cryptography research groups, involving interdisciplinary projects on materials discovery ( Æinstein initiative) and post-quantum blockchain security.

Amin Sakzadمشاهده پروفایل

دانشیار

• Lattice-Based Cryptography
• Wireless Communications
• Post-Quantum Cryptography
• +۶ مورد دیگر

Dr. Amin Sakzad is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University's Faculty of Information Technology. His research focuses on lattice-based cryptography, wireless communications, and post-quantum security protocols. He holds a PhD in Applied Mathematics from Amirkabir University of Technology (2011) and has held academic roles at Carleton University and Monash since 2012. Dr. Sakzad’s expertise spans lattice coding theory, MIMO systems, and privacy-preserving technologies for genomic databases and blockchain applications. He leads multiple ARC-funded projects, including work on secure databases (SRDBMS) and post-quantum cryptographic primitives for FinTech and energy sectors. His research has been recognized through awards such as the FIT Dean’s Award for Teaching Excellence (2021). Key collaborations include projects on blockchain security (CollinStar Lab), genomic data privacy, and energy market cybersecurity. His work addresses UN SDGs through contributions to quality education (SDG 4) and industry innovation (SDG 9). Recent publications highlight advancements in lattice-based cryptography (e.g., CRYSTALS-Kyber variants), privacy-preserving energy trading, and secure blockchain protocols like FPPW watchtower systems. His research bridges theoretical cryptography with practical implementations in embedded systems and 5G telecommunications. Grants: 16 active/completed projects including $1.2M in ARC funding Advising: Supervising PhD projects on lattice applications in post-quantum crypto and blockchain Labs: Core member of Monash’s Software Defined Telecommunications (SDT) Lab and CollinStar Lab

• Photosynthesis
• Global Change Biology
• Protected Cropping
• +۴ مورد دیگر

Professor Oula Ghannoum is a renowned plant scientist and academic leader at the Hawkesbury Institute for the Environment (HIE), Western Sydney University. She serves as Director of the ARC Training Centre for Smart and Sustainable Horticulture and holds leadership roles, including Biological Sciences Discipline Lead and Associate Editor at Functional Plant Biology . Her research focuses on photosynthesis, global change biology, and protected cropping, aiming to enhance food security and climate resilience through crop improvement and sustainable agricultural practices. Education: BSc (Honours) in Plant Biochemistry, University of NSW (1993) PhD in Plant Physiology, Western Sydney University (1998) Research Interests: Professor Ghannoum’s work addresses global challenges like food security and climate change by exploring plant responses to environmental stress. Her lab uses advanced technologies like smart glasshouses and hyperspectral imaging to optimize crop yield and quality. Key areas include sugar signaling pathways in C3/C4 plants, water use efficiency, and heat tolerance in cereal crops. Grants & Funding: She has secured over $25M in research funding, leading projects on C4 photosynthesis, automated crop monitoring, and protected cropping systems. Notable collaborations include the ARC Centre of Excellence for Translational Photosynthesis and Future Food Systems CRC. Awards: 2024 Vice-Chancellor’s Excellence in Research Award 2023 Education and Outreach Award (Australian Society of Plant Scientists) 2001 ARC Postdoctoral Fellowship Labs & Teams: Her team develops innovative frameworks for sustainable horticulture, combining biology with AI and smart technologies. Ongoing work includes imaging-based crop monitoring and phenotyping for climate-resilient crops.

Kerry Taylorمشاهده پروفایل

دانشیار

• Ontologies
• Semantic Web
• Linked Data
• +۵ مورد دیگر

Kerry Taylor is an Associate Professor (Data Science) at the School of Computing, Australian National University (ANU). She holds visiting roles at the University of Surrey (UK) and University of Melbourne. Her career spans 20 years at CSIRO, UN big data projects with ABS, and interdisciplinary research in data management, IoT, and semantic technologies. She lectures in data mining and convenes ANU's postgraduate applied data analytics programs. Education includes a BSc (Hons 1) in Computer Science from UNSW (1983) and a PhD in Computer Science and Technology from ANU (1996). She co-chaired the W3C/OGC Spatial Data on the Web working group (2015-2017) and serves on editorial boards for Knowledge-Based Systems and International Journal of Distributed Sensor Networks . Research focuses on ontologies, semantic web, machine learning in IoT, and spatial data systems. Active projects include government information frameworks, distributed IoT facilities, and sensor data integration. Her work emphasizes interdisciplinary applications of logic-based and semantic approaches to data challenges.

• Cybersecurity and privacy
• Privacy protection
• AI and machine learning
• +۴ مورد دیگر

Dr. Bo Liu is an Associate Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he serves as a core member and director of the AI Security and Privacy (AISP) Research Lab at the Australian Artificial Intelligence Institute (AAII). With expertise spanning cybersecurity, privacy protection, AI and machine learning, and wireless communications, Dr. Liu has established himself as a leading researcher in the field of AI security and privacy. Dr. Liu earned his PhD from the Department of Electronic Engineering at Shanghai Jiao Tong University in 2010. His academic journey at UTS has progressed from Senior Lecturer (November 2019-December 2022) to his current position as Associate Professor (January 2023-present). Dr. Liu's research focuses on the critical intersection of artificial intelligence and security, particularly addressing emerging threats in the age of advanced AI systems. His work spans multiple dimensions of security and privacy, including deepfake detection, privacy-preserving data synthesis, AI model security, and fair machine learning. He has pioneered approaches to detect AI-generated content, protect visual privacy through de-identification techniques, and address the complex relationship between algorithmic fairness and privacy preservation. His publication record demonstrates significant contributions across multiple cutting-edge research areas, with particular emphasis on detecting and mitigating threats from generative AI systems. His recent work reveals a strong focus on deepfake detection across multiple modalities (images, video, and audio), privacy-preserving techniques for sensitive data, and the security implications of emerging AI architectures like Retrieval-Augmented Generation systems. Dr. Liu has secured substantial research funding, including as Lead Chief Investigator on multiple ARC Discovery and Linkage Projects, totaling over $3.5 million AUD. His industry collaborations include partnerships with the NSW Department of Planning and the Reserve Bank of Australia, demonstrating the practical applicability of his research. As an academic leader, Dr. Liu serves as Associate Editor for IEEE Transactions on Broadcasting and actively contributes to the academic community through conference organization, peer review for top-tier venues, and assessment for ARC grant schemes. He also teaches courses including Penetration Testing, Ethical Hacking and Offensive Security, and supervises Masters and PhD students in cybersecurity and privacy research.

• Materials Engineering
• Electrical and Electronic Engineering
• Nanotechnology
• +۷ مورد دیگر

Yongxiang Li is a Professor in the School of Engineering at RMIT University, Australia, specializing in advanced materials and nanotechnology. His research focuses on piezoelectric materials, 2D functional materials, and energy harvesting systems. He is actively involved in supervision of PhD and Master’s projects, including topics like flexible piezoelectric nanogenerators, 2D heterostructures, and low-temperature co-fired ceramic (LTCC) sensors. Research Interests: Materials Engineering, Electrical Engineering, Nanotechnology, Condensed Matter Physics, and Functional Ceramic Design. Key projects include developing lead-free piezoelectric materials, integrating sensors for lithium-ion batteries, and exploring gas sensors using liquid metal-derived oxides. Teaching Interests: Dielectric materials, ferroelectric systems, sensor technologies, and LTCC applications. His interdisciplinary work bridges materials science with optoelectronics and energy storage. Publications span over 400 outputs, emphasizing machine learning-driven materials discovery, defect engineering in 2D materials, and sensor innovation. He leads projects in collaboration with industry, focusing on practical applications of advanced ceramics and nanomaterials.

Vijini Mallawaarachchiمشاهده پروفایل

پژوهشگر ارشد

• Metagenomics
• Bioinformatics
• Bacteriophages
• +۳ مورد دیگر

Vijini Mallawaarachchi is a Research Fellow in Bioinformatics at Flinders University's Flinders Accelerator for Microbiome Exploration (FAME). His research focuses on developing computational methods for metagenomic analysis, particularly viral genome recovery from metagenomes. He holds a PhD in Computer Science from the Australian National University (2022) and a BSc in Computer Science and Engineering (Honours) from the University of Moratuwa, Sri Lanka (2018). Education: Doctor of Philosophy (Computer Science), Australian National University, 2018–2022 Bachelor of Science (Computer Science & Engineering, Honours), University of Moratuwa, 2014–2018 Research Interests: Metagenomics, algorithms for genome recovery, bacteriophage discovery, machine learning applications in bioinformatics, and software engineering for computational biology. His work emphasizes leveraging assembly graphs and computational models to analyze microbial communities and viral genomes. Grants & Awards: 2025: National Computational Merit Allocation Scheme Grant (Co-CI) - A$412,000 2025: ARC Discovery Projects Grant (Co-CI) - A$685,781 2024: Outstanding PhD Thesis Award (ABACBS) 2023: Australian Society for Microbiology Early Career Award Professional Engagement: Active member of ISMB, ISVM, ACM, IEEE, ABACBS, ASM, and RSE AU/NZ. Supervises HDR and Honours students in bioinformatics and computational biology. Labs & Tools: Leads projects at FAME, developed tools like GraphBin, Phables, and ConDiGA for metagenomic analysis. Collaborates on open-source initiatives like the cogent3 Python APIs.

Matthew Stephensonمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Data Science
• +۵ مورد دیگر

Matthew Stephenson is a Lecturer at Flinders University's College of Science and Engineering, specializing in Artificial Intelligence applications for games. He leads the Data for Decisions initiative within the Factory of the Future Transdisciplinary Hub, focusing on AI-powered scenario generation for smart digital twins. Additionally, he is a member of IRL CROSSING, an international lab studying human-autonomous agent teaming dynamics. PhD in Computer Science (Australian National University, 2019) B.Sc.(Hons) in Computer Science (University of Canterbury, 2015) His research applies AI, Machine Learning, and Data Science to game domains, including intelligent agent development for physics-based environments, procedural content generation, and game analytics. He also investigates deceptive behaviors in multi-agent systems and leverages games as testbeds for real-world AI solutions. Recent publications focus on large language models for game benchmarking, physical reasoning challenges, and evolutionary game generation. Scientific awards include an honourable mention at Foundations of Digital Games (FDG'18). He supervises students in procedural generation, game AI, and physics-based task creation, with teaching roles in computational intelligence and neural networks courses.

• Epidemiology
• Public Health
• Child Health
• +۵ مورد دیگر

Associate Professor Aditi Dey holds a conjoint appointment at the Sydney Medical School within the University of Sydney , and serves as Manager of Surveillance at the National Centre for Immunisation Research and Surveillance (NCIRS) . Her work focuses on immunization program evaluation, vaccine coverage analysis, and surveillance of vaccine-preventable diseases and adverse events. She has extensive experience in public health research, spanning roles in Thailand and India prior to her current positions in Australia. Dr Dey completed an MBBS, followed by postgraduate qualifications including a DTM&H (Tropical Medicine & Hygiene), Grad Dip Applied Science (Health Information Management), MPH, and a PhD from the University of Sydney. Her research integrates epidemiological analysis with public health policy implementation, particularly addressing health disparities in vaccination access and outcomes among Indigenous populations and other underserved groups. Her research interests prominently feature vaccine safety , epidemiological trends of infectious diseases, and the impact of vaccination policies such as Australia’s “No Jab, No Pay” initiative. She also investigates the effectiveness of vaccination programs for diseases like rotavirus, HPV, and varicella-zoster virus, often analyzing large-scale national health datasets. Notable contributions include evaluations of Australia’s HPV vaccination program, analysis of rotavirus vaccine efficacy, and assessments of adverse events following immunization. Her work emphasizes improving immunization coverage through better data systems and healthcare provider education, particularly for at-risk populations. Dr Dey actively contributes to public health efforts through teaching and course coordination at the University of Sydney, and collaborates with institutions like the Sydney Infectious Diseases Institute . Her expertise bridges clinical practice, research, and policy, driving evidence-based improvements in Australia’s immunization landscape.

• Graph Analytics
• Graph Mining
• Graph Algorithms
• +۳ مورد دیگر

Dr. Lijun Chang is an Associate Professor in the School of Computer Science at the University of Sydney. He holds an ARC Future Fellowship (2019–2022) and an ARC DECRA Fellowship (2015–2017). Previously, he was at the University of New South Wales. His research focuses on graph analytics, mining, algorithms, and network science. He teaches courses like INFO5011 (Competitive Programming), COMP5313 (Large Scale Networks), and COMP9120 (Database Management Systems), and coaches the USYD Programming Competition Teams. Education: B.Eng. in Computer Science & Technology from Renmin University of China; PhD from the Chinese University of Hong Kong. Research highlights include scalable graph processing systems (e.g., ScaleG), densest subgraph detection, and graph similarity search. He leads projects funded by ARC grants such as 'Advanced Search of Cohesive Subgraphs in Big Graphs' (2018) and 'Directionality-Aware Cohesive Subgraph Search' (2022). His work emphasizes efficient algorithms for large-scale networks and graph databases. Awards : ARC Future Fellow, ARC DECRA Fellow Students : Yu KONG, Rashmika MATHTHAKA GAMAGE, Mouyi XU Labs/Teams : Focuses on graph algorithms and systems research, contributing to open-source tools and large-scale network analysis.

• Business Process Management
• Process Mining
• Explainable AI
• +۵ مورد دیگر

Chun Ouyang is a Professor at Queensland University of Technology (QUT) in the School of Computer Science within the Faculty of Science. With an extensive publication record spanning over two decades from 2002 to 2025, Professor Ouyang has established themselves as a leading researcher in Business Process Management, Process Mining, and Explainable AI. Their work bridges theoretical foundations with practical applications across healthcare, finance, and industrial sectors. Professor Ouyang's research interests primarily focus on Business Process Management systems, Process Mining techniques, Explainable Artificial Intelligence, and Healthcare Process Analysis. Their work has evolved from foundational BPMN/BPEL translation research in the early 2000s to sophisticated process mining approaches in the 2010s, and most recently to cutting-edge Explainable AI applications in clinical and business contexts. They have developed novel methodologies for process querying, predictive process analytics, and XAI evaluation frameworks that have significantly advanced the field. Their research consistently emphasizes practical applicability while maintaining strong theoretical foundations, with publications in top-tier journals and conferences including IEEE Transactions, Springer journals, and major BPM conferences. Analysis of Professor Ouyang's recent publications (2023-2025) reveals a strategic research trajectory that integrates traditional process mining with modern AI techniques, particularly focusing on explainability and trustworthiness. Their work demonstrates a consistent pattern of addressing real-world challenges through rigorous methodological development, with increasing emphasis on healthcare applications, clinical decision support systems, and the ethical implications of AI deployment. The publications show strong interdisciplinary collaboration patterns, particularly with medical researchers and industry partners. Professor Ouyang has mentored numerous PhD students and early-career researchers who have gone on to establish themselves in the BPM and AI communities. Their research group at QUT has secured multiple competitive grants supporting innovative work in process analytics and AI. They maintain active collaborations with leading researchers globally, including Catarina Pinto Moreira, Arthur ter Hofstede, and Moe Wynn. Professor Ouyang leads the Process Analytics Research Group at QUT, which focuses on developing advanced techniques for business process analysis, prediction, and optimization. The group maintains strong industry connections with healthcare providers, financial institutions, and government agencies, ensuring their research has practical impact. Current projects include developing trustworthy AI systems for clinical decision support, cross-organizational process analysis frameworks, and next-generation process mining techniques for complex, distributed systems.

• Cybersecurity
• Data Security and Privacy
• GRC (Governance, Risk, Compliance)
• +۶ مورد دیگر

Dr. Gowri Sankar Ramachandran is a Senior Lecturer in the School of Information Systems at Queensland University of Technology (QUT), specializing in cybersecurity and distributed systems. She holds a PhD from KU Leuven (Belgium) and a postdoctoral position at the University of Southern California (USC). Her research focuses on open-source software security, runtime threat detection, blockchain applications, and IoT vulnerabilities. Notable contributions include the FUSE tool for detecting malicious packages and the discovery of hyperlink hijacking vulnerabilities affecting millions of domains. Research interests span software supply chain security, metadata-based risk analysis, and generative AI for cyber risk modeling. Awards include Best Paper Awards at ACM CBSE (2016), Mobiquitous (2017), and BigMM (2019). Collaborations include projects with CSIRO, the City of Los Angeles, and the University of São Paulo. She teaches courses on cybersecurity, database management, and network security, and actively supervises PhD students in cybersecurity and blockchain domains. Recent publications address blockchain-based data governance, quantum-resilient IoT protocols, and decentralized identity systems. Her work bridges academic research with real-world impact, addressing critical challenges in digital systems security and privacy.

• Indoor Environmental Quality (IEQ)
• Thermal Comfort
• Building Science
• +۴ مورد دیگر

Dr. Thomas Parkinson is a Lecturer at The University of Sydney and Deputy Director of the IEQ Lab within the School of Architecture, Design and Planning. His research focuses on high-performance buildings, indoor environmental quality (IEQ), and human thermal physiology. He holds a PhD in Architectural Science from The University of Sydney and has led technical development of the SAMBA IEQ Monitoring System. He previously worked at the Center for the Built Environment at UC Berkeley. Education: BEnvMgt, BSc(Hons1), PhD in Architectural Science (University of Sydney). Research interests include thermal comfort dynamics, sensor technology for built environments, and adaptive comfort models. Key contributions include the ASHRAE Global Thermal Comfort Database II and studies on workplace dissatisfaction in 600 office buildings. Awards: 2022 Ralph G. Nevins Award (ASHRAE) for thermal comfort research, 2018 Best Paper Award (Building and Environment). He reviews for journals like Building and Environment and serves on ASHRAE Technical Committees. Teaching: Courses include BADP1001 Empirical Thinking and DESC9067 Mechanical Services. Current advisee: Sepideh Borghei (PhD project on indoor greenery's impact). Affiliations: Lawrence Berkeley National Laboratory, UC Berkeley's Center for the Built Environment. Active in ASHRAE committees and professional networks.

Peter Suttonمشاهده پروفایل

دانشیار

• Engineering Education
• Embedded Computing Systems
• Reconfigurable Computing
• +۳ مورد دیگر

Associate Professor Peter Sutton is an academic at the University of Queensland (UQ), holding roles as Deputy Head of School (Teaching and Learning) and Associate Professor in the School of Electrical Engineering and Computer Science. His research focuses on Engineering Education, Embedded Systems, Reconfigurable Computing, and Electronic Design Automation. He has contributed to curriculum design, remote lab management during the pandemic, and hardware-software co-design for embedded systems. Sutton completed his undergraduate studies at UQ and earned advanced degrees at Carnegie Mellon University, with over three decades of experience in computer systems research and education. Education Bachelor of Science, University of Queensland Bachelor (Honours) of Engineering, University of Queensland Masters of Science (Coursework), Carnegie Mellon University Doctor of Philosophy, Carnegie Mellon University Research Interests Sutton’s work spans engineering pedagogy, embedded system design, and reconfigurable computing. Recent projects include adapting hands-on labs for remote learning during the pandemic and optimizing FPGA-based architectures for data compression and encryption. His contributions to cache optimization and multiprocessor systems highlight his expertise in hardware-software integration. Publications His 50+ publications cover topics like FPGA implementations of neural networks, code compression techniques for VLIW processors, and embedded system design tools. Notable contributions include frameworks for reconfigurable system-on-chip development and methods to enhance debugging practices in post-novice students. Labs & Teams He collaborates within UQ’s School of Electrical Engineering and Computer Science, contributing to research groups focused on embedded systems and engineering education innovation.

Zhenghao Chenمشاهده پروفایل

مدرس

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

Dr. Zhenghao Chen is a Lecturer in Data Science at the University of Newcastle, affiliated with the School of Information and Physical Sciences. He earned his Ph.D. from the University of Sydney in 2022, following a B.Eng. H1 degree from the same institution in 2017. Prior to his current position, Dr. Chen served as a Postdoctoral Research Fellow at the University of Sydney (2022-2024), a Research Engineer at TikTok (2024), and as a Visiting Research Scientist at Microsoft Research and Disney Research (2022-2023). Dr. Chen's educational background includes: Doctor of Philosophy, University of Sydney (2022) Bachelor of Information Technology (B.Eng. H1), University of Sydney (2017) Dr. Chen's research spans multiple domains within artificial intelligence, with particular expertise in Computer Vision, Natural Language Processing, and Machine Learning. His work in Generative AI has garnered significant recognition, with applications in both academic and industrial settings. His research interests are reflected in his Fields of Research percentages: Deep Learning (30%), Computer Vision (30%), Natural Language Processing (20%), and Multimodal Analysis and Synthesis (20%). His publications in top-tier venues like CVPR, ICCV, ECCV, and journals like IEEE TPAMI demonstrate the breadth and impact of his work. Analysis of Dr. Chen's recent publications (2022-2025) reveals a consistent focus on neural compression techniques, 3D perception, and multimodal AI systems. His work spans medical imaging (CXR bone suppression), video compression, point cloud processing, and neural surface reconstruction. A notable trend is his exploration of efficient AI systems that work well under resource constraints, as evidenced by his involvement in the EMCLR workshop. His research often bridges theoretical advances with practical applications across multiple domains. Dr. Chen has received several prestigious awards: Microsoft Research Asia StarTrack Fellowship (2025) ACM SIGMM Award for Outstanding PhD Thesis in Multimedia Computing (2024) Australia Government Research Training Program (RTP) Fellowship (2019) Google Australia Prize for Excellence in Computer Science (2017) Dr. Chen is actively involved in the academic community, serving on the Program Committee for major AI conferences including CVPR, ICCV, ECCV, SIGGRAPH, AAAI, and others. He also organizes workshops in Multimedia and ICCV conferences, and serves as a reviewer for prestigious journals. His teaching responsibilities include courses on Intelligent Visual Signal Understanding, Video Intelligence and Compression, Database and Information Management, and Computing Fundamentals at both the University of Sydney and University of Newcastle.