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Raymond WongView profile

Associate Professor

• Database Systems
• Data Science/Analytics
• Information Extraction
• +5 more

Associate Professor Raymond Wong is a faculty member in the School of Computer Science & Engineering at UNSW Sydney, where he also serves as Director of Online Education. He holds a PhD from Hong Kong University of Science & Technology, and has extensive expertise in database systems, data science, information extraction, and cloud computing. With over 220 conference papers, 56 journal articles, and two US patents, his research spans data mining, machine learning, and big data analytics. He has received 8 best paper awards and 4 industry/professional recognitions. His advisory roles include stints at Data61/CSIRO and Stats Central, and he has co-founded technology startups. Key projects include the Singapore NTU SPIRIT Smart Nation Research Centre's Intelligent Case Retrieval System (ICRS) and collaborations across Asia-Pacific regions. His research interests focus on database optimization, data analytics, NLP, and cloud infrastructure. He has supervised 25 PhD students and 65 honors students, six of whom earned University Medals. Notable grants include projects on ovarian cancer detection ($135k), cardiovascular disease analysis ($100k), and social media analytics ($250k). Awards include the 2016 ARC UNSW Supervisor Award and multiple IEEE best paper honors. His work bridges academia and industry, addressing challenges in healthcare, cybersecurity, and data-driven innovation.

• Operating systems
• Distributed systems
• Computer Science
• +3 more

Professor Willy Zwaenepoel is a distinguished academic and Dean of the Faculty of Engineering at the University of Sydney. He holds Fellowships from ACM, IEEE, and ATSE. His research focuses on distributed systems, operating systems, and experimental computer science. Previously, he spent two decades at Rice University and nine years as Dean of EPFL's School of Computer and Communication Sciences before joining Sydney in 2018. Education: BS/MS, Ghent University (1979) MS/PhD, Stanford University (1980/1984) Research Interests: His work emphasizes distributed systems and operating systems, with contributions to key-value stores, transactional systems, and large-scale graph processing. Recent projects include optimizing geo-replicated systems and improving datacenter scheduling efficiency. Articles Trends: Recent publications address OS scheduling (Nest), distributed graph mining (Tesseract), and transactional systems' performance limits. He explores hardware-software co-design for multicore systems and energy-efficient data centers. Awards: Fellow of ACM (2021) Fellow of IEEE (2020) Fellow of ATSE (2019) Grants & Advising: Active grants include adaptive key-value store research (2021) and large-graph processing systems (2018). He advises PhD students and postdocs on distributed systems and storage challenges. Labs/Teams: Leads the Sydney systems research group focusing on scalable distributed systems and cloud infrastructure.

• Privacy-Preserving Machine Learning
• Reinforcement Learning
• Data Privacy and Security
• +5 more

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.

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +4 more

Dr. Shikha Anirban is a Lecturer at the School of Information Technology , Murdoch University , with a focus on artificial intelligence, graph databases, and machine learning. She bridges theory and practice through active learning and collaborative projects. Education: PhD in Computer Science (2018–2023, Griffith University); MSc and BS in Computer Science and Engineering from Jahangirnagar University (Bangladesh). Research Interests: Graph databases, machine learning, data engineering, and their applications in AI and information retrieval. Awards: Recipient of the Jeffry Blee Memorial Award in 2019 and 2022. Her teaching expertise spans Database Analysis and Design, Artificial Intelligence, Machine Learning, and Computer Programming. She emphasizes critical thinking and real-world applications in her pedagogy.

Shuang AoView profile

Research Fellow

• Machine Learning
• Reinforcement Learning
• Curriculum Learning
• +4 more

Mr. Shuang Ao is a Postdoctoral Research Fellow at the School of Computer Science and Engineering, University of New South Wales (UNSW Sydney). He earned his PhD from the University of Technology Sydney in January 2024. His research focuses on machine learning, reinforcement learning, and curriculum learning, with applications in robotic control and antibody drug discovery. Research Interests: Machine Learning Reinforcement Learning Curriculum Learning Graph Algorithms Optimization Techniques Recent Publication Trends: Shuang's work spans large language models for location-based recommendations, spatio-temporal forecasting, reinforcement learning frameworks, and graph algorithm optimizations. His articles address both theoretical advancements and practical applications in scalable systems and data analysis. Contact: Email: shuang.ao@unsw.edu.au

Mark WallaceView profile

Professor

• Optimization
• Constraint Programming
• Artificial Intelligence
• +5 more

Professor Mark Wallace is a renowned academic at Monash University , specializing in modelling and software platforms for planning, scheduling, and optimization problems . He has led groundbreaking projects such as the development of the ECLiPSe constraint programming platform (acquired by Cisco Systems) and the G12 hybrid optimization platform , which was commercialized through his company, Opturion . His research consistently bridges theoretical innovation with real-world applications, supported by industry funding from major companies like British Airways, Qantas, and Woodside Energy. Education: MA in Mathematics and Philosophy from Oxford University; MSc in Artificial Intelligence from Queen Mary College, London; PhD in Communicating with Databases in Natural Language from Southampton University. His work focuses on application-driven research in constraint programming, hybrid optimization systems, and decision support technologies. Recent publications highlight advancements in public transport routing , autonomous vehicle fairness constraints , and human-centred feasibility restoration . He actively collaborates with industry partners on projects involving Melbourne Water , Woodside , and the Alertness CRC , driving innovations in smart infrastructure and transport systems.

Binghao LiView profile

Associate Professor

• Indoor Positioning
• Pedestrian Navigation
• Satellite Positioning
• +3 more

Associate Professor Binghao Li leads the MIoT & IPIN Lab at the School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney. He holds a PhD in Spatial Information Systems from UNSW and advanced degrees in Civil and Electrical/Mechanical Engineering from Tsinghua University and Beijing Jiaotong University. Expertise in indoor/outdoor positioning systems Pioneering mine IoT applications Leader in pedestrian navigation research His research spans indoor positioning technologies, satellite navigation, and mining IoT solutions. Key grant projects include: 2021 CRC-P grant ($2m) for underground mine LoRa networks 2020 ARC Research Hub ($5m) for connected sensors 2018 Digital Grid Seed Funding for indoor navigation 2015-2019 ARC Linkage grants for positioning systems Award highlights: 2019 Best Paper & Presentation Awards 2010 VC's Post-Doctoral Fellowship 2004-2005 student research awards He supervises research in indoor positioning and mine IoT, and teaches courses including ENGG1000 Engineering Design and MINE8710 Mine Slope Stability.