Kee Siong Ng

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.