Taotao Cai

Dr. Taotao Cai serves as an Honorary Lecturer and Postdoctoral Research Fellow in the School of Computing at Macquarie University, bringing expertise in graph analytics and machine learning. Previously, they held an Associate Research Fellow position at Deakin University's School of Information Technology. Their academic foundation includes a Ph.D. from Deakin University and a Master's in Computer Science from Shenzhen University, China.

Education:

• Ph.D. in Computer Science, Deakin University, Australia
• Master of Computer Science, Shenzhen University, P.R. China

Research spans graph data processing, social network analytics, and data mining with significant contributions to causal graph neural networks, influence maximization, and privacy-preserving machine learning. Recent work demonstrates strong interdisciplinary applications in remote sensing, healthcare decision systems, and battery management, while maintaining core focus on dynamic network analysis. The research portfolio reveals increasing sophistication in causal reasoning within graph structures and practical implementations for real-world systems.

Publication trends from 2023-2025 show concentrated activity in graph neural networks (40%), privacy/security (25%), and remote sensing applications (20%), with emerging work in causal classification and medical AI. This evolution reflects both theoretical advancement and practical problem-solving across diverse domains.

No formal scientific awards are documented in available sources.

While no formal student advising is recorded, collaborative patterns indicate active mentorship within research teams. Grant activity remains unspecified, but extensive cross-institutional collaborations (particularly with Deakin University and international partners) suggest participation in multiple funded projects. Current trajectories point toward causal graph neural networks for explainable AI and privacy-enhanced federated learning systems as primary research frontiers.

Active participation in international research networks is evident through co-authorship spanning Australia, China, and global institutions, with particular strength in graph algorithm development and social network analysis communities.