Scholar
Falih Gozi Febrinanto
Google Scholar ID: wqzYPTcAAAAJ
Federation University Australia
Citations & Impact
Citations
159
H-index
5
i10-index
3
Publications
19
Co-authors
6
list available
Publications
Resume (English only)
Academic Achievements
- Published papers include 'Graph lifelong learning: A survey' in IEEE Computational Intelligence Magazine, and 'Efficient Graph Learning for Anomaly Detection Systems' presented at WSDM.
Research Experience
- Currently conducting research on applying graph neural networks to various fields such as cyber-physical systems and brain disease detection.
Education
- PhD: Federation University Australia (in progress). Master's Degree: Federation University Australia, specializing in enterprise systems and business analytics. Bachelor's Degree: Brawijaya University, Indonesia, Computer Science.
Background
- Research Interests: Artificial Intelligence, Graph Learning, and Anomaly Detection. Brief Introduction: He is a PhD candidate at Federation University Australia and also affiliated with CSIRO's Data61. His research focuses on leveraging graph neural networks for anomaly detection.
Miscellany
- Email addresses are f.falih.outlook.com; f.febrinanto@federation.edu.au; falih.febrinanto@data61.csiro.au
