Bryan HOOIAssistant Professor
PhD in Machine Learning, Carneige Mellon University, 2019
MSc in Computer Science and BSc in Mathematics, Stanford University, 2014
- Artificial Intelligence
- Machine Learning
- Graph Algorithms
- Anomaly Detection
- Spatiotemporal Data
- Biomedical Applications of AI
Bryan HOOI is an assistant professor in the Computer Science Department, School of Computing at the National University of Singapore. He has obtained his PhD degree in Machine Learning from Carnegie Mellon University, USA in 2019, his Master of Science degree in Computer Science and Bachelor with Honours degree in Mathematics from Stanford University, USA in 2014. His research interests include machine learning, graph mining, anomaly detection, spatiotemporal data, and biomedical applications of AI.
Minji Yoon, Bryan Hooi, Kijung Shin and Christos Faloutsos. Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2019.
Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye. BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series, International Joint Conference on Artificial Intellince (IJCAI) 2019.
Bryan Hooi, Dhivya Eswaran, Hyun Ah Song, Amritanshu Pandey, Marko Jereminov, Larry Pileggi, and Christos Faloutsos GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2018. Runner-up Best Student Data Mining Paper Award
Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, and Christos Faloutsos. FRAUDAR: Bounding Graph Fraud in the Face of Camouflage. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2016. KDD Best Paper Award (Research Track)
Awards & Honours
- ECML-PKDD 2018 Runner-Up Best Student Data Mining Paper Award
- KDD 2016 Best Paper Award (Research Track)
- CS5228: Knowledge Discovery and Data Mining