Bryan HOOI Kuen-Yew

Bryan HOOI Kuen-Yew is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), affiliated with the School of Computing and the Institute of Data Science. His research focuses on enhancing the reliability and applicability of machine learning systems, particularly in trustworthiness, graph learning, and real-world applications such as cybersecurity and biomedical informatics.

He holds a PhD in Machine Learning from Carnegie Mellon University (2019), and dual MSc (Computer Science) and BSc (Mathematics) degrees from Stanford University (2014). His work has been recognized with awards including the KDD 2016 Best Paper Award (Research Track) and the ECML-PKDD 2018 Runner-Up Best Student Data Mining Paper Award.

Research Interests:

• Trustworthy AI: Mitigating hallucinations, biases, and distribution shifts in large language models.
• Graph Algorithms: Developing robust methods for graph mining, anomaly detection, and fraud detection in dynamic networks.
• Multimodal Learning: Leveraging vision-language models and knowledge graphs for improved reasoning and security.
• Biomedical Applications: Applying AI to healthcare challenges, including drug design and medical data analysis.

Selected Awards:

• ECML-PKDD 2018 Runner-Up Best Student Data Mining Paper Award
• KDD 2016 Best Paper Award (Research Track)
• NUS Computing Teaching Excellence Award (2024)

Advising and Grants: Actively supervises PhD and master’s students, with research supported by grants focusing on AI security, graph learning, and trustworthy systems. Collaborates on projects such as PhishAgent (multimodal phishing detection) and UniGraph (cross-domain graph foundation models).