FANG Yuan

FANG Yuan is a tenured Associate Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). He holds the prestigious Lee Kong Chian Fellowship and leads research in artificial intelligence and data science. His institutional affiliation includes:

• School of Computing and Information Systems, Singapore Management University

Education:

• Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2014)
• Bachelor of Computing (First Class Honors), National University of Singapore (2009) - Top student in Computer Science

Research Focus: Dr. FANG specializes in data mining, machine learning, and AI with emphasis on graph learning, information networks, recommendation systems, and knowledge graph applications. His work bridges theoretical foundations with practical applications in social analytics, biomedical informatics, and digital transformation, often employing advanced neural network architectures.

Publication Trends: Recent works (2024-2025) demonstrate strong focus on graph machine learning innovations, including graph foundation models, prompt-based learning for dynamic graphs, and LLM-graph integrations. Key themes include few-shot/zero-shot learning, non-homophilic graph processing, and applications in recommendation systems, bioinformatics, and NLP. Methodological advancements frequently involve contrastive learning, transformer architectures, and explainable AI techniques.

Awards & Honors:

• Lee Kong Chian Fellow
• World's Top 2% Scientist (2024) by Stanford/Elsevier
• #1 Most Influential Paper at WWW'23 (GraphPrompt) - Paper Digest (2024-09)
• Top 5 Most Influential Papers at WWW'23 (GraphPrompt) - Paper Digest (2024-05)
• Top Computer Science Graduate, NUS (2009)

Student Advising: Currently advises doctoral candidates including DONG Viet Hoang, LIU Ran, and NIU Yudong. Recently supervised Dr. Zhongzhou Liu's successful PhD defense (2024) on trustworthy recommendation systems.

Professional Engagement: Regularly organizes tutorials at premier venues (WWW, KDD) and delivers invited talks internationally on graph learning advancements. Leads multiple research projects in collaboration with industry partners.