Yu Wang

Yu Wang serves as an Assistant Professor in the Department of Computer and Information Science at the University of Oregon's School of Computer and Data Sciences, where he leads research in graph-based machine learning and AI systems. Joining the faculty in September 2024 after completing his PhD at Vanderbilt University, he directs the Graph Machine Learning Lab and actively recruits PhD students for cutting-edge projects at the intersection of data mining, network analysis, and trustworthy AI.

His educational background includes a Ph.D. in Computer Science from Vanderbilt University (2019-2024) and a B.S. from Harbin Institute of Technology (2015-2019). During his doctoral studies, he completed research internships at Adobe Research and The Home Depot, focusing on knowledge graph applications for information retrieval.

Wang's research centers on graph machine learning, with emphasis on neural-symbolic learning, LLM integration with structured knowledge, and trustworthy AI systems. His work addresses critical challenges in imbalanced/biased graph learning, generative graph models, and social network analysis, driving applications in cyber-security, biochemistry, and infrastructure systems. Current projects explore agentic social simulations and spatial-temporal machine learning for real-world impact.

His publication trajectory shows increasing focus on LLM-graph integration and trustworthy AI, with recent work spanning generative graph models, personalized LLMs, and robust networking systems. Key trends include bridging symbolic reasoning with neural approaches, advancing evaluation benchmarks for social network analysis, and developing privacy-preserving techniques for graph-based retrieval systems.

• Outstanding Doctoral Student Award (Vanderbilt, 2023-2024)
• Best Paper Award at NeurIPS GLFrontiers Workshop (2023)
• Vanderbilt Graduate Leadership Anchor Award for Research (2023)
• KDD Outstanding Dissertation Award Honorable Mention (2025)
• NSF IIS-III Core Program Grant as Lead PI (2025)
• Top-10 Most Influential Papers at CIKM'22 and WWW'23

Wang mentors PhD students including Yongjia Lei (SDM Doctoral Forum Honorable Mention recipient) and Riya (Pulse Research Fellow), with research funded through NSF grants and industrial collaborations with Adobe, Visa, and Home Depot. His lab provides substantial computational resources including L40S GPUs and OpenAI API access, supporting both theoretical innovation and real-world deployments through industry internships. Current advising focuses on generative graph models, LLM-agent collaboration, and trustworthy AI frameworks for societal applications.

The Graph Machine Learning Lab maintains strong ties with industrial research groups, facilitating student internships at Adobe Research and other tech companies while developing open-source tools like ChemicalX for drug discovery. Ongoing projects include GraphRAG frameworks for security applications and social simulation environments for studying network dynamics.