Jun Wu

Jun Wu is an Assistant Professor in the Department of Computer Science and Engineering at Michigan State University (MSU), part of the College of Engineering. He holds a Ph.D. in Computer Science from the University of Illinois Urbana-Champaign. His research focuses on trustworthy machine learning, transfer learning/domain adaptation, and graph learning, with applications in agriculture, bioinformatics, e-commerce, and legal analytics. His work has been published in top-tier venues like NeurIPS, ICML, KDD, and TKDE.

Education: Ph.D. in Computer Science, University of Illinois Urbana-Champaign. Prior to MSU, his academic trajectory included significant contributions to machine learning theory and applications.

Research Interests:

• Trustworthy AI mechanisms under distribution shifts
• Graph-based transfer learning frameworks
• Adversarial robustness in federated learning
• Domain adaptation for heterogeneous data

Publications reflect a strong emphasis on foundational machine learning challenges, particularly in ensuring reliability and adaptability across domains. Recent work addresses robustness in large language models, federated learning resilience, and graph-structured data analysis.

No awards or grants explicitly listed in the provided text. Active in advising doctoral students but none listed here.