Dr. Jun Wu is an Assistant Professor in the Department of Computer Science and Engineering at the Michigan State University (MSU). Before joining MSU, he received his Ph.D. degree in Computer Science at the University of Illinois Urbana-Champaign. He has a broad research interest in trustworthy machine learning, transfer learning/domain adaptation, and graph learning, with applications in agriculture, bioinformatics, e-commerce, and legal analytics. He has multiple publications at major peer-reviewed conferences and journals (e.g., ICML, AISTATS, NeurIPS, KDD, AAAI, TKDE, etc.). He has also served as a (senior) program committee member in ML/AI/DM conferences (e.g., ICML, NeurIPS, ICLR, AISTATS, KDD, AAAI, IJCAI, etc.). His work has been recognized by the 2025 AAAI New Faculty Highlights.

To prospective students: I am seeking self-motivated students to join my research group. If you are interested in working with me, please drop me an email with your CV, transcript, and a brief overview of your research experience. Find more information here.

Research

I am particularly interested in exploring data heterogeneity and ensuring model trustworthiness in machine learning, artificial intelligence, and data mining. Currently, my research includes, but is not limited to, the following topics:

• Heterogeneous machine learning, e.g., transfer learning, domain adaptation, out-of-distribution generalization, etc.
• Trustworthy machine learning, e.g., adversarial robustness, privacy, fairness, transparency, etc.
• Graph machine learning, e.g., network of networks, heterogeneous information network, graphon modeling, etc.

News

• [01/2025] One paper has been accepted by AISTATS’25
• [12/2024] Honored to be selected as one of the AAAI-25 New Faculty Highlights
• [11/2024] Invited to serve as a Program Committee member for ICML’25 and IJCAI’25
• [11/2024] Invited to serve as a Program Committee member for AISTATS’25