Jian Pei

Arthur S. Pearse Distinguished Professor of Computer Science

Data science, data mining, databases, information retrieval, computational statistics, applied machine learning and AI.

Appointments and Affiliations

  • Arthur S. Pearse Distinguished Professor of Computer Science
  • Professor of Computer Science
  • Chair of the Department of Computer Science
  • Professor of Electrical and Computer Engineering
  • Professor of Biostatistics & Bioinformatics

Contact Information

  • Office Location: 308 Research Drive, Durham, NC 27708
  • Email Address: j.pei@duke.edu
  • Websites:

Education

  • Ph.D. Simon Fraser University, 2002

Courses Taught

  • BRAINSOC 395T: Bass Connections in Brain & Society Research Team
  • BRAINSOC 795T: Bass Connections in Brain & Society Research Team
  • CBB 526: Data Science
  • CBB 590: Special Topics in Computational Biology
  • COMPSCI 391: Independent Study
  • COMPSCI 393: Research Independent Study
  • COMPSCI 394: Research Independent Study
  • COMPSCI 526: Data Science
  • COMPSCI 590: Advanced Topics in Computer Science
  • ECE 583: Data Science
  • ECE 590: Advanced Topics in Electrical and Computer Engineering
  • ECE 899: Special Readings in Electrical Engineering

In the News

Representative Publications

  • Wu, N., M. Gong, L. Shou, J. Pei, and D. Jiang. “RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation.” In International Conference on Information and Knowledge Management, Proceedings, 4871–78, 2023. https://doi.org/10.1145/3583780.3615498.
  • Shi, H., M. A. Tayebi, J. Pei, and J. Cao. “Cost-Sensitive Learning for Medical Insurance Fraud Detection With Temporal Information.” IEEE Transactions on Knowledge and Data Engineering 35, no. 10 (October 1, 2023): 10451–63. https://doi.org/10.1109/TKDE.2023.3240431.
  • Jiang, H., H. Yu, X. Cheng, J. Pei, R. Pless, and J. Yu. “DP2-Pub: Differentially Private High-Dimensional Data Publication With Invariant Post Randomization.” IEEE Transactions on Knowledge and Data Engineering 35, no. 10 (October 1, 2023): 10831–44. https://doi.org/10.1109/TKDE.2023.3265605.
  • Wu, L., J. Pei, J. Tang, Y. Xia, and X. Guo. “Deep Learning on Graphs: Methods and Applications (DLG-KDD2023).” In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 5891–92, 2023. https://doi.org/10.1145/3580305.3599207.
  • Wu, L., P. Cui, J. Pei, L. Zhao, and X. Guo. “Graph Neural Networks: Foundation, Frontiers and Applications.” In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 5831–32, 2023. https://doi.org/10.1145/3580305.3599560.