Jiaming Xu

Associate Professor of Business Administration

Jiaming Xu is an Associate Professor in the Decision Sciences area.  His research focus is on the intersection of computation and statistics. Professor Xu seeks to understand the deep interplay between statistical optimality and computational complexity in high-dimensional statistical inference problems. He has been working on sharp performance analysis of semidefinite programming relaxations and belief propagation for community detection. Professor Xu teaches Decision Analytics and Modeling.

Appointments and Affiliations

  • Associate Professor of Business Administration
  • Assistant Professor in the Department of Electrical and Computer Engineering
  • Faculty Network Member of the Duke Institute for Brain Sciences

Contact Information

Education

  • B.S.E. Tsinghua University (China), 2009
  • M.S. University of Texas, Austin, 2011
  • Ph.D. University of Illinois, Urbana-Champaign, 2014

Research Interests

Network science, machine learning, high-dimensional statistical inference, information theory, optimization, stochastic systems, game theory, communications and networking

Courses Taught

  • BA 990: Selected Topics in Business
  • BA 996: Curricular Practical Training
  • DECISION 521Q: Decision Analytics and Modeling
  • DECISION 546Q: Modern Analytics
  • DECISION 611: Decision Models
  • DECISION 611W: Decision Models
  • ECE 493: Projects in Electrical and Computer Engineering
  • ECE 590: Advanced Topics in Electrical and Computer Engineering

In the News

Representative Publications

  • Xu, J., K. Xu, and D. Yang. “Learner-Private Convex Optimization.” In Ieee Transactions on Information Theory, 69:528–47, 2023. https://doi.org/10.1109/TIT.2022.3203989.
  • Mao, Cheng, Yihong Wu, Jiaming Xu, and Sophie H. Yu. “Random graph matching at Otter's threshold via counting chandeliers,” September 25, 2022.
  • Wu, Y., J. Xu, and S. H. Yu. “Settling the Sharp Reconstruction Thresholds of Random Graph Matching.” Ieee Transactions on Information Theory 68, no. 8 (August 1, 2022): 5391–5417. https://doi.org/10.1109/TIT.2022.3169005.
  • Yu, Liren, Jiaming Xu, and Xiaojun Lin. “SeedGNN: Graph Neural Networks for Supervised Seeded Graph Matching,” May 26, 2022.
  • Hsu, W. K., J. Xu, X. Lin, and M. R. Bell. “Integrated Online Learning and Adaptive Control in Queueing Systems with Uncertain Payoffs.” Operations Research 70, no. 2 (March 1, 2022): 1166–81. https://doi.org/10.1287/opre.2021.2100.