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 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

  • Chen, Yudong, Xiaodong Li, and Jiaming Xu. “Convexified modularity maximization for degree-corrected stochastic block models.” The Annals of Statistics 46, no. 4 (August 1, 2018). https://doi.org/10.1214/17-aos1595.
  • Xu, Jiaming, Bruce Hajek, and Yihong Wu. “Recovering a hidden community beyond the Kesten-Stigum threshold in O(|E|log*|V) time.” Journal of Applied Probability 55, no. 2 (July 15, 2018): 325–52.
  • Banks, Jess, Cristopher Moore, Roman Vershynin, Nicolas Verzelen, and Jiaming Xu. “Information-Theoretic Bounds and Phase Transitions in Clustering, Sparse PCA, and Submatrix Localization.” Ieee Transactions on Information Theory 64, no. 7 (July 2018): 4872–94. https://doi.org/10.1109/tit.2018.2810020.
  • Xu, Jiaming, Bruce Hajek, and Yihong Wu. “Submatrix localization via message passing.” Journal of Machine Learning Research 18, no. 186 (April 15, 2018): 1–52.
  • Hajek, Bruce, Yihong Wu, and Jiaming Xu. “Information Limits for Recovering a Hidden Community.” Ieee Transactions on Information Theory 63, no. 8 (August 2017): 4729–45. https://doi.org/10.1109/tit.2017.2653804.