Kuang Xu is an Associate Professor of Operations, Information and Technology at Stanford Graduate School of Business, and Associate Professor by courtesy with the Electrical Engineering Department, Stanford University. Born in Suzhou, China, he received the B.S. degree in Electrical Engineering (2009) from the University of Illinois at Urbana-Champaign, and the Ph.D. degree in Electrical Engineering and Computer Science (2014) from the Massachusetts Institute of Technology.

His research primarily focuses on understanding fundamental properties and design principles of large-scale stochastic systems using tools from probability theory and optimization, with applications in queueing networks, healthcare, privacy and machine learning. He received First Place in the INFORMS George E. Nicholson Student Paper Competition (2011), the Best Paper Award, as well as the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS (2013), and the ACM SIGMETRICS Rising Star Research Award (2020). He currently serves as an Associate Editor for Operations Research and Management Science.

Academic Appointments

  • Associate Professor, Operations, Information & Technology
  • Associate Professor (By courtesy), Electrical Engineering

Administrative Appointments

  • Associate Editor, Management Science (2021 - Present)
  • Associate Editor, Operations Research (2018 - Present)

Honors & Awards

  • Rising Star Research Award, ACM SIGMETRICS (2020)
  • Best Paper Award, SIGMETRICS Conference (2013)
  • Kenneth C. Sevcik Outstanding Student Paper Award, SIGMETRICS Conference (2013)
  • First Place, George E. Nicholson Student Paper Competition, INFORMS (2011)

Professional Education

  • Ph.D., Massachusetts Institute of Technology, Electrical Engineering and Computer Science (2014)
  • S.M., Massachusetts Institute of Technology, Electrical Engineering and Computer Science (2011)
  • B.S., University of Illinois at Urbana-Champaign, Electrical Engineering (2009)

2023-24 Courses

Stanford Advisees

All Publications

  • Experimenting in Equilibrium MANAGEMENT SCIENCE Wager, S., Xu, K. 2021; 67 (11): 6694-6715
  • Private Sequential Learning OPERATIONS RESEARCH Tsitsiklis, J. N., Xu, K., Xu, Z. 2021; 69 (5): 1575-1590
  • Temporal concatenation for Markov decision processes PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES Song, R., Xu, K. 2021
  • Optimal query complexity for private sequential learning against eavesdropping Xu, J., Xu, K., Yang, D., Banerjee, A., Fukumizu, K. MICROTOME PUBLISHING. 2021
  • Learner-Private Convex Optimization Xu, J., Xu, K., Yang, D., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021
  • Reinforcement with Fading Memories MATHEMATICS OF OPERATIONS RESEARCH Xu, K., Yun, S. 2020; 45 (4): 1258–88
  • Information and Memory in Dynamic Resource Allocation OPERATIONS RESEARCH Xu, K., Zhong, Y. 2020; 68 (6): 1698–1715
  • No Detectable Surge in SARS-CoV-2 Transmission Attributable to the April 7, 2020 Wisconsin Election. American journal of public health Leung, K. n., Wu, J. T., Xu, K. n., Wein, L. M. 2020: e1–e2


    The April 7, 2020, Wisconsin election produced a large natural experiment to help understand the transmission risks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of April 14, 2020, 1 551 711 total votes were cast (,1 and 1 138  491 absentee ballots were returned as of April 21, 2020,1 suggesting that approximately 413 220 people voted in person. Waiting times in Milwaukee averaged 1.5 to 2 hours.2 Poll workers had surgical masks and latex gloves, hand sanitizer was made available to voters, isopropyl alcohol wipes were used to clean voting equipment, and painting tape and signs were used to facilitate social distancing.1 (Am J Public Health. Published online ahead of print June 18, 2020: e1-e2. doi:10.2105/AJPH.2020.305770).

    View details for DOI 10.2105/AJPH.2020.305770

    View details for PubMedID 32552029

  • Delay-Predictability Trade-offs in Reaching a Secret Goal Operations Research Tsitsiklis, J. N., Xu, K. 2018

    View details for DOI 10.1287/opre.2017.1682

  • Query Complexity of Bayesian Private Learning Xu, K., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
  • On the Capacity of Information Processing Systems Operations Research Massoulié, L., Xu, K. 2018

    View details for DOI 10.1287/opre.2017.1680

  • Flexible Queueing Architectures Operations Research Tsitsiklis, J. N., Xu, K. 2017; 65 (5)

    View details for DOI 10.1287/opre.2017.1620

  • Using Future Information to Reduce Waiting Times in the Emergency Department via Diversion M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT Xu, K., Chan, C. W. 2016; 18 (3): 314-331
  • Necessity of Future Information in Admission Control OPERATIONS RESEARCH Xu, K. 2015; 63 (5): 1213-1226
  • Queuing with future information The Annals of Applied Probability Spencer, J., Sudan, M., Xu, K. 2014; 24 (5): 2091-2142

    View details for DOI 10.1214/13-AAP973

  • On the Power of (Even a Little) Resource Pooling Stochastic Systems Tsitsiklis, J. N., Xu, K. 2012; 2 (1): 1-66

    View details for DOI 10.1287/11-SSY033

  • Self-synchronizing properties of CSMA wireless multi-hop networks ACM Sigmetrics Xu, K., Dousse, O., Thiran, P. 2010

    View details for DOI 10.1145/1811039.1811048

  • On the capacity of information processing systems 29th Annual Conference on Learning Theory (COLT) Massoulié, L., Xu, K. 2016: 1292–97