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Kuang Xu
Kuang Xu
Assistant Professor, Operations, Information & Technology
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KuangXu
Assistant Professor of Operations, Information & Technology
Assistant Professor of Electrical Engineering (by courtesy), School of Engineering
GSB Trust Faculty Scholar for 2018-2019
Academic Area: 
Operations, Information & Technology

Research Statement

Professor Xu's research focuses on the analysis, design, and decision making in stochastic systems. Recently, he has been interested in the use of predictive information and flexibility in improving the performance of large-scale dynamic resource allocation systems. Application areas of his research include stochastic scheduling, queueing systems, and health care operations.

Research Interests

  • stochastic modeling
  • probability theory
  • dynamic resource allocation
  • queueing networks
  • optimization
  • operations research and management
  • privacy
  • statistical learning theory

Bio

Professor Kuang Xu was born in Suzhou, China. He received the B.S. degree in Electrical Engineering (2009) from the University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, and the Ph.D. degree in Electrical Engineering and Computer Science (2014) from the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. He was a postdoctoral fellow at the Microsoft Research-Inria Joint Center in Paris, France (2014-2015). 

His research interests lie in the fields of applied probability theory, optimization, and operations research, seeking to understand fundamental properties and design principles of large-scale stochastic systems, with applications in queueing networks, healthcare, privacy and statistical learning theory. He has received several awards including a First Place in INFORMS George E. Nicholson Student Paper Competition, a Best Paper Award, as well as a Kenneth C. Sevcik Outstanding Student Paper Award from ACM SIGMETRICS.

Academic Degrees

  • PhD, Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2014
  • SM, Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2011
  • BS, Electrical Engineering, University of Illinois at Urbana-Champaign, 2009

Awards and Honors

  • GSB Trust Faculty Scholar, 2018-2019
  • Jin Au Kong Award for Best PhD Thesis in Electrical Engineering, MIT, 2014
  • Dimitris N. Chorafas Foundation award for outstanding PhD research, 2014
  • Best Paper Award, as well as a Kenneth C. Sevcik Outstanding Student Paper Award, from ACM SIGMETRICS, 2013
  • First-place winner of the INFORMS George E. Nicholson Student Paper Competition, 2011
  • Ernst A. Guillemin Thesis Award for Best SM Thesis in Electrical Engineering, MIT, 2011

Publications

Journal Articles

Delay-Predictability Trade-offs in Reaching a Secret Goal
John N. Tsitsiklis, Kuang Xu. Operations Research. March 2018, Vol. 66, Issue 2, Pages 587-596.
On the Capacity of Information Processing Systems
Laurent Massoulié, Kuang Xu. Operations Research. March 2018, Vol. 66, Issue 2, Pages 568-586.
Flexible Queueing Architectures
John N. Tsitsiklis, Kuang Xu. Operations Research. July 21, 2017.
Using Future Information to Reduce Waiting Times in the Emergency Department via Diversion
Kuang Xu, Carri Chan. Manufacturing & Service Operations Management (MSOM). 2016, Vol. 18, Issue 3, Pages 314-333.
Necessity of future information in admission control
Kuang Xu. Operations Research. September 2015, Vol. 63, Issue 5, Pages 1213 - 1226.
Queueing with future information
Joel Spencer, Madhu Sudan, Kuang Xu. Annals of Applied Probability. 2014, Vol. 24, Issue 5, Pages 2091-2142.
On the Power of (Even a Little) Resource Pooling
John N. Tsitsiklis, Kuang Xu. Stochastic Systems. 2012, Vol. 2, Issue 1.

Other Publications

Private sequential learning
John Tsitsiklis, Kuang Xu, Zhi Xu. Proceedings of Conference on Learning Theory (COLT) . Stockholm: Proceedings of Machine Learning Research, July 2018, Vol. 75.
Reinforcement with fading memories
Kuang Xu, Se-Young Yun. Proceedings of ACM SIGMETRICS. July 2018.

Teaching

Degree Courses

2018-19

OIT 247: Optimization and Simulation Modeling - Accelerated

The course is aimed at students who already have a background or demonstrated aptitude for quantitative analysis, and thus are comfortable with a more rapid coverage of the topics, in more depth and breadth, than in OIT 245.

2017-18

OIT 247: Optimization and Simulation Modeling - Accelerated

The course is aimed at students who already have a background or demonstrated aptitude for quantitative analysis, and thus are comfortable with a more rapid coverage of the topics, in more depth and breadth, than in OIT 245.

2016-17

OIT 247: Optimization and Simulation Modeling - Accelerated

The course is aimed at students who already have a background or demonstrated aptitude for quantitative analysis, and thus are comfortable with a more rapid coverage of the topics, in more depth and breadth, than in OIT 245.

Stanford University Affiliations

Greater Stanford University

  • Assistant Professor (by courtesy), Stanford Electrical Engineering Department

Service to the Profession

  • Reviewer for Operations Research, Management Science, Mathematics of Operations Research, and Annals of Applied Probability.

Insights by Stanford Business

writtenPredictive Data Can Reduce Emergency Room Wait Times
October 11, 2016
Anticipating ER traffic jams before they begin can save lives.

School News

writtenStanford GSB Welcomes New Faculty to Enrich, Broaden its Educational Experience
October 6, 2015
New faculty and lecturers offer unique perspectives and experience to augment program offerings.

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