Kuang Xu

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
• optimization
• operations research and management

Bio

Professor Xu received his PhD and SM in Electrical Engineering and Computer Science from MIT, in 2014 and 2011, respectively, and his B.S. in Electrical Engineering from the University of Illinois at Urbana-Champaign in 2009. He was a postdoctoral fellow at the Microsoft Research - Inria Joint Center in Palaiseau, France during the academic year 2014-2015.

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

• 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

Teaching

Degree Courses

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.

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.