Prior to that he was an instructor in applied mathematics at MIT. Yuan received his Ph.D. from Carnegie Mellon University in 2014. His research interests span theoretical computer science and operations research with emphasis on linear programming and semidefinite programming relaxations, discrete optimization, approximation algorithms and hardness of approximation, harmonic analysis of discrete functions, process flexibility and decision under uncertainty with applications to crowdsourcing.