Sun’s research focuses on both theory and applications of deterministic optimization and optimization under uncertainty. Much of his research concerns applications for energy systems, including resiliency of power systems.

He has developed one of the first efficient algorithms for solving two-stage robust optimization problems that are significantly faster than Benders’ decomposition; cutting plane methods for multistage robust linear optimization that are much more scalable than the duality approach; models for decision-dependent uncertainty; and a fast exact algorithm (SDDiP) for multistage stochastic integer linear programming. Sun’s team is working toward developing new algorithms and complexity analysis for multistage stochastic and distributionally robust programs with integer decisions and nonlinear constraints, as well as first-order methods for nonconvex distributed and nonlinear optimization.

In particular, Sun has received recognition for his research on applications for energy systems. A Georgia Tech team led by Sun won third place in the 2020 ARPA-E Grid Optimization Competition for solving large-scale optimization problems for electric power grid operation. Additional awards include:

• Best Paper Award for publications in IEEE Transactions on Power Systems (2017-19);
• INFORMS Energy, Natural Resources, and Environment (ENRE) Best Paper Award in Energy (2017, 2019);
• Best Paper Award in the Hawaii International Conference on System Sciences (HICSS) Energy Track (2018).

Sun also received an NSF CAREER grant in 2018.