Andy Sun is an Affiliate Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. His research interests encompass a wide range of applications in optimization, particularly focusing on deterministic optimization problems. Dr. Sun has worked extensively on solving nonconvex optimization problems within network structures, such as optimizing network flows under nonconvex flow laws, exploring nonconvex quadratic programming, and employing consensus optimization and market matching techniques. He is also developing distributed algorithms aimed at solving nonconvex problems with convergence guarantees. His contributions include work on certifying the existence and uniqueness of solutions for polynomial systems within network structures, integrating complex analysis and algebraic geometric methods. Furthermore, Dr. Sun's research into optimization under dynamical constraints connects network flow issues with ordinary and partial differential equations. His contributions to uncertain optimization include devising efficient algorithms for two-stage robust optimization problems, notably outperforming traditional methods like Benders decomposition, as well as advancements in multistage robust linear optimization. Dr. Sun has collaborated with major utility companies on robust optimization in power system operations planning, gaining attention from both industry and academia. His educational background includes a Bachelor's in Electronic Engineering from Tsinghua University in Beijing, a Master's in Media Arts and Sciences from MIT, and a PhD in Operations Research from MIT, followed by a postdoctoral position at IBM T. J. Watson Research Center.