SangWoo Park

SangWoo Park received the B.S. degree in environmental engineering from Johns Hopkins University, Baltimore, MD, USA, in 2016, and the M.S. and Ph.D. degrees in industrial engineering and operations research from the University of California, Berkeley, Berkeley, CA, USA, in 2017 and 2022, respectively. He is currently an Assistant Professor in mechanical and industrial engineering with the New Jersey Institute of Technology, Newark, NJ, USA. He was a recipient of the Best Student Paper Award of 2020 American Control Conference.

Ph.D.; University of California-Berkeley; Industrial Engineering and Operations Research; 2022

M.S.; University of California-Berkeley; Industrial Engineering and Operations Research; 2017

B.S.; Johns Hopkins University; Environmental Engineering; 2016

Monday 10:30am - 12:00pm

https://sangwoopark-njit.github.io/

IE 441 - INFO & KNOW ENGR SYSTEMS

IE 790A - DOCTORAL DISSERTATION

EM 726 - INDEPENDENT STUDY II

IE 700B - MASTER'S PROJECT

IE 701C - MASTER'S THESIS

IE 726 - INDEPENDENT RESEARCH II

EM 701B - MASTER'S THESIS

IE 481 - INVSTGTNS IN INDS ENGR I

IE 725 - INDEPENDENT RESEARCH I

EM 700B - MASTER'S PROJECT

IE 701B - MASTER'S THESIS

EM 701C - MASTER'S THESIS

EM 725 - INDEPENDENT STUDY I

IE 792B - PRE DOCTORAL RESEARCH

Management of information systems, data analytics, statistical learning, linear optimization, nonlinear optimization, integer optimization, convex optimization, power systems operations and design.

Design and analysis of algorithms for complex and large scale optimization problems, mainly related to electric power systems. Other application areas include transportation systems, healthcare systems and supply chains. Current research topics include: reinforcement learning for the optimal pricing of incentives in demand response with heterogeneous consumers; convolutional neural network for power system state estimation; anomaly detection in power systems using context-agnostic learning; optimizing routes for the multi pickup and delivery problem with time windows, dynamic power grid using swarm robotics, dynamic bin allocation problem for parcel sorting.

Modeling circular supply chains resilience: A case study in fashion industry
23rd Working Seminar on Production Economics, February 2024

Modeling Post-disruption Equilibrium for Circular Supply Chains and New Measures for Resilience
International Material Handling Research Colloquium (IMHRC)/The College Industry Council on Material Handling Education (CICMHE)/Progress in Material Handling Research, June 2023

Distributed Power System State Estimation Using Graph Convolutional Neural Networks
Proceedings of the Hawaii International Conference on System Sciences, January (1st Quarter/Winter) 2023

Diminishing regret for online nonconvex optimization
2021

Homotopy method for finding the global solution of post-contingency optimal power flow
2020

Zhang, Haixiang, & Park, SangWoo, & Lavaei, Javad, & Baldick, Ross (2022). Uniqueness of power flow solutions using graph-theoretic notions. IEEE Transactions on Control of Network Systems, 9(1), 100--112.

Park, SangWoo, & Glista, Elizabeth, & Lavaei, Javad, & Sojoudi, Somayeh (2022). An Efficient Homotopy Method for Solving the Post-Contingency Optimal Power Flow to Global Optimality. IEEE Access/IEEE, 10, 124960 - 124978.

Mulvaney-Kemp, Julie, & Park, SangWoo, & Jin, Ming, & Lavaei, Javad (2022). Dynamic Regret Bounds for Constrained Online Nonconvex Optimization Based on Polyak–Lojasiewicz Regions. IEEE Transactions on Control of Network Systems/IEEE, 10(2), 599 - 611.

Park, SangWoo, & Mohammadi-Ghazi, Reza, & Lavaei, Javad (2021). Nonlinear Least Absolute Value Estimator for Topology Error Detection and Robust State Estimation. IEEE Access, 9, 137198--137210.

Park, SangWoo, & Zhang, Richard Y, & Lavaei, Javad, & Baldick, Ross (2020). Uniqueness of power flow solutions using monotonicity and network topology. IEEE Transactions on Control of Network Systems, 8(1), 319--330.

Park, SangWoo, & Xu, Qingyu, & Hobbs, Benjamin F (2019). Comparing scenario reduction methods for stochastic transmission planning. IET Generation, Transmission \& Distribution, 13(7), 1005--1013.

Hobbs, Benjamin F, & Xu, Qingyu, & Ho, Jonathan, & Donohoo, Pearl, & Kasina, Saamrat, & Ouyang, Jasmine, & Park, Sang Woo, & Eto, Joseph, & Satyal, Vijay (2016). Adaptive transmission planning: implementing a new paradigm for managing economic risks in grid expansion. IEEE Power and Energy Magazine, 14(4), 30--40.

Ho, Jonathan L, & Hobbs, Benjamin F, & Donohoo-Vallett, Pearl, & Xu, Qingyu, & Kasina, Saamrat, & Park, SangWoo, & Ouyang, Yueying (2016). Planning transmission for uncertainty: Applications and lessons for the western interconnection, .