Ph.D., Industrial & Operations Engineering, University of Michigan, 2023M.S., Statistics, University of Michigan, 2023M.S., Industrial Management Engineering, Korea University, 2018B.S., Industrial Management Engineering, Korea University, 2016
My research focuses on the Internet of Things (IoT)-enabled systems, where multiple entities/units (e.g., vehicles, wearable devices, etc.) collect data and collaborate to establish enhanced smart analytics based on their connectivity. I enjoy exploring data-driven methods such as federated learning, multi-task learning, and Bayesian probabilistic modeling, to tackle key challenges arising in data analytics for connected systems, including statistical/systems heterogeneity, fast personalization, and inscalability. As such, I aim to advance smart & connected healthcare and manufacturing systems and their reliability.
Quality, Statistics, and Reliability (QSR) Best Paper Finalist (General track), INFORMS Annual Conference.2022
Quality Control & Reliability Engineering (QCRE) Best Student Paper Finalist, IISE Annual Conference.2021
Rackham Predoctoral Fellowship2022
Bayesian Probabilistic Modeling
Smart Manufacturing Systems and Reliability
Smart and Connected Health
Federated Multi-output Gaussian Processes Chung, S. and Kontar, R. (2023+), Technometrics (accepted)
Federated Condition Monitoring Signal Prediction with Improved Generalization Chung, S. and Kontar, R. (2023+) IEEE Transactions on Reliability (accepted)
Weakly supervised multi-output regression via correlated gaussian processes. Chung, S., Al Kontar, R., & Wu, Z. (2022). INFORMS Journal on Data Science, 1(2), 115-137
Functional principal component analysis for extrapolating multistream longitudinal data Chung, S., & Kontar, R. (2021). IEEE Transactions on Reliability, 70(4), 1321-1331.