Find my full CV here.

Education

• Ph.D. in Industrial & Operations Engineering, University of Michigan - Ann Arbor, 2023
• M.S. in Statistics, University of Michigan - Ann Arbor, 2023
• M.S. in Industrial & Management Engineering, Korea University, 2018
• B.S. in Industrial & Management Engineering, Korea University, 2016

Work experience

• 2023 - present: Assistant Professor
  • Systems & Information Engineering, University of Virginia
• 2018 - 2023: Graduate Research Assistant
  • Industrial & Operations Engineering, University of Michigan - Ann Arbor
• 2016 - 2018: Graduate Research Assistant
  • Industrial & Management Engineering, Korea University

Selected Publications

Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes

Chung, S., & Kontar, R. A. (2024). Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes. arXiv preprint arXiv:2403.16377.

Federated Multi-output Gaussian Processes

Chung, S. & Kontar, R. (2023) Federated Multi-output Gaussian Processes. Technometrics. Advance online publication. DOI: 10.1080/00401706.2023.2238834.

Federated Condition Monitoring Signal Prediction with Improved Generalization

Chung, S. & Al Kontar, R. Federated Condition Monitoring Signal Prediction With Improved Generalization. IEEE Transactions on Reliability. Advance online publication. doi: 10.1109/TR.2023.3283348.

Weakly Supervised Multi-output Regression via Correlated Gaussian Processes

Chung, S., Al Kontar, R., & Wu, Z. (2022). Weakly Supervised Multi-output Regression via Correlated Gaussian Processes. INFORMS Journal on Data Science 1(2):115-137.

Federated Condition Monitoring Signal Prediction With Improved Generalization

Chung, S., Chou, C. H., Fang, X., Al Kontar, R., & Okwudire, C. (2022). A Multi-Stage Approach for Knowledge-Guided Predictions With Application to Additive Manufacturing. IEEE Transactions on Automation Science and Engineering, 19(3), 1675-1687.

Functional Principal Component Analysis for Extrapolating Multistream Longitudinal Data

Chung, S. & Kontar, R. (2021). Functional principal component analysis for extrapolating multistream longitudinal data. IEEE Transactions on Reliability, 70(4), 1321-1331.

Look-ahead decision making for renewable energy: A dynamic “predict and store” approach

Wang, J., Chung, S., AlShelahi, A., Kontar, R., Byon, E., & Saigal, R. (2021). Look-ahead decision making for renewable energy: A dynamic “predict and store” approach. Applied Energy, 296, 117068.

A mathematical programming approach for integrated multiple linear regression subset selection and validation

Chung, S., Park, Y. W., & Cheong, T. (2020). A mathematical programming approach for integrated multiple linear regression subset selection and validation. Pattern Recognition, 108, 107565.

Awards

• QCRE Best paper competition finalist (general track), “Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes,” 2024 IISE Annual Conference.
• QSR Best paper competition finalist (general track), “Federated Multi-output Gaussian Processes,” 2022 INFORMS Annual Conference.
• QCRE Best paper finalist (student track), “Weakly-supervised Multi-output Regression via Correlated Gaussian Processes,” 2021 IISE Annual Conference.
• Featured Article, “The Internet of Federated Things (IoFT),” IEEE Access.
• Rackham Predoctoral Fellowship, University of Michigan, 2022-2023. [Link]

Teaching

• SYS 6021: Statistical Modeling I (FALL 2023)

Service, Volunteering, and Activities

• Reviewer for AISTATS 2022/23, IEEE Transactions on Reliability, IEEE Transactions on Automation Science and Engineering, IEEE/ASME Transactions on Mechatronics, and Journal of Intelligent Manufacturing, and IISE Transactions.