Sungho Shin

Ph.D. Candidate (Advisor: Victor M. Zavala)

Department of Chemical and Biological Engineering, University of Wisconsin-Madison

2011 Engineering Hall, 1415 Engineering Drive, Madison, WI 53706, USA

Bio

I am currently a Ph.D. candidate in the Department of Chemical and Biological Engineering at the University of Wisconsin-Madison. I spent 2020 Summer at Los Alamos National Laboratory (worked with Carleton Coffrin and Kaarthik Sundar) and 2018 Summer at Argonne National Laboratory (worked with Mihai Anitescu). My research interests include model predictive control, optimization algorithms, system identification, and their applications to large-scale network systems.

Education

  • Ph. D. in Chemical Engineering, University of Wisconsin-Madison, USA (2021, Expected)

  • B.S. in Mathematics and Chemical Engineering, Seoul National University, South Korea (2016)

Research Interests

  • Optimization Algorithms

  • Model Predictive Control

  • System Identification

Publications

Journal Publications:

  • Jalving, J., Shin, S., Zavala, V.M. A Graph-Based Modeling Abstraction for Optimization: Concepts and Implementation in Plasmo.jl, Under Review. [arXiv]

  • Na, S., Shin, S., Anitescu, M., Zavala, V.M. Overlapping Schwarz Decomposition for Nonlinear Optimal Control. Under Review. [arXiv]

  • Shin, S., Lu, Q., Zavala, V.M. Unifying Theorems for Subspace Identification and Dynamic Mode Decomposition. Under Review. [arXiv]

  • Shin, S. and Zavala, V.M. Diffusing-Horizon Model Predictive Control. Under Review. [arXiv]

  • Shin, S. , Zavala, V.M., and Anitescu, M. Decentralized Schemes with Overlap for Solving Graph-Structured Optimization Problems. IEEE Transactions on Control of Network Systems, 2020. [link] [arXiv]

  • Shin, S., Hart, P., Jahns, T. and Zavala, V.M. A Hierarchical Optimization Architecture for Large-Scale Power Networks. IEEE Transactions on Control of Network Systems, 2019. [link] [arXiv]

  • Shin, S., Venturelli, O., and Zavala, V.M. Scalable Nonlinear Programming Framework for Parameter Estimation in Dynamic Biological System Models. PLOS Computational Biology, 2019. [link] [bioRxiv]

  • Kim, D., Shin, S., Choi, G., Jang, K., Suh, and Lee, J. Diagnosis of Partial Blockage in Water Pipeline Using Support Vector Machine with Fault-Characteristic Peaks in Frequency Domain, Canadian Journal of Civil Engineering, 2017. [link]


Conference Publications:

  • Shin, S., Coffrin, C., Sundar, K., Zavala, V.M. Graph-Based Modeling and Decomposition of Energy Infrastructures. Under Review, 2020. [arXiv]

  • Shin, S., Anitescu, M., Zavala, V.M. Overlapping Schwarz Decomposition for Constrained Quadratic Programs. Accepted. In Proceedings of IEEE Conference on Decision and Control, 2020. [arXiv]

  • Lu, Q., Shin, S., Zavala, V.M. Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control. In Proceedings of IFAC World Congress, 2020. [arXiv]

  • Shin, S., Faulwasser, T., Zanon, M., Zavala, V. M. A Parallel Decomposition Scheme for Solving Long-Horizon Optimal Control Problems. In Proceedings of IEEE Conference on Decision and Control, 2019. [link] [arXiv]

  • Shin, S., Smith, A. D., Qin, S. J., Zavala, V. M. On the Convergence of the Dynamic Inner PCA Algorithm. In Proceedings of Foundations of Process Analytics and Machine Learning, 2019. [arXiv]


Book Chapters:

  • Shin, S. and Zavala, V.M. Multi-Grid Schemes for Multi-Scale Control of Energy Systems. In Sean Meyn, Tariq Samad, Ian Hiskens, and Jakob Stoustrup ed. Energy Markets and Responsive Grids, Springer, 2018. [link] [arXiv]