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

Sungho Shin is currently a Ph.D. candidate in the Department of Chemical and Biological Engineering at the University of Wisconsin-Madison. He received his B.S. in mathematics and chemical engineering at Seoul National University. He was a Givens Associate at the Mathematics and Computer Science Division at Argonne National Laboratory (2018 Summer). His research interests include model predictive control, large-scale optimization algorithms, and spatiotemporal data analysis.

Education

  • Ph. D. in Chemical Engineering, University of Wisconsin-Madison, USA (2021, Expected)
  • B.S. in Chemical Engineering and Mathematics, Seoul National University, South Korea (2016)

Research Interests

  • Optimization
  • Control
  • Machine Learning

Publications

Journal Publications:

  • Shin, S. and Zavala, V.M. Computing Economic-Optimal and Stable Equilibria for Droop-Controlled Microgrids. Under Review. [link]
  • Shin, S. , Zavala, V.M., and Anitescu, M. Decentralized Schemes with Overlap for Solving Graph-Structured Optimization Problems. Cond. Accepted, IEEE Transactions on Control of Network Systems, 2019. [link]
  • 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, 6(3):1004–1014, 2019. [link]
  • Shin, S., Venturelli, O., and Zavala, V.M. Scalable Nonlinear Programming Framework for Parameter Estimation in Dynamic Biological System Models. PLOS Computational Biology, 15(3), e1006828, 2019. [link]
  • 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:

  • Lu, Q., Shin, S., Zavala, V.M. Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control. Under review. [link]
  • 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, Accepted, 2019. [link]
  • Shin, S., Smith, A. D., Qin, S. J., Zavala, V. M. On the Convergence of the Dynamic Inner PCA Algorithm. Proceedings of Foundations of Process Analytics and Machine learning, 2019. [link]


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]