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Welcome to the Shin Group’s website! We are a research group at the Massachusetts Institute of Technology, led by Professor Sungho Shin.
Group Overview
Our research group is dedicated to tackling the mathematical, computational, and practical challenges involved in making systematic decisions in complex systems. We strive to make contributions in the fields of mathematical optimization, control theory, machine learning, process systems engineering, and energy systems.
Here are some of our specific research interests:
- Developing scalable algorithms capable of addressing challenging decision-making problems in various engineering and society domains.
- Advancing our understanding and developing algorithms for machine learning methods in control problems.
- Building computational infrastructure for scalable and data-driven decision-making by harnessing the capabilities of modern high-performance computing, particularly through the use of GPU computing.
- Making tangible impacts in decision-making practices within the domains of process systems engineering and energy systems by introducing new modeling paradigms, algorithms, and software tools.
Learn more about our research >>
PSE Seminar Series
Shin group and Braatz group are co-hosting the MIT Process Systems Engineering Seminar Series. This seminar series features talks by leading researchers in the field of process systems engineering, covering a wide range of topics such as process control, process design, optimization, machine learning, data analytics, and their applications in diverse areas. Open to the public, the seminar series offers a great opportunity to learn about the latest research in process systems engineering. You can receive seminar information by subscribing to our mailing list. We hope to see you at the next seminar!
Learn more about PSE Seminar Series >>
MadSuite: An Optimization Software Suite for GPUs
MadSuite is a suite of open-source optimization software deisgned to support GPU acceleration. The software tools within this suite encompasses modeling library, optimization solvers, linear system solvers, and additional utility tools. Shin Group is actively contributing to the development of MadSuite.
News
| July 2025 | Our paper "Near-Optimal Performance of Stochastic Model Predictive Control" is accepted for publication in Mathematics of Operations Research [ Link ] | |
| November 2024 | Our recent work on solving AC optimal power flow on GPUs has been highlighted in NVIDIA's technical blog. This posting highlights how our team has exploited the capabilities of NVIDIA's new sparse direct solver, cuDSS, to solve challenging power system optimization problems. [ Link ] | |
| July 2024 | Joshua Pulsipher received the best presentation prize at JuMP-dev 2024 for the talk "InfiniteExaModels.jl: Accelerating Infinite-Dimensional Optimization Problems on CPU & GPU", based on collaborative work with Sungho Shin, François Pacaud, and Mihai Anitescu. [ Link ] | |
| February 2024 | Our paper "A graph-based modeling abstraction for optimization: Concepts and implementation in Plasmo.jl" has received the 2022 Outstanding Paper Award of the Mathematical Programming Computation. [ Link ] | |
| January 2024 | Our paper "Accelerating optimal power flow with GPUs: SIMD abstraction of nonlinear programs and condensed-space interior-point methods" with F. Pacaud and M. Anitescu has been accepted for publication in PSCC 2024. [ Link ] |
Selected Publications
| [S4] | Sungho Shin, Sen Na, and Mihai Anitescu. Near-optimal performance of stochastic model predictive control. Mathematics of Operations Research, 2025. Accepted. arXiv:2210.08599. |
| [S3] | Sungho Shin, Mihai Anitescu, and François Pacaud. Accelerating optimal power flow with GPUs: SIMD abstraction of nonlinear programs and condensed-space interior-point methods. Electric Power Systems Research, 236:110651, 2024. arXiv:2307.16830, doi:10.1016/j.epsr.2024.110651. |
| [S2] | Sungho Shin, Yiheng Lin, Guannan Qu, Adam Wierman, and Mihai Anitescu. Near-optimal distributed linear-quadratic regulator for networked systems. SIAM Journal on Control and Optimization, 61(3):1113–1135, 2023. arXiv:2204.05551, doi:10.1137/22M1489836. |
| [S1] | Sungho Shin, Mihai Anitescu, and Victor M Zavala. Exponential decay of sensitivity in graph-structured nonlinear programs. SIAM Journal on Optimization, 32(2):1156–1183, 2022. arXiv:2101.03067, doi:10.1137/21M1391079. |