PHD at the University of Michigan, United States
Minseok Ryu is an Assistant Professor at Arizona State University’s School of Computing and Augmented Intelligence. He earned his Ph.D. in Industrial and Operations Engineering from the University of Michigan in 2020. Prior to his current role, he held a postdoctoral position at Argonne National Laboratory and conducted research at Los Alamos National Laboratory. Ryu’s expertise spans privacy-preserving federated learning, optimization, and power system resilience. He has secured significant funding, including from DOE-ASCR and NSF, and developed the open-source APPFL package for federated learning. Ryu is actively involved in professional societies like INFORMS and IEEE, contributing to conferences and serving as a panelist for NSF reviews.
Professional Profiles
Education
Ph.D. Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI; May 2020 M.S. Aerospace Engineering, KAIST, Daejeon, Korea; Feb 2014 B.S. Aerospace Engineering, KAIST, Daejeon, Korea; Feb 2012
Work Experience
Assistant Professor, School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ Aug 2023–present Postdoctoral Appointee, Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL Aug 2020–Jul 2023 Research Assistant, Applied Mathematics and Plasma Physics Group, Los Alamos National Laboratory, Los Alamos, NM May 2019–Aug 2019 Post Baccalaureate Research Fellow, Kellog School of Management, Northwestern University, Evanston, IL Nov 2014–Apr 201
Honors & Awards
2024 Alliance Fellow, Mayo Clinic and ASU Alliance for Health Care 2023 Highlighted Research, Department of Energy, Advanced Scientific Computing Research (DOE-ASCR) 2022 Highlighted Research, DOE-ASCR
Professional Activities
Membership in Professional Societies: INFORMS, IEEE, IISE, SIAM Proposal Review: Panelist for National Science Foundation (NSF) Journal/Conference Review: Numerous journals and conferences including IEEE Transactions on Power Systems, Management Science, and others.
Research Focus
Minseok Ryu’s research primarily focuses on advanced optimization techniques and privacy-preserving federated learning systems. His work spans several key areas including privacy-preserving distributed control in power systems, data-driven distributionally robust optimization for scheduling, and mitigating uncertain disturbances in electric grids. Ryu has also contributed significantly to the development of algorithms for differentially private federated learning, enhancing security and robustness in biomedical research and heterogeneous computing environments. His expertise extends to heuristic algorithms for geomagnetically induced current blocking devices, showcasing a deep commitment to advancing resilient infrastructure and secure data handling in complex operational environments.
Publications
- A GPU-based Distributed Algorithm for Linearized Optimal Power Flow in Distribution Systems, Publication date: 2023.
- Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx (preprint), Publication date: 2023.
- Efficient Heuristic Approaches to Binary Optimization: a Sensor Placement Application, Publication date: 2023.
- APPFLX: Providing privacy-preserving cross-silo federated learning as a service, Publication date: 2023.
- Heuristic Algorithms for Placing Geomagnetically Induced Current Blocking Devices, Publication date: 2023.
- Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx, Publication date: 2022.
- APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning, Publication date: 2022.
- A Privacy-Preserving Distributed Control of Optimal Power Flow, Publication date: 2022.
- Differentially private federated learning via inexact ADMM with multiple local updates, Publication date: 2022.
- Mitigating the Impacts of Uncertain Geomagnetic Disturbances on Electric Grids: A Distributionally Robust Optimization Approach, Publication date: 2022.
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