Assist Prof Dr Minseok Ryu, Arizona State University, United States
Dr. Minseok Ryu is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University, Tempe, AZ. He holds a Ph.D. in Industrial and Operations Engineering from the University of Michigan and an M.S. and B.S. in Aerospace Engineering from KAIST. Dr. Ryu’s research spans advanced scientific computing, privacy-preserving federated learning, and power system resilience. He has held notable positions at Argonne and Los Alamos National Laboratories and has been recognized by the Department of Energy for his highlighted research. He is a member of prominent societies like INFORMS and IEEE. ππ§βπ«π¬
Publication profile
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EducationΒ
Her hold a Ph.D. in Industrial and Operations Engineering from the University of Michigan, Ann Arbor, completed in May 2020 π. Prior to this, I earned a Master’s degree in Aerospace Engineering from KAIST in Daejeon, Korea, in February 2014 π. My academic journey began with a Bachelor’s degree in the same field at KAIST, which I completed in February 2012 βοΈ. This extensive background has equipped me with a robust understanding of both industrial systems and aerospace technologies, paving the way for a dynamic and interdisciplinary career π οΈπ.
Honors & Awards
In 2024, I had the honor of being an Alliance Fellow with the Mayo Clinic and ASU Alliance for Health Care during their esteemed Faculty Summer Residency program. My research has consistently been recognized, including being highlighted by the Department of Energyβs Advanced Scientific Computing Research in both 2023 and 2022 π. My academic journey has been supported by several prestigious awards, such as the Rackham Graduate Student Research Grant from the University of Michigan in 2016 π and multiple fellowships in 2015. Additionally, my early academic achievements include the National Science Foundation Student Award π and recognition from the Government of Korea for outstanding scholarship π.
Employment πΌ
He is currently an Assistant Professor at Arizona State University (since August 2023), after a tenure as a Postdoctoral Appointee at Argonne National Laboratory from 2020 to 2023.
Research focus
M. Ryu’s research spans privacy-preserving federated learning frameworks, including differential privacy and distributed control of optimal power flow in electric grids. They also contribute to distributionally robust optimization techniques for scheduling and staffing problems, particularly in healthcare and power systems. Their work emphasizes practical algorithms and frameworks (like APPFL) for enhancing privacy and efficiency in distributed systems. Additionally, they explore mitigating uncertain impacts of geomagnetic disturbances on electric grids. Overall, M. Ryu’s research integrates optimization, privacy, and robustness into practical applications across diverse domains. ππ
Publication top notes
Data-Driven Distributionally Robust Appointment Scheduling over Wasserstein Balls
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning
A Privacy-Preserving Distributed Control of Optimal Power Flow
An extended formulation of the convex recoloring problem on a tree
Nurse Staffing under Absenteeism: A Distributionally Robust Optimization Approach
Differentially private federated learning via inexact ADMM with multiple local updates
Mitigating the Impacts of Uncertain Geomagnetic Disturbances on Electric Grids: A Distributionally Robust Optimization Approach
Algorithms for Mitigating the Effect of Uncertain Geomagnetic Disturbances in Electric Grids
Development of an Engineering Education Framework for Aerodynamic Shape Optimization
Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx