Kibaek Kim

Kibaek Kim is a Computational Mathematician in the Laboratory for Applied Mathematics, Numerical Software, and Statistics at Argonne National Laboratory's Mathematics and Computer Science Division. He also serves as a Senior Scientist at-Large at the University of Chicago Consortium for Advanced Science and Engineering.

His educational background includes a Ph.D. in Industrial Engineering and Management Sciences from Northwestern University (2014) under the supervision of Dr. Sanjay Mehrotra.

Dr. Kim's research focuses primarily on computational optimization, including stochastic programming and integer programming, with applications to complex systems. His recent work has centered on developing algorithms for solving stochastic mixed-integer programs using high-performance computing clusters. He is particularly known for his contributions to federated learning algorithms and software development, as well as modeling and numerical algorithms for large-scale optimization on high-performance computing systems and GPUs. His work finds applications in electric grid systems, healthcare, and key scientific domains of interest to the Department of Energy.

Dr. Kim's publications demonstrate a strong focus on federated learning frameworks, with significant contributions to privacy-preserving techniques and distributed training algorithms. His work bridges theoretical optimization with practical implementation on high-performance computing infrastructure.

• DOE Early Career Research Program award (2019)
• IEEE Senior Member (2022)
• Multiple IMPACT Argonne Awards (2021)
• George L. Nemhauser Best Student Paper (2014)

Throughout his career, Dr. Kim has mentored numerous postdoctoral researchers, predocs, and Ph.D. interns from top institutions including MIT, UIUC, Georgia Tech, and Northwestern University. He serves as associate editor for Mathematical Programming Computation and Naval Research Logistics, and as a board member for COIN-OR Foundation and IISE Energy Systems.

Dr. Kim leads research on the APPFL (Advanced Privacy-Preserving Federated Learning) framework, which provides a comprehensive environment for implementing, testing, and validating privacy-preserving federated learning algorithms across distributed computing environments.