Kibaek Kim is a [Computational Mathematician] in the Mathematics and Computer Science Division at Argonne National Laboratory. His research develops scalable algorithms at the intersection of large-scale optimization, machine learning, and high-performance computing — spanning foundation-model pretraining, agentic AI workflows, distributed and federated learning, stochastic programming, and physics-informed methods that exploit leadership-class systems such as Aurora and Frontier. He led Argonne’s effort on the DOE Genesis Mission GridAI seed project, where these methods are advanced through LUMINA, a foundation-model family trained at scale on DOE supercomputers, and GridMind, an agentic analysis workflow. He also leads APPFL, an open-source framework for privacy-preserving federated learning, and serves as PI on multiple DOE-funded efforts bridging applied mathematics, AI, and HPC. His work emphasizes algorithms and software that translate frontier compute into deployable scientific and engineering decision support.
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