Youngdae Kim

I am an Assistant Professor in the Department of Industrial Engineering, and I am also jointly affiliated with the Graduate School of Artificial Intelligence at Ulsan National Institute of Science and Technology (UNIST). My research interestes are in numerical optimization that leverages AI and high-performance computing with GPUs, with applications in power systems, genome-wide association studies, and manufacturing process optimization. Prior to joining UNIST in 2024, I was a Postdoctoral Appointee in the Mathematics and Computer Science Division at Argonne National Laboratory (2018-2022) and a Research Associate at ExxonMobil, New Jersey (2022-2024).

See my Google Scholar page for publications.

I am looking for highly motivated students who are interested in working at the intersection of optimization, AI, and GPU computing. One position is currently available.

Current Projects

• (PI: Excellent Young Researcher Program (한국연구재단 우수신진연구), 2025-2026) GPU-accelerated, privacy-preserving, large-scale distributed optimization for cross-biobank genome-wide association studies, in collarboration with Argonne National Laboratory

Students

• Taeyeop Kang (2025-Present)

Teaching

Current

• IE201: Operations Research I (Fall 2025)

Previous

• IE205: Basic Probability Theory for Engineers (Spring 2025)
• IE551: Nonlinear Optimization (Fall 2024)

Software

• ExaTron.jl: Batch nonlinear programming on GPUs (written in Julia)
• A real-time optimization of a rolling horizon multiperiod ACOPFs (written in Julia)
• SELKIE: An agent-based decomposition method for equilibrium problems (written in C)
• New JAMS for equilibrium programming (written in Delphi)
• Block LU update: It is being currently used as one of the linear algebra engines for the PATH solver (written in C)