Faculty Research Profile
Youngdae Kim
학력
· 2017: Ph.D. Computer Sciences, University of Wisconsin-Madison, USA
· 2009: M.S. Computer Science and Engineering, POSTECH, South Korea
· 2007: B.S. Computer Science and Engineering, Mathematics, POSTECH, South Korea
주요 경력
· 2024~Present: Assistant Professor, UNIST
· 2022~2024: Research Associate, ExxonMobil Research
· 2018~2022: Postdoctoral Appointee, Argonne National Laboratory
· 2018~2018: Postdoctoral Researcher, University of Wisconsin-Madison
가속화된 최적화 연구실
Accelerated Optimization Laboratory
가속화된 최적화 연구실은 그래픽 처리 장치 (GPU)와 AI를 활용하여 수리 최적화 알고리즘의 계산 속도를 향상시키는 방법을 연구하며, 또한 수리 최적화를 이용하여 AI 결과물의 성능을 향상시키는 연구를 수행합니다. 이를 위해, i) GPU를 활용한 분산 대용량 수리 최적화 알고리즘 개발; ii) 수리 최적화와 AI의 접목 연구; iii) 다양한 응용 어플리케이션에 쉽게 적용하기 위한 계산 프레임워크 개발을 목표로 합니다. 우리 연구실의 최근 연구 결과물은 대용량 전력 시스템의 최적화와 인체자원은행의 분석에 적용된 바 있습니다.
ACCOL (ACCelerated Optimization Laboratory) aims at developing accelerated mathematical optimization algorithms via GPUs and AI and improving the quality of AI solutions via mathematical optimization. To achieve this, we study i) GPU-accelerated distributed large-scale mathematical optimization algorithms; ii) the integration of mathematical optimization with AI; and iii) a computational framework that provides easy access to our technology. Our recent research results have been applied to large-scale power system optimization and biobank analysis.
ACCOL (ACCelerated Optimization Laboratory) aims at developing accelerated mathematical optimization algorithms via GPUs and AI and improving the quality of AI solutions via mathematical optimization. To achieve this, we study i) GPU-accelerated distributed large-scale mathematical optimization algorithms; ii) the integration of mathematical optimization with AI; and iii) a computational framework that provides easy access to our technology. Our recent research results have been applied to large-scale power system optimization and biobank analysis.
연구분야
Nonlinear Optimization, High Performance Computing, Variational Inequalities
Nonlinear Optimization, High Performance Computing, Variational Inequalities
연구 희망분야
GPU-accelerated and AI-enhanced mathematical optimization
GPU-accelerated and AI-enhanced mathematical optimization
연구주제
· GPU-accelerated and AI-enhanced mathematical optimization
· Integration of mathematical optimization with AI
· GPU-accelerated and AI-enhanced mathematical optimization
· Integration of mathematical optimization with AI
논문
· Science / Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program / A Verma, JE Huffman, A Rodriguez, M Conery, M Liu, YL Ho, Y Kim et al. / 2024
· Learning for Dynamics and Control Conference / QCQP-Net: Reliably learning feasible alternating current optimization power flow solutions under constraints / S Zeng, Y Kim, Y Ren, K Kim / 2024
· Workshop on International Conference on Parallel Processing / Accelerated computation and tracking of AC optimal power flow solutions using GPUs / Y Kim, K Kim / 2022