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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.
Major research field
Nonlinear Optimization, High Performance Computing, Variational Inequalities
Desired field of research
GPU-accelerated and AI-enhanced mathematical optimization
Research Keywords and Topics
· GPU-accelerated and AI-enhanced mathematical optimization
· Integration of mathematical optimization with AI
Research Publications
· 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