The lab focuses on numerical optimization that leverages AI and high-performance computing with GPUs, with applications in power systems, genome-wide association studies, and manufacturing process optimization.

AI와 GPU를 활용한 수치 최적화에 중점을 두고 있으며, 전력 시스템, 전유전체 연관 연구, 제조 공정 최적화에 적용됩니다.

Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program

A reinforcement learning approach to parameter selection for distributed optimal power flow

APPFL: open-source software framework for privacy-preserving federated learning

RV-SVM: An efficient method for learning ranking SVM

MPEC methods for bilevel optimization problems

An efficient method for learning nonlinear ranking SVM functions

Solving equilibrium problems using extended mathematical programming

Accelerated computation and tracking of AC optimal power flow solutions using GPUs

Leveraging GPU batching for scalable nonlinear programming through massive Lagrangian decomposition

Adaptively refined dynamic program for linear spline regression

MINLP formulations for continuous piecewise linear function fitting

Exact indexing for support vector machines

GPU-accelerated DNS of compressible turbulent flows

A real-time optimization with warm-start of multiperiod AC optimal power flows

A structure-preserving pivotal method for affine variational inequalities

Julia as a portable high-level language for numerical solvers of power flow equations on GPU architectures

iKernel: Exact indexing for support vector machines

Escaping a dominance region at minimum cost

QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints

Accelerating Genome- and Phenome-Wide Association Studies using GPUs – A case study using data from the Million Veteran Program

Youngdae Kim's Lab

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알럼나이가 간 회사

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