In Machine Learning Systems Lab, we investigate programmability, scalability,  manageability, and performance of machine learning systems. We optimize the performance of existing machine learning systems such as Tensorflow, PyTorch, Megatron, and , as well as big data systems such as Hadoop. We investigate various trade-offs (such as performance and accuracy) in existing systems. We also work on security aspect of machine learning models and systems. See Research Page for the list of the research topics.

Research Topics include  (연구 주제):

• Large-Scale Machine Learning System             대규모 분산 머신러닝 시스템

• Machine Learning Model Engineering                      머신러닝 모델의 효율적인 개발/적용을 위한 기술/도구

• Deep Learning Model Security/Testing            딥러닝 시스템 보안/모델 테스팅

• NLP Model Training/Inference Optimization     자연어 처리 모델  학습/추론 최적화

• Graph Processing Systems                                         그래프 분석 시스템

  자세한 내용은 Research Page 참고