백웅기 연구실

Research and develop system software support for high-performance and efficient machine learning, including characterizing and optimizing machine-learning frameworks, programming language runtimes, and resource management for large-scale distributed systems.

Investigate machine learning-augmented system software including improving efficiency of parallel and distributed task schedulers, dynamic data placement and migration, security and reliability enhancement, and code analysis and optimization.

Research and develop system software techniques for highly-scalable and energy-efficient parallel computing on embedded, multi/many-core, and GPGPU systems, including OS scheduling, runtime techniques, and performance optimization.

Investigate system software support for large-scale and emerging memory systems, including heterogeneous memory systems, performance and durability enhancement, and efficient memory management for emerging computer systems.

Investigate the design and implementation of secure computer systems, including secure system software for safe and efficient computing, security attacks and vulnerabilities, and machine learning techniques for attack detection.