박성용 연구실

System-level optimizations for RAG-based LLM serving including heterogeneous resource utilization, data movement minimization, KV cache management, and QoS-aware adaptive scheduling for latency-stable inference.

Software optimization for distributed deep learning including Graph Neural Networks (GNN), model and data parallelism, and efficient resource utilization for vision, language, and graph models.

Vector database architecture for billion-scale datasets with storage-based ANN search, index partitioning, and stream processing integration for RAG and recommendation systems.

Storage systems optimization for LSM-tree based key-value stores addressing write amplification, read overhead, and space amplification with hardware acceleration.

Research on CXL technology for memory expansion and disaggregated computing environments, optimizing memory, storage, and I/O for GNN-based recommendation systems.

Object storage technology design for unstructured data management in data lakes, cloud-native applications, and machine learning workloads.

Optimization of in-memory streaming processing platforms for heterogeneous computing infrastructures including query execution plans, task scheduling, and state management.