Qi Lin is a Ph.D. student in the Intelligent Data Infrastructure Lab at Arizona State University. His research focuses on disaggregated database systems, cloud-native database systems, and query engines for AI/ML platforms. He is fortunate to be supervised by Prof. Zhichao Cao. Previously, he studied Computer Science as a master student at the University of California, Irvine, supervised by Prof. Faisal Nawab.

He will join Microsoft Research as a Research Intern in Summer 2026.

Research Interests

• Data Infrastructure/Storage: Key-value stores (RocksDB, LevelDB), RDMA-based high-speed networking and remote memory access, and high-performance storage devices (NVMe, SPDK).
• RDMA and CXL-Based Disaggregated Memory Systems: Cache-coherent shared memory over RDMA and CXL, memory consistency and ordering, remote load/store semantics, and failure-resilient shared-memory system design.

News

• [2026] Paper accepted at SIGMOD 2026: “O3-LSM: Maximizing Disaggregated LSM Write Performance via Three-Layer Offloading.”
• [2025] Paper accepted at EMNLP 2025: “Bit-Flip Error Resilience in LLMs: A Comprehensive Analysis and Defense Framework.”
• [2024] Paper accepted at EDBT 2024: “Serving Deep Learning Model in Relational Databases.”
• [2024] Paper accepted at TKDE 2024: “RollStore: Hybrid Data Indexing for Decentralized Blockchain Applications.”