Zhichao Cao is an assistant professor and research scientist with 11 years of experience bridging academia and industry to advance storage systems, databases, and infrastructure for LLMs. He holds a PhD in Computer Science from the University of Minnesota and has led research and engineering efforts at Facebook on RocksDB, contributing performance and correctness fixes to a widely used open-source key-value store. His work spans key-value and file systems, new storage/media (NVM, SMR/IMR), memory disaggregation (CXL), and applying ML to storage problems such as tiering, deduplication, and migration. At Arizona State University he teaches operating systems and large-scale data processing while continuing systems research into LLM infrastructure and IoT data storage. Zhichao combines deep hands-on systems engineering (container live migration, HBase optimization, Docker/Kubernetes) with rigorous academic publishing and editorial leadership at ACM Transactions on Storage. He is notable for translating low-level storage innovations into practical, production-ready improvements for high-scale services.
11 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at University of Minnesota
Bachelor of Science (BS), Automation, Bachelor of Science (BS), Automation at Tsinghua University
A library that provides an embeddable, persistent key-value store for fast storage.
Role in this project:
Back-end Developer & Database Engineer
Contributions:2 releases, 616 reviews, 137 commits in 3 years 6 months
Contributions summary:Zhichao primarily focused on improving the performance and functionality of the RocksDB library. Their contributions included fixing format issues in printing uint64_t values, adding a new DB property related to statistics, and enhancing compaction options. Furthermore, the user fixed a bug in the db_bench MergeRandom benchmark to ensure it correctly accesses the default column family when multiple column families are specified, demonstrating a strong understanding of the database's core functionalities and internal workings.
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