Summary
Rui Pan is a systems-focused Ph.D. candidate at Princeton with eight years of experience building high-performance infrastructure for ML and LLM inference, advised by Ravi Netravali. His work spans end-to-end systems research and practical deployments, with publications at SOSP, NSDI, MLSys, NeurIPS, and HotNets that address latency-throughput tradeoffs, adaptive inference, and real-time video rate control. He has interned and collaborated with Google, AWS, and MPI-INF, translating research ideas like prefix caching and speculative decoding into impactful prototypes and production-minded insights. Beyond papers, Rui has taught core systems courses and worked on cluster and scheduling projects that reveal a knack for marrying theoretical rigor with systems engineering. Colleagues describe him as a “systems researcher wannabe” who actually ships systems—and often finds clever ways to speed inference per input rather than by brute-force scale.
8 years of coding experience
3 years of employment as a software developer
Bachelor's Degree Computer Science Mathematics, Bachelor's Degree Computer Science Mathematics at University of Wisconsin-Madison
High School Affiliated to Shanghai Jiao Tong University
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Princeton University
Chinese, English, klingon