Yi Li is a research engineer specializing in compiler development and optimization for efficient DNN execution on machine learning accelerators, with six years of experience spanning academia and industry. He holds advanced degrees from Princeton and has driven end-to-end compiler pipelines, scheduling and mapping optimizations, and formal verification work for coarse-grained hardware accelerators. Yi has interned and now works at Meta, where he built an MLIR-based pipeline translating PyTorch to a custom DSP and implemented passes like vectorization and loop tiling to auto-generate optimized vector code. His background combines hands-on circuit and mixed-signal work from earlier research with deep expertise in compiler passes and accelerator-aware scheduling, a mix that helps bridge hardware constraints and software-level optimization. Based in Redmond, he brings proven ability to turn deep-learning models into high-performance executable mappings for specialized accelerators.
6 years of coding experience
7 years of employment as a software developer
Foshan No.1 High School 佛山市第一中学
Bachelor of Science - BS, Electrical Engineering and Computer Science, Bachelor of Science - BS, Electrical Engineering and Computer Science at Peking University
CSST Summer Research Program, Electrical Engineering, CSST Summer Research Program, Electrical Engineering at University of California, Los Angeles
PhD Student, Electrical Engineering, PhD Student, Electrical Engineering at Princeton University
Contributions:234 commits, 14 PRs, 32 pushes in 2 years 10 months
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