Chen Fu
Principal Software Engineer at Microsoft
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolChen Fu is a Principal Software Engineer based in San Jose with seven years of recent industry experience and a long track record of building high-performance, reliable distributed systems across Microsoft, Alibaba, and Apple. He specializes in performance optimization and resource-efficient storage and database designs, having shipped innovations like the Exabyte Scavenger to tame write amplification and stabilize multi-tenant workloads. At Microsoft he has bridged research and production—applying model checking, Bayesian-driven incident mitigation, and automated diagnosis to shorten incident response from hours to minutes. Chen is also an active contributor to high-profile open-source ML infrastructure, optimizing ONNX Runtime for memory efficiency and 4-bit quantized matrix multiplications to accelerate inference. His PhD-level background in computer science underpins a pragmatic approach that blends deep systems research with measurable production impact. Quietly, he often focuses on reducing collateral resource spikes that disrupt collocated services, a recurring but underappreciated lever for improving cloud reliability.
7 years of coding experience
9 years of employment as a software developer
Master of Engineering Computer Engineering, Master of Engineering Computer Engineering at Institute of Computing, Chinese Academy of ScienceBachelor Computer Science, Bachelor Computer Science at Peking UniversityPh.D Computer Science, Ph.D Computer Science at Rutgers University
Chinese
Github Skills (14)
tensorrt10
quantization10
machine-learning10
tensor10
c-language10
tensorflow10
onnx10
cprogramming-language10
performance-optimization10
operation10
linear-algebra9
cuda9
arm9
avx8
Programming languages (6)
Github contributions (5)
microsoft/onnxruntime
Mar 2021 - Jan 2023
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Role in this project:
ML Engineer
ML EngineerContributions:330 reviews, 48 commits, 165 PRs in 1 year 10 months
Contributions summary:Chen primarily contributed to the performance optimization of the ONNX Runtime, focusing on memory usage and code efficiency within the context of machine learning inferencing. They identified and addressed issues related to buffer management in prepacked tensors, and worked on integrating and optimizing quantized matrix multiplication operations. This work included implementing optimizations for 4-bit quantization and implementing performance tests.
runtimetrainingtensorflowai-frameworkaccelerator
chenfucn/onnxruntime
Feb 2021 - Jul 2024
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Contributions:1 review, 1 PR, 1106 pushes in 3 years 5 months
pytorchdeep-learningruntimemachine-learningonnx
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