Xinyu Zhu
Software Engineer at Intel Corporation
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolXinyu Zhu is a software engineer based in Shanghai with 10 years of experience specializing in deep learning frameworks and backend systems. Currently at Tencent and with internship experience at Intel, Xinyu has contributed significant performance and Windows-build fixes to oneDNN and helped integrate MKL-DNN optimizations into Apache MXNet for quantization and model support. Their work on GluonCV added quantized model definitions and Yolo support, reflecting practical expertise in computer vision model optimization and benchmarking. Comfortable across low-level performance tuning and higher-level ML tooling, they bridge systems engineering with applied machine learning. A Shanghai Jiao Tong University alumnus with both bachelor's and master's degrees in computer science, Xinyu combines strong academic grounding with hands-on open-source impact on widely used projects.
10 years of coding experience
Master's degree, Computer Science, Master's degree, Computer Science at Shanghai Jiao Tong University
Github Skills (28)
quants10
c-language10
python10
image-classification10
windows10
mxnet10
machine-learning10
sycl10
deeplearning-ai10
oneapi10
deep-learning10
deep-neural-networks10
model-optimization10
neural-network10
object-detection10
Programming languages (6)
Github contributions (5)
uxlfoundation/oneDNN
Apr 2018 - Jan 2023
oneAPI Deep Neural Network Library (oneDNN)
Role in this project:
Back-end Developer
Back-end DeveloperContributions:18 reviews, 254 commits, 2 PRs in 4 years 9 months
Contributions summary:Xinyu primarily focused on bug fixes and enhancements related to Windows builds within the oneDNN library. They addressed issues with macros, file paths, and memory allocation specifically for the Windows operating system, as evidenced by changes across several files. The user also contributed to improvements in sycl_interop functionality, allowing for the creation of streams for CPU engines. They made modifications to core graph backend components, including those dealing with scales within the graph, indicating their work on integrating and optimizing the codebase.
bfloat16sse41avx512openmpopencl
apache/mxnet
Mar 2018 - Jun 2020
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:ML Engineer & Backend Developer
ML Engineer & Backend DeveloperContributions:2 reviews, 55 commits, 65 PRs in 2 years 3 months
Contributions summary:Xinyu primarily contributed to the optimization and enhancement of the `mxnet` deep learning framework, focusing on the integration of MKL-DNN for improved performance. Their work included implementing and integrating MKL-DNN for Conv1d and other operators, particularly for quantization tasks. The user also focused on improving existing functionality within the framework and enhancing the support of different deep learning models such as NCF, Bert, and other GluonCV models within the context of quantization. They added support for more models for benchmarking and implemented features to improve the user experience and efficiency, such as improved documentation.
pythonschedulerdataflowmutationdata-science
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