Yunfei Wang
Failure Analysis & Reliability Engineer at Intel Corporation
Greater Phoenix Area United States
Summary
👤
Senior🎓
Top SchoolYunfei Wang is a Failure Analysis & Reliability Engineer with 10 years of experience applying Physics of Failure, materials science, and AI-driven metrology to improve CPU package reliability and product qualification for Intel platforms like Meteor Lake and Lunar Lake. He combines deep academic training (PhD in Materials Science and Engineering) with hands-on lab leadership—developing sample prep recipes, troubleshooting equipment, and leading chemistry research into electromigration, thermal aging, and conductive anodic filament failures. Yunfei has translated lab findings into design and material recommendations that influenced package architecture decisions and has published industrial research while sharing results across multidisciplinary teams. He also bridges hardware and software: contributing to high-profile open-source projects such as TensorFlow.js by improving WebGPU performance for ML workloads and adding robust Node.js tests to the Intel RealSense SDK. Based in the Greater Phoenix area, he blends experimental rigor with practical automation to shorten analysis cycles and lower inspection costs. An often-overlooked strength is his ability to turn complex failure modes into actionable, data-driven test plans that accelerate product release qualification.
10 years of coding experience
Research Assistance, Chemical Engineering, Research Assistance, Chemical Engineering at 清华大学Bachelor's degree, Materials Science and Engineering, Bachelor's degree, Materials Science and Engineering at 北京科技大学Johns Hopkins University
English, Chinese
Github Skills (19)
ls10
unit-testing10
javascript10
lib10
testing10
tensorflowjs10
machine-learning10
standard-library10
sdk10
wgsl10
webgpu10
nodejs10
test-automation10
linear-algebra9
performance-optimization9
Programming languages (7)
Github contributions (5)
tensorflow/tfjs
Sep 2019 - Dec 2022
A WebGL accelerated JavaScript library for training and deploying ML models.
Role in this project:
ML Engineer
ML EngineerContributions:103 reviews, 96 commits, 121 PRs in 3 years 3 months
Contributions summary:Yunfei primarily contributed to the WebGPU backend implementation for the TensorFlow.js library, focusing on enabling and improving machine learning model performance. Their work includes refactoring and optimizing existing code, such as the simplification of im2col and adding support for various operations (e.g., realDivide, dilated convolution, Elu, OnesLike, GatherV2, Range, Einsum, ResizeNearestNeighbor, Sqrt, GatherND, LogicalNot, Tanh, SplitV, and ScatterND). Furthermore, they worked on bug fixes and performance improvements, including adjustments to shader keys, the implementation of 5D/6D support, and the optimization of matmul for dispatch size.
deployingjavascript-librarygpu-accelerationml-modelswebgl
IntelRealSense/librealsense
Sep 2017 - Jun 2018
Intel® RealSense™ SDK
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:17 commits, 17 PRs, 37 comments in 9 months
Contributions summary:Yunfei primarily contributed by adding test cases for the nodejs wrapper within the librealsense SDK. Their work involved writing tests for various classes and methods related to sensors, frames, devices, depth sensors, point clouds, colorizers, pipelines, context, framesets, align, config, playback devices, and recording features. These tests covered a wide range of functionalities, including property validation, method execution, and ensuring correct behavior of different components within the SDK's nodejs implementation.
intel-realsensedeveloper-kitspayment-methodslibrealsensein-app-purchase
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.