Chang-hong Chen
Advanced Packaging Engineer at Intel Corporation
Chandler, Arizona, United States
Summary
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Senior🎓
Top SchoolChang-hong Chen is an Advanced Packaging Engineer with nine years of experience at Intel, specializing in CMP process development, Foveros packaging integration, and tooling/facility planning for advanced 3D packaging. He has led module startups and cross-vendor coordination as owner of Intel WATD CMP and Foveros Omni polish efforts, and was recognized with an ATTD DRA award for impact. Trained as a Ph.D. chemist, he combines deep materials and process knowledge with strong problem-identification, rapid solutioning, and clear presentation skills. Beyond packaging, he contributes to open-source ML privacy tooling—improving Privacy Meter’s class-based metrics and attack performance—highlighting a rare mix of hardware process engineering and privacy-aware ML implementation experience. Based in Chandler, Arizona, he is known for fast learning, ownership mentality, and bridging lab-scale research with high-volume manufacturing realities.
9 years of coding experience
5 years of employment as a software developer
Master of Science (M.S.) Chemistry, Master of Science (M.S.) Chemistry at National Taiwan UniversityDoctor of Philosophy - PhD Chemistry, Doctor of Philosophy - PhD Chemistry at Texas A&M University
Chinese, English
Github Skills (7)
pytorch10
machine-learning10
python10
data-structure9
algorithm9
data-structures9
algorithms9
Programming languages (3)
Github contributions (5)
privacytrustlab/ml_privacy_meter
Oct 2022 - Dec 2022
Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.
Role in this project:
ML Engineer
ML EngineerContributions:21 commits, 8 PRs, 22 comments in 2 months
Contributions summary:Chang-hong primarily focused on updating and improving the Privacy Meter (PM) library, which is designed to audit data privacy in machine learning algorithms. Their contributions include enabling class-based thresholding within the GroupPopulationMetric, which enhances the metric's ability to compute thresholds on a per-class basis, reflecting a deep understanding of the attack's mechanics. They also improved the performance of the reference attack by reducing the number of loops, indicating a focus on optimization. These changes suggest the user is focused on improving the implementation and effectiveness of privacy auditing techniques.
privacy-auditmeterdata-auditinformation-leakagedata-privacy
changhongyan123/mypoker
Jan 2019 - Apr 2019
CS3243 Introduction to AI Term Project: AI Poker
Contributions:28 commits, 2 PRs, 7 pushes in 2 months
poker-aimachine-learningterm-projectterm
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