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Zifeng Huang
Entry Machine Learning Engineer specializing in anomaly detection and deep learning
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Built a production industrial anomaly detection system for a laminator using only limited runtime logs (time/pressure/temperature) and scarce abnormal examples. Addressed inconsistent manual labeling across customers by creating an operator feedback loop for remarking predictions and retraining customized models, and communicated results to a non-technical company liaison using clear tables, trend plots, and interactive demos.
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Key Strengths
- Built and deployed an industrial anomaly detection system for laminator machine logs
- Designed a human-in-the-loop feedback and retraining pipeline to address limited/biased labeled data
- Pragmatic reliability testing via noise/oscillation injection and edge-case evaluation
- Strong stakeholder communication: translated ML results into tables, trend plots, and interactive demos for non-technical liaison
- Modular system design with independent component testing; metrics-driven tradeoffs (prioritized recall over precision)
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Experience
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Publications
1 publication
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