Linfan Zhang
Senior Applied Scientist at Microsoft
Mountain View, California, United States
Summary
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Senior🎓
Top SchoolLinfan Zhang is a Senior Applied Scientist based in Mountain View with eight years of experience bridging statistical research and applied ML in advertising and trust & safety. A Ph.D. candidate in Statistics at UCLA, he specializes in social network inference, high-dimensional statistics, and unsupervised learning, and brings that rigor to production problems like ad quality, multimodal relevance, and image compliance. At Microsoft he built scalable pipelines for prompt tuning and GPT-4o migration that enabled structured outputs and measurable gains in accuracy and error reduction, and earlier work distilled GPT-4v labels to boost revenue and halve image defect rates. During an internship at Pinterest he showed user-embedding models could detect spam earlier and expanded anomalous user detection using XGBoost and LSTM on image embeddings. He combines deep theoretical training with hands-on ML engineering to move cutting-edge models into high-throughput systems. Though primarily focused on ads and safety, his background suggests strong capability in turning unsupervised and network-based insights into operational signals.
8 years of coding experience
2 years of employment as a software developer
The University of Hong Kong (HKU)Doctor of Philosophy - PhD, Statistics, Doctor of Philosophy - PhD, Statistics at University of California, Los AngelesBachelor of science, Statistics, Bachelor of science, Statistics at Zhejiang University
Stackoverflow
11reputation
4kreached
0answers
1question
Github Skills (11)
google-cloud-run6
timeout6
go6
ssl6
handshake6
r-package3
interpretable-machine-learning2
iml2
machine-learning1
xai1
fairness-ml1
Programming languages (1)
Github contributions (5)
linfanz/nac-test
Dec 2020 - Sep 2022
Contributions:17 commits, 12 pushes in 1 year 8 months
linfanz/KRLS
Aug 2019 - Jun 2021
R package for machine learning technique to fit flexible, interpretable functional forms for continuous and binary outcomes.
Contributions:14 pushes, 2 branches in 1 year 10 months
r-packagelimeimlfairness-mlbinary-classification
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