Degao Peng
Engineering Manager II at Pinterest
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolDegao Peng is an engineering manager and former scientist with 11 years of experience building ML-driven systems for search, recommendation, ads, and marketplace optimization at companies including Uber, Pinterest, LinkedIn, and Airbnb. With a PhD in theoretical/computational chemistry from Duke and CS studies at Stanford, he blends rigorous quantitative modeling with production engineering to drive end-to-end solutions from query understanding to ranking and multi-objective optimization. He has hands-on experience contributing to large-scale open-source ML work such as LinkedIn’s Photon-ML, improving preprocessing, normalization, and optimization performance. Based in Mountain View, he leads teams that translate complex algorithms into scalable product features and is equally comfortable debating equations as shipping robust, high-throughput services.
11 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy (PhD) Theoretical Chemistry/Computational Chemistry, Doctor of Philosophy (PhD) Theoretical Chemistry/Computational Chemistry at Duke UniversityBachelor of Science (BS) Chemistry, Bachelor of Science (BS) Chemistry at Peking UniversityComputer Science, Computer Science at Stanford University
Chinese, Chinese, English
Github Skills (9)
algorithm10
scala10
apache-spark10
machine-learning10
optimisation10
optimizers10
linear-algebra10
optimization10
normalization10
Programming languages (3)
Github contributions (5)
linkedin/photon-ml
Mar 2015 - Aug 2016
A scalable machine learning library on Apache Spark
Role in this project:
ML Engineer
ML EngineerContributions:36 commits, 3 PRs, 1 push in 1 year 5 months
Contributions summary:Degao's commits focus on refactoring and updating the Photon-ML library. They updated the Driver class by incorporating normalization and other preprocessing steps related to feature summarization. The user also refactored and improved the optimization functions, specifically by utilizing axpy for speeding up calculation. Furthermore, they contributed to the implementation of the NormalizationContext class and its related integ tests.
scalabledata-sciencemachine-learningapachespark
TDDFT/photon-ml
May 2016 - Aug 2016
Contributions:13 pushes, 1 branch in 3 months
scalabledata-sciencemachine-learningapachespark
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