Peizhen Guo
Senior Software Engineer at Google
New York City Metropolitan Area United States
Summary
👤
Senior🎓
Top SchoolPeizhen Guo is a Senior Software Engineer at Google with a PhD in Computer Science from Yale and six years of experience building high-performance distributed systems, data analytics frameworks, and deep learning infrastructure. Previously on Google Cloud’s NetInfra team and as an intern at Facebook, Peizhen worked on runtime-adaptive optimization and skew-aware operators for big data engines, translating research into production improvements. He contributes to notable open-source projects like Presto, adding quantile computation and statistics aggregation features that improve query planning and execution. Comfortable across systems, networking, and edge computing, he combines academic rigor with hands-on engineering to optimize performance at scale. Based in the New York City area, he brings a research-first mindset to practical problems, often surfacing subtle statistical and memory-related optimizations that yield measurable gains.
5 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at Yale UniversityBS Electrical and Electronics Engineering, BS Electrical and Electronics Engineering at Tsinghua UniversityUniversity of California, Los AngelesBeijing jingshan school
Github Skills (11)
javas10
query-optimization10
presto10
sql10
java10
data-model10
user-data10
data-set10
big-data9
hadoop8
hive8
Programming languages (1)
Github contributions (3)
prestodb/presto
Jun 2020 - Aug 2020
The official home of the Presto distributed SQL query engine for big data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:13 commits, 9 PRs, 38 comments in 2 months
Contributions summary:Peizhen primarily contributed to the `presto` query engine by implementing new functionalities for quantile calculations and statistics aggregation. They added a `quantile_at_value` function to compute quantiles based on input values, alongside associated test cases and documentation updates. The user also made changes related to memory limits on broadcasted tables and enabling statistics aggregation for temporary tables, suggesting involvement in query optimization and performance enhancements. Furthermore, they addressed concerns related to statistics for temporary tables in the Hive metastore, improving query execution.
distributed-sqlquerybigdataquery-enginesql
pguofb/presto
Jun 2020 - Aug 2020
The official home of the Presto distributed SQL query engine for big data
Contributions:79 pushes, 12 branches in 2 months
querybigdataquery-enginesqlbig-data
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