Fangyuan Deng - Graduate Student

Fangyuan Deng

Graduate Student

New York, New York, United States

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Summary

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Senior

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Top School

Fangyuan Deng is a data-driven graduate student at Columbia University specializing in quantitative methods, data analytics, visualization, and machine learning, with a strong economics foundation from the University of Miami (magna cum laude). With a decade of technical experience, she combines rigorous statistical training—linear and logistic regression, PCA, clustering, text and social network analysis—with practical software contributions to high-performance analytics systems. Notably, she has contributed backend bug fixes, performance improvements, and unit tests to prominent open-source projects like Apache Druid, focusing on materialized views and fast restart reliability. A detail-oriented problem solver, Fangyuan excels at translating quantitative research models into actionable insights for economic and business problems. Multilingual (Mandarin native, fluent English, advanced Japanese) and culturally curious, she thrives in diverse environments and enjoys learning new languages and exploring travel-inspired perspectives.

11 years of coding experience

Master's degree, Quantitative Methods in the Social Sciences, Master's degree, Quantitative Methods in the Social Sciences at Columbia University

Bachelor of Arts - BA, Economics, 3.991/4, Bachelor of Arts - BA, Economics, 3.991/4 at University of Miami

Chinese, Japanese

Github Skills (9)

javas10

apache-druid10

back-end-development10

materialized-view10

java10

data-storage9

unit-testing9

performance-optimization8

sql5

Programming languages (2)

JavaC++

Github contributions (5)

apache/druid

Jan 2019 - Dec 2019

Apache Druid: a high performance real-time analytics database.

Role in this project:

Back-end Developer

Contributions:5 commits, 11 PRs, 23 comments in 11 months

Contributions summary:Fangyuan primarily contributed to bug fixes and improvements within the Apache Druid codebase, specifically related to the materialized view feature. Their work involved addressing issues with post-aggregators, concurrent modification errors, and incorrect size calculations within the derivative data source manager. They also contributed to the historical fast restart by lazy loading column metadata. The user's contributions involved unit tests and code refactoring to improve performance and reliability.

apache-druiddatabasedruid

pzhdfy/test

Oct 2019 - Dec 2024

Contributions:7 pushes in 5 years 2 months

test2

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