Kexin Nie
Research Scientist at Meta
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolKexin Nie is a Staff Research Scientist/Engineer in the San Francisco Bay Area with nine years of experience applying statistics and data science to large-scale production systems. Currently at Meta (Facebook), Kexin blends research rigor with engineering discipline to build reliable, high-performance anomaly detection and auto-tuning features. Their open-source contributions to Apache Pinot highlight practical backend expertise in realtime OLAP datastores, including implementing alert filter auto-tuning interfaces and endpoints that improve detection job performance. Trained at Zhejiang University and Stanford in mathematics, statistics, and innovation design, Kexin brings a rare combination of theoretical depth and product-minded systems engineering. Colleagues describe them as someone who deliberately pursues the intersection of [in]possibility—turning ambitious detection research into production-ready solutions.
9 years of coding experience
6 years of employment as a software developer
Bachelor of Science (BS) Mathematics and Statistics (Major Degree); Innovation Design (Dual Degree), Bachelor of Science (BS) Mathematics and Statistics (Major Degree); Innovation Design (Dual Degree) at Zhejiang UniversityMaster of Science (M.S.) Statistics, Master of Science (M.S.) Statistics at Stanford University
Stackoverflow
501reputation
28kreached
3answers
0questions
schema
top-5%
Github Skills (20)
api-rest10
apache-pinot10
anomaly-detection10
back-end-development10
api-design10
restful-api10
java10
javas10
rest-api10
schema9
jupyter-notebook6
ggplot6
ipython6
plot6
python6
Programming languages (2)
Github contributions (4)
apache/pinot
Feb 2017 - Oct 2018
Apache Pinot - A realtime distributed OLAP datastore
Role in this project:
Back-end Developer
Back-end DeveloperContributions:42 commits, 91 PRs, 152 pushes in 1 year 7 months
Contributions summary:Kexin primarily contributed to the development of auto-tuning features and related endpoints within the Apache Pinot project. They focused on implementing alert filter auto-tuning, including the creation of interfaces, factories, and endpoints. Their work involved modifications to the detection job resource, alert filter factory, and related utilities, demonstrating expertise in anomaly detection and system performance optimization.
apache-pinotolapjava
startreedata/thirdeye
Feb 2017 - Oct 2018
ThirdEye is an integrated tool for realtime monitoring of time series and interactive root-cause analysis.
Contributions:42 commits in 1 year 7 months
monitoringtime-seriesanomaly-detectionpinotrootcauseanalysis
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