Edward Gan
Member Of Technical Staff at Anthropic
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolEdward Gan is a Member of Technical Staff at Anthropic with 13 years of experience building production ML infrastructure and data-quality platforms. He holds an MS and PhD from Stanford and a BA in Computer Science and Mathematics from Harvard, and has moved between research and industry roles at Scale AI, Waymo, and Databricks. At Scale he led post-training/model-eval platforms powering Precog and public leaderboards; at Waymo and Databricks he focused on prediction-model evaluation, experiment management, and automated data/model quality monitoring. He is an active open-source contributor who has improved algorithmic components like KDTree/TreeKDE for MacroBase outlier detection and optimized Local Linear Forest implementations in the GRF project, reflecting strength in both low-level algorithmic tweaks and systems engineering. Based in New York, he combines academic rigor with pragmatic production experience to deliver high-performance, reliable ML tooling.
13 years of coding experience
7 years of employment as a software developer
Bachelor of Arts (A.B.), Computer Science and Mathematics, Bachelor of Arts (A.B.), Computer Science and Mathematics at Harvard UniversityMontgomery Blair High SchoolMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at Stanford University
Github Skills (15)
algorithm10
data-structures10
javas10
algorithms10
machine-learning10
c-language10
datastructures10
outlier-detection10
data-structure10
java10
c-programming-language10
datastructure10
data-analysis9
testing9
econometrics9
Programming languages (8)
Github contributions (5)
stanford-futuredata/macrobase
Apr 2016 - Jan 2020
MacroBase: A Search Engine for Fast Data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:50 commits, 40 PRs, 122 pushes in 3 years 9 months
Contributions summary:Edward primarily focused on fixing bugs and improving the `KDTree` and `TreeKDE` components related to the `MacroBase` library. They addressed data ordering and classifier dumping issues within `KDTree.java` and `TreeKDE.java` files. Their work involved refining the KDE (Kernel Density Estimation) algorithm, including adjustments to distance calculations and score estimation, crucial for outlier detection performance. Additionally, the user made modifications to the `BatchingPercentileClassifier` including improvements to the data processing and cutoff calculation logic.
dataframesmacrosquery-languagesearch-enginedatabase
grf-labs/grf
Jun 2018 - Jul 2018
Generalized Random Forests
Role in this project:ML Engineer
ML EngineerContributions:5 commits, 1 PR, 3 comments in 18 days
Contributions summary:Edward made several code changes related to optimizing and testing a Local Linear Forest implementation within the Generalized Random Forest (GRF) framework. Their work primarily focused on refining the prediction strategy, including sparse diagonal weights optimization and improvements to the underlying linear prediction logic. The user also addressed testing and benchmarking aspects of the Local Linear Forest, demonstrating a focus on performance and accuracy. This indicates a strong involvement in the core machine learning components of the GRF project.
random-forestdata-sciencemachine-learningforestscausal-inference
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