Edward Gan
Member Of Technical Staff at Anthropic
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolEdward Gan is a seasoned software engineer with 13 years of experience building scalable ML and data infrastructure across industry and academia, currently a Member of Technical Staff at Anthropic after roles at Scale AI, Waymo, and Databricks. He pairs a Stanford PhD in computer science with hands-on production work—past contributions include ML experiment management, post-training evaluation platforms, and prediction-model evaluation tooling. His open-source work on projects like MacroBase and Generalized Random Forests highlights deep expertise in scalable analytics and model-centric code (KDE, KDTree, and local linear forest optimizations). Comfortable moving between research and production, he has a track record of improving core algorithms and performance-critical systems that power data quality and model monitoring. Notably, he’s contributed low-level algorithmic fixes that directly improved outlier detection and prediction accuracy, reflecting an engineer who cares about both correctness and throughput.
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 UniversityHigh School Diploma, High School Diploma at Montgomery Blair High SchoolDoctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Stanford University
Github Skills (13)
algorithm10
data-structures10
javas10
algorithms10
machine-learning10
c-language10
cprogramming-language10
data-structure10
java10
outlier-detection10
testing9
data-analysis9
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
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.