Erin Ledell
Chief Scientist at Distributional
Oakland, California, United States
Summary
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Rockstar🎓
Top SchoolErin LeDell is a Chief Scientist and founder with 11 years of experience building and evaluating production-grade AI systems, currently leading scientific strategy at Distributional to automate deep statistical testing across the AI lifecycle. She previously led H2O.ai’s AutoML development as Chief Machine Learning Scientist, helping pioneer one of the first open-source enterprise AutoML platforms and advancing explainable AI, fairness, and benchmarking. As founder of DataScientific, Inc., she blends consulting on LLMs, model evaluation, and product strategy with hands-on engineering and deployment experience involving Docker and AWS. A UC Berkeley PhD in Biostatistics with a computational emphasis, Erin pairs rigorous quantitative research with practical tooling—her open-source contributions include extending the popular H2O-3 platform and improving the OpenML AutoML benchmarking framework to integrate H2OAutoML and TPOT. Notably, her work often bridges test design and infrastructure, ensuring models are both statistically sound and deployable at scale.
11 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD Biostatistics with Designated Emphasis on Computational Science and Engineering, Doctor of Philosophy - PhD Biostatistics with Designated Emphasis on Computational Science and Engineering at University of California, Berkeley
Stackoverflow
8,769reputation
365kreached
297answers
0questions
machine-learning
top-5%
r
top-5%
deep-learning
top-5%
python
top-5%
Github Skills (24)
unit-testing10
benchmark10
python10
r10
machine-learning10
benchmarking10
model-validation10
statistical-models10
automl10
dockers9
docker9
cross-validation9
deep-learning9
aws8
random-forest6
Programming languages (12)
Github contributions (5)
h2oai/h2o-3
Jun 2015 - Jan 2023
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:312 reviews, 754 commits, 325 PRs in 7 years 8 months
Contributions summary:Erin primarily contributed to cleaning and improving R scripts related to the testing of generalized linear models (GLM) in the H2O library, and included the integration of weights. The code changes centered on test logic cleanup, formatting improvements, and bug fixes related to weight calculations, and they verified the equivalence of models trained on weights vs. repeated rows. The user also added several new cross-validation checks to ensure accuracy across various scenarios.
xgboostgampythonk-meansautoencoders
openml/automlbenchmark
Aug 2018 - Jan 2022
OpenML AutoML Benchmarking Framework
Role in this project:ML Engineer
ML EngineerContributions:3 reviews, 68 commits, 4 PRs in 3 years 5 months
Contributions summary:Erin primarily contributed to the development and enhancement of the AutoML benchmarking framework. Their work included modifying the codebase to utilize a public OpenML API key, indicating a focus on integrating with data sources. The commits demonstrate the integration of H2OAutoML and TPOT frameworks, which also highlights the user's efforts towards expanding the AutoML capabilities. Furthermore, the changes in the code related to the AWS infrastructure and the use of Docker containers also indicates their experience with deploying the machine learning models.
benchmarkingopenmlmachine-learningbenchmarkautoml
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