Erin LeDell

Erin LeDell

Chief Machine Learning Scientist at

Erin LeDell is the Chief Machine Learning Scientist at, the company that produces the open source, distributed machine learning platform, H2O. At, she leads the H2O AutoML project and her current research focus is automated machine learning. Before joining, she was the Principal Data Scientist at (acquired by GE) and Marvin Mobile Security (acquired by Veracode), the founder of DataScientific, Inc. and a software engineer at a large consulting firm. She is also founder of the Women in Machine Learning and Data Science (WiMLDS) organization ( and co-founder of R-Ladies Global ( Erin received her Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from University of California, Berkeley and has a B.S. and M.A. in Mathematics.


Scalable & Responsible Automatic Machine Learning (AutoML)

Automatic Machine Learning (AutoML) is a subfield of machine learning which aims to automate the training & tuning of machine learning models. One of the main goals of an AutoML tool is to train the “best” model possible in the least amount of computation time, with zero/minimal configuration by the user. AutoML tools reduce the expertise required for practitioners to train powerful machine learning models, which has expanded and accelerated the application of machine learning to problems in both academic research and industry. AutoML greatly speeds up the workflow and efficiency of even the most experienced data scientist. 

As automation and use of machine learning increases, in particular with the proliferation of open source AutoML tools, there’s an increased risk in misuse of, or harm by, machine learning models used in real world applications. In order to reduce the risk of harmful models being deployed, machine learning tools, and especially AutoML tools, can offer easy-to-use or automated explainability, interpretability and algorithmic fairness methods that can be used to evaluate and probe machine learning models.