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Erin LeDell is the the Chief Machine Learning Scientist at H2O.ai, where she leads the development of the H2O AutoML algorithm, an automated machine learning algorithm.

Education and Career
LeDell holds a B.Sc. and M.A. in Mathematics and a Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from the University of California, Berkeley. Her dissertation focused on "Scalable Ensemble Learning and Computationally Efficient Variance Estimation," studying ensemble machine learning methods and focusing on the Super Learner algorithm, which combines diverse base learning algorithms into a powerful prediction function through metalearning. It proposes practical solutions to reduce computational costs, introduces a generalized metalearning method for optimizing performance metrics, and presents a computationally efficient technique for estimating variance, ultimately aiming to develop scalable approaches for high-performing predictive models and efficient inference.

Prior to joining H2O, LeDell worked as a Principal Data Scientist at Wise.io and Marvin Mobile Security (acquired by Veracode in 2012) and was the founder of DataScientific, Inc.

Achievements
In addition to her work at H2O.ai, Erin LeDell co-founded R-Ladies Global and founded WiMLDS (Women in Machine Learning and Data Science) in 2013.

LeDell has presented her work as a keynote at several conferences in the past, such as JuliaCon 2022, NeurIPS 2021 , or useR 2020.

LeDell is also an active contributor to multiple open-source packages, including h2o, cvAUC , and rsparkling (now sparkling-water ). She also develops machine learning benchmarking tools with the OpenML organization. Working also at the intersection of R and Python, she has also been invited to joint R-Ladies and PyLadies events to speak about using the H2O framework in both programming languages.