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Erin LeDell
Known for
Academic background
Alma mater
ThesisScalable Ensemble Learning and Computationally Efficient Variance Estimation (2015)
Websitehttps://github.com/ledell

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.[1]

Education and Career[edit]

LeDell holds a B.Sc. and M.A. in Mathematics[2] and a Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from the University of California, Berkeley.[3] 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.[4][2]

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.[5]

Achievements[edit]

In addition to her work at H2O.ai, Erin LeDell co-founded R-Ladies Global[6] and founded WiMLDS (Women in Machine Learning and Data Science) in 2013.[3][7]

LeDell has presented her work as a keynote at several conferences in the past, such as JuliaCon 2022[8], NeurIPS 2021[9], or useR 2020[10].

LeDell is also an active contributor to multiple open-source packages, including h2o[11], cvAUC[12], and rsparkling[13] (now sparkling-water[14]). She also develops machine learning benchmarking tools with the OpenML organization.[15] 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.[16]

Selected Publications[edit]

Gijsbers, Pieter; Bueno, Marcos L. P.; Coors, Stefan; LeDell, Erin; Poirier, Sébastien; Thomas, Janek; Bischl, Bernd; Vanschoren, Joaquin (2022). "AMLB: an AutoML Benchmark". arXiv:2207.12560v2.

LeDell, Erin; Porier, Sébastien (2020). H2O automl: Scalable automatic machine learning. Proceedings of the AutoML Workshop at ICML. San Diego, CA, USA.

Gijsbers, Pieter; LeDell, Erin; Thomas, Janek; Poirier, Sébastien; Bischl, Bernd; Vanschoren, Joaquin (2019). "An Open Source AutoML Benchmark". arXiv:1907.00909.

LeDell, Erin (2015). "Scalable Ensemble Learning and Computationally Efficient Variance Estimation" (PDF). GitHub. Retrieved 2024-03-21.

  1. ^ "Role Models in AI: Erin Ledell". Medium. AI4ALL. 2019-02-26. Retrieved 2024-03-21. This is what we call 'H2O AutoML' and this is the team that I lead.
  2. ^ a b "Speaker Profile: Erin LeDell". O'Reilly Artificial Intelligence Conference 2018. O'Reilly Media. Retrieved 2024-03-21. Erin holds a PhD from the University of California, Berkeley, where her research focused on scalable machine learning and statistical computing, as well as a BS and MA in mathematics.
  3. ^ a b "Role Models in AI: Erin Ledell". Medium. AI4ALL. 2019-02-26. Retrieved 2024-03-21.
  4. ^ LeDell, Erin (2015). "Scalable Ensemble Learning and Computationally Efficient Variance Estimation" (PDF). GitHub. Retrieved 2024-03-21.
  5. ^ "Erin Ledell". D-Lab. University of California, Berkeley. Retrieved 2024-03-21.
  6. ^ R-Ladies Global. "History of R-Ladies". R-Ladies. R-Ladies Global. Retrieved 2024-03-21.
  7. ^ "About - Women in Machine Learning & Data Science". LinkedIn. LinkedIn Corporation. Retrieved 2024-03-21.
  8. ^ "JuliaCon 2022". JuliaCon. 2022. Retrieved 2024-03-21.
  9. ^ "Neural Information Processing Systems (NeurIPS) 2021 Virtual Conference". Neural Information Processing Systems (NeurIPS) Conference. Retrieved 2024-03-21.
  10. ^ "UseR 2020 Keynotes". R Project. Retrieved 2024-03-21.
  11. ^ "h2oai/h2o-3: H2O Open Source AI Platform". GitHub. H2O.ai. Retrieved 2024-03-21.
  12. ^ Erin LeDell. "ledell/cvAUC: Cross-Validated Area Under the ROC Curve Metrics". GitHub. Retrieved 2024-03-21.
  13. ^ "rsparkling: H2O's Sparkling Water R Package". GitHub. H2O.ai. Retrieved 2024-03-21.
  14. ^ "sparkling-water/r: H2O's Sparkling Water R Package". GitHub. H2O.ai. Retrieved 2024-03-21.
  15. ^ "OpenML/automlbenchmark". GitHub. Retrieved 2024-03-21.
  16. ^ "AutoML in R & Python using H2O". Meetup. 2023-02-17. Retrieved 2024-03-20.
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