Draft:Erin LeDell
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|  |
Erin LeDell | |
|---|---|
| Known for | |
| Academic background | |
| Alma mater | |
| Thesis | Scalable Ensemble Learning and Computationally Efficient Variance Estimation (2015) |
| Website | https://github.com/ledell |
Erin LeDell is Chief Scientist at Distributional.[1] She is known for her work on developing automated machine learning algorithms.[2]
Education and Career
[edit]LeDell holds a B.Sc. and M.A. in Mathematics[3] and a Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from the University of California, Berkeley.[2] 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][3]
Before joining Distributional, LeDell worked as Chief Machine Learning Scientist at H2O.ai, where she led the development of the H2O AutoML algorithm,[2] 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.[2][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 join 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 [cs.LG].
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 [cs.LG].
LeDell, Erin (2015). "Scalable Ensemble Learning and Computationally Efficient Variance Estimation" (PDF). GitHub. Retrieved 2024-03-21.
References
[edit]- ^ Scott Clark (2024-11-20). "Get to know Erin LeDell, Chief Scientist at Distributional". distributional blog. Retrieved 2025-01-28.
- ^ a b c d "Role Models in AI: Erin Ledell". Medium. AI4ALL. 2019-02-26. Retrieved 2024-03-21.
- ^ 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.
- ^ LeDell, Erin (2015). "Scalable Ensemble Learning and Computationally Efficient Variance Estimation" (PDF). GitHub. Retrieved 2024-03-21.
- ^ "Erin Ledell". D-Lab. University of California, Berkeley. Retrieved 2024-03-21.
- ^ R-Ladies Global. "History of R-Ladies". R-Ladies. R-Ladies Global. Retrieved 2024-03-21.
- ^ "About - Women in Machine Learning & Data Science". LinkedIn. LinkedIn Corporation. Retrieved 2024-03-21.
- ^ "JuliaCon 2022". JuliaCon. 2022. Retrieved 2024-03-21.
- ^ "Neural Information Processing Systems (NeurIPS) 2021 Virtual Conference". Neural Information Processing Systems (NeurIPS) Conference. Retrieved 2024-03-21.
- ^ "UseR 2020 Keynotes". R Project. Retrieved 2024-03-21.
- ^ "h2oai/h2o-3: H2O Open Source AI Platform". GitHub. H2O.ai. Retrieved 2024-03-21.
- ^ Erin LeDell. "ledell/cvAUC: Cross-Validated Area Under the ROC Curve Metrics". GitHub. Retrieved 2024-03-21.
- ^ "rsparkling: H2O's Sparkling Water R Package". GitHub. H2O.ai. Retrieved 2024-03-21.
- ^ "sparkling-water/r: H2O's Sparkling Water R Package". GitHub. H2O.ai. Retrieved 2024-03-21.
- ^ "OpenML/automlbenchmark". GitHub. Retrieved 2024-03-21.
- ^ "AutoML in R & Python using H2O". Meetup. 2023-02-17. Retrieved 2024-03-20.