Hello there, I am Romain Egelé and here is my portfolio!

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I am a PhD student working on “Automated Data-Centric Deep Learning” under the joint supervision of Prof. Isabelle Guyon and Dr. Prasanna Balaprakash. I have been working on the topic of Automated Machine Learning since 2018.

Projects

DeepHyper

Open In Colab

Star Fork

Checkout the documentation

Scale Your Search

An Automated Machine Learning tool-box with hyperparameter optimization, neural architecture search, uncertainty quantification. Exectuable from a single-laptop to a large scale supercomputer with thousands of parallel workers.


Data-Centric AI

The science of systematically engineering the data of an AI system.

My Contributions

Talks

  • Learning to Discover at Institut Pascal Paris-Saclay (April 2022) [video]

Others Resources

Hubs

  • Data-Centric AI Resource Hub [link]
  • HazyResearch/data-centric-ai [link]

Benchmarks

  • DCAI benchmark [link]

Workshops

  • NeurIPS - DCAI Workshop (December 2021) [link]
  • HAI Stanford University - DCAI Virtual Workshop (November 2021) [link]
  • Snorkel - The future of DCAI (October 2021) [link]
research

2022

  • [preprint] Egele, Romain, Joceran Gouneau, Venkatram Vishwanath, Isabelle Guyon, and Prasanna Balaprakash. “Asynchronous Distributed Bayesian Optimization at HPC Scale”. arXiv, 2022. https://doi.org/10.48550/ARXIV.2207.00479.

2021

  • [preprint] Egele, Romain, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, and Prasanna Balaprakash. “AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.” arXiv preprint arXiv:2110.13511 (2021).
  • Egele, Romain, Prasanna Balaprakash, Isabelle Guyon, Venkatram Vishwanath, Fangfang Xia, Rick Stevens, and Zhengying Liu. “AgEBO-tabular: joint neural architecture and hyperparameter search with autotuned data-parallel training for tabular data.” In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1-14. 2021.

2020

  • Maulik, Romit, Romain Egele, Bethany Lusch, and Prasanna Balaprakash. “Recurrent neural network architecture search for geophysical emulation.” In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1-14. 2020.

2019

  • Balaprakash, Prasanna, Romain Egele, Misha Salim, Stefan Wild, Venkatram Vishwanath, Fangfang Xia, Tom Brettin, and Rick Stevens. “Scalable reinforcement-learning-based neural architecture search for cancer deep learning research.” In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1-33. 2019.