Hello there, I am Romain Égelé and I love science!

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I am a doctor in computer science on the “Optimization of Learning Workflows at Large Scale on High-Performance Computing Platforms”. I obtained my Ph.D. in June 2024 under the joint supervision of Prof. Isabelle Guyon and Dr. Prasanna Balaprakash. I have been working on the topics Neural Architecture Search and Hyperparameter optimization since 2018. For a few years now, I have been interested in Uncertainty Quantification in Machine Learning.

Projects

DeepHyper

Open In Colab

Star Fork

Checkout the documentation

Scale and Automate the Developement of Your Machine Learning Workflows

An Automated Machine Learning tool-box with hyperparameter optimization, neural architecture search, uncertainty quantification, multi-fidelity, warm start. Exectuable from a single-laptop to the largest supercomputer in the world (i.e., used on Frontier at Oak Ridge National Laboratory in 2024).


Data-Centric AI

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

  • Romain Egele, Julio C. S. Jacques Junior, Jan N. van Rijn, Isabelle Guyon, Xavier Baró, Albert Capés, Prasanna Balaprakash, Sergio Escalera, Thomas Moeslund, Jun Wan. “AI Competitions and Benchmarks: Dataset Development” arXiv preprint arXiv:2404.09703 (2024). (ArXiv Link)
research

2024

  • [preprint] Sun, Yixuan, Ololade Sowunmi, Romain Egele, Sri Hari Krishna Narayanan, Luke Van Roekel, and Prasanna Balaprakash. “Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach.” arXiv preprint arXiv:2404.05768 (2024).
  • [preprint] Egele, Romain, Felix Mohr, Tom Viering, and Prasanna Balaprakash. “The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization.” arXiv preprint arXiv:2404.04111 (2024).

2023

  • [Best Paper Award] Egelé, Romain, Isabelle Guyon, Venkatram Vishwanath, and Prasanna Balaprakash. “Asynchronous decentralized bayesian optimization for large scale hyperparameter optimization.” In 2023 IEEE 19th International Conference on e-Science (e-Science), pp. 1-10. IEEE, 2023.
  • Maulik, Romit, Romain Egele, Krishnan Raghavan, and Prasanna Balaprakash. “Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles.” Physica D: Nonlinear Phenomena 454 (2023): 133852.
  • Egele, Romain, Isabelle Guyon, Yixuan Sun, and Prasanna Balaprakash. “Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?.” In the 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2023.
  • [preprint] Egele, Romain, Tyler Chang, Yixuan Sun, Venkatram Vishwanath, and Prasanna Balaprakash. “Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives.” arXiv preprint arXiv:2309.14936 (2023).
  • [preprint] Dash, Sajal, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, Guojing Cong, Feiyi Wang, and Prasanna Balaprakash. “Optimizing Distributed Training on Frontier for Large Language Models.” arXiv preprint arXiv:2312.12705 (2023).

2022

  • Dorier, Matthieu, Romain Egele, Prasanna Balaprakash, Jaehoon Koo, Sandeep Madireddy, Srinivasan Ramesh, Allen D. Malony, and Rob Ross. “Hpc storage service autotuning using variational-autoencoder-guided asynchronous bayesian optimization.” In 2022 IEEE International Conference on Cluster Computing (CLUSTER), pp. 381-393. IEEE, 2022.
  • Egele, Romain, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, and Prasanna Balaprakash. “Autodeuq: Automated deep ensemble with uncertainty quantification.” In 2022 26th International Conference on Pattern Recognition (ICPR), pp. 1908-1914. IEEE, 2022.

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.