The DeepHyper project is led by Prasanna Balaprakash (pbalapra[at]anl[dot]gov) and co-led by Romain Egele (romainegele[at]gmail[dot]com).

Other co-maintainers:

  • Joceran Gouneau (joceran.g[at]gmail[dot]com)
  • Kyle Gerard Felker (felker[at]anl[dot]gov)

Major contributors:

  • Stefan Wild - Conceptualization, advisor on optimization
  • Venkat Vishwanath - Conceptualization, advisor on scaling and applciations
  • Romit Maulik - Neural architecture search with stacked LSTM for sea-surface temperature prediction, Automated Deep ensembles with uncertainty quantification, Documentation
  • Shengli Jiang - Neural architecture search with graph neural network for molecular data, Documentation
  • Bethany Lusch - Automated deep ensembles with uncertainty quantification, Documentation
  • Misha Salim - Basis of hyperparameter search and parallel execution of jobs with Balsam
  • Matthieu Dorier - Autotuning of HEPnOS software runtime

Other contributors:

  • Dipendra Kumar Jha - Basis of neural architecture search
  • Elise Jennings - Hyperparameter search with time reduction
  • Felix Perez - Refactoring of Evaluator API with AsyncIO, Documentation
  • Hongyuan Liu - Light fix
  • Sun Haozhe - Light fix
  • Zachariah Carmichael - Light fix
  • Denis Boyda - Light fix and tutorials
  • Ian Wixom - Tutorials

If you have contributed to the DeepHyper project but your name is not listed, please contact us.

BibTex Citation:

@misc{deephyper_software,
title = {"DeepHyper: A Python Package for Scalable Neural Architecture and Hyperparameter Search"},
author = ,
organization = {DeepHyper Team},
year = 2018,
url = {https://github.com/deephyper/deephyper}
}