Gilles Louppe

Career

Gilles Louppe completed a PhD in computer science at the University of Liège from 2010 to 2014, supervised by Pierre Geurts, with a thesis titled Understanding Random Forests, From Theory to Practice. During the same years Louppe was an FRS-FNRS Research Fellow at Liège and a core developer of scikit-learn.²⁴

Louppe then held a Marie-Curie fellowship at CERN from 2014 to 2015 and was a Postdoctoral Associate at New York University's Center for Data Science and Center for Cosmology and Particle Physics from 2015 to 2017. Louppe returned to Liège as an Assistant Professor in 2017 and has been a Professor there since 2023, leading the Science with AI Lab at the Montefiore Institute.²¹

Research

Louppe's research develops deep learning models and statistical methods for scientific applications, with an emphasis on Bayesian inverse problems in the physical sciences. The CV describes work on simulation-based inference with deep neural networks, carried out with collaborators in particle physics, astrophysics, astronomy, biophysics, and neuroscience, and names Earth and climate sciences as the next area of focus.¹²

With Kyle Cranmer and Johann Brehmer, Louppe wrote "The frontier of simulation-based inference" (2019), a review prepared for the Sackler Colloquia of the US National Academy of Sciences. Louppe has co-organized the NeurIPS workshop on Machine Learning and the Physical Sciences and serves as an area chair for NeurIPS, ICML, ICLR, and AISTATS.⁵²

Teaching and open-source work

Louppe teaches multi-year courses in Introduction to AI, Foundations of Data Science, Deep Learning, and Advanced Machine Learning, and publishes all course materials openly. Louppe was a core developer of scikit-learn from 2010 to 2015 and is listed among the project's emeritus maintainers; the library is distributed under the BSD 3-Clause License.²³⁶

Work and contributions

• scikit-learn Open-source code

  Core developer of scikit-learn from 2010 to 2015 according to Louppe's CV, and listed among the emeritus maintainers on the project's About page.²³⁶

• Course materials (Introduction to AI, Deep Learning, Advanced Machine Learning) Published research

  Teaches multi-year courses including Introduction to AI, Foundations of Data Science, Deep Learning and Advanced Machine Learning, with all materials published openly.²

• The frontier of simulation-based inference Published research

  Co-author, with Kyle Cranmer and Johann Brehmer, of a 2019 review of simulation-based inference written as proceedings for the Sackler Colloquia of the US National Academy of Sciences.⁵

Timeline

1. 2010Began a PhD in computer science at the University of Liège and became a core developer of scikit-learn.²
2. 2014Started a Marie-Curie Research Fellowship at CERN.²
3. Jul 28, 2014Posted the PhD thesis Understanding Random Forests, From Theory to Practice.⁴
4. 2015Joined New York University as a Postdoctoral Associate in data science and particle physics.²
5. 2017Became an Assistant Professor at the University of Liège.²
6. Nov 4, 2019Co-authored "The frontier of simulation-based inference" with Kyle Cranmer and Johann Brehmer.⁵
7. 2023Became a Professor at the University of Liège.²

Other documented roles

Roles the sources document that are not confirmed as current.

• Postdoctoral Associate, Center for Data Science and Center for Cosmology and Particle Physics (2015–2017), New York University²
• Marie-Curie Research Fellow (2014–2015), CERN²
• Assistant Professor (2017–2022); FRS-FNRS Research Fellow (2010–2014), University of Liège²

Where they work

• University of Liège 1 person in the wiki

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