Gilles Gasso

Gilles Gasso

Full Professor

INSA Rouen Normandy

I am a Full Professor at INSA Rouen Normandy, affilitated to the Litis Lab. My research interests include machine learning, AI and applications on object detection and classification.

CurrentIy I am the Reseach Head of INSA Rouen Normandy. From 2020 to 2022 I was the Deputy Head of LITIS Lab after being the Head of the Computer Science Department from 2013-2016. Back in 2008 and 2009, I was a visiting researcher at NEC Labs America.

  • Artificial Intelligence
  • Machine Learning, kernel methods, optimal transport, optimization
  • Object detection and classification,
  • Application on image and audio

Latest News

Job offer

1-year Post-doc position

Expected Starting date: as soon as possible but no later than end of January, 2023

Title: Machine Learning for chemical reaction prediction

Keywords: Machine Learning, Molecule Representation Learning, Deep Learning, Transfer Learning, chemical molecules synthesis, cyclopropanes.


This position is proposed in the context of the french project CYCLIA for tailoring statistical models to optimize the asymmetric synthesis cyclopropane compounds: chemical molecules representation, prediction algorithms, evaluation framework. The project is funded by the Normandy Region. It gathers 2 research teams: the Machine learning team of LITIS lab and the Biomolecules Synthethis Team of COBRA at INSA Rouen Normandy (France).

The Post-doc candidate will join the Machine learning team of the LITIS in Rouen (France), and will have the opportunity to closely collaborate with the reknown experts in Organic and Bio-organic molecules synthesis.of COBRA. The Machine learning team is composed of 15 permanent researchers and several PhD, post-doctorates. The candidate will be part of the machine learning project of the lab whose topics include statistical learning, deep learning, representation learning, domain adaptation, graph learning…

Scientific objectives

The objective of this project is to propose new machine learning methods to predict/optimize the asymmetric synthesis cyclopropane compounds that are present in the structure of many drugs, based on the chemical reactants, the catalysts, and the experimental conditions.

Research topics of interest include but are not limited to:

  • chemical molecules representation learning approaches,
  • deep learning approaches,
  • transfer learning,
  • development of new algorithmic approaches,
  • proper evaluation of designed algorithms.

Required skills

We are searching for a highly motivated candidate with:

  • A PhD degree in machine learning or related domains
  • Strong programming skills in languages such as Python.

Working environment

The post-doc position is located in Rouen a mid-size city of Normandy (France). The hosting research group has established expertise in relevant domains including statistical machine learning, deep learning, optimal transport, domain adaptation. The successful candidate will have the opportunity to work in synergy with a post-doctorant and a Ph.D. student involved in the CYCLIA project. The expected salary is 2172 euros NET per month, including healthcare.

Application instructions

The candidates should send:

  • a CV (with the publications record),
  • a motivation letter explaining the relevance of the candidate for the position
  • names and contact details of at least two references.


Gilles Gasso, gilles.gasso[AT]
Phone: (+33) 2 32 95 99 88
Samia Ainouz, samia.ainouz[AT]
Benoit Gauzere, benoit.gauzere[AT]

Selected Publications

Quickly discover relevant content by filtering publications.
(2022). Bregman Neural Networks. Proceedings of the 39th International Conference on Machine Learning.

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(2022). Convergent Working Set Algorithm for Lasso with Non-Convex Sparse Regularizers . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics.

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PhD Students

Recent Alumni

Teaching / Courses

Matrix computation

This course (in French) is mostly about numerical linear algebra.

  • Introduction to matrix calculus
  • Notion of numerical complexity
  • Matrix Factorizations (LU, Cholesky, QR factorization)
  • Solving linear system
    • Review of Least squares problem
    • Application of matrix factorizations
    • Iterative methods (Jacobi, Gauss-Seidel, relaxation)
  • Algorithms for Eigenvalues and Singular values computation

Details can be found here ITI - Méthodes Numériques pour l’Ingénieur

Machine Learning

This course introduces the conventional methods in unsupervised and unsupervised learning. It provides some (convex) optimization notions. Numerical materials are in Python.

Details can be found here ITI - Introduction au Machine Learning

Data Science

The course introduces to tools for data science. The target audience is Master students aiming pluridisciplinary skills. Numerical materials are in Python.

Details can be found here MS ESD - Ingénierie des données


  • (+33) 02 32 95 98 96
  • 685 Avenue de l'Université, Saint-Etienne du Rouvray, 76800