I am currently on extended medical leave for an undetermined period of time and unable to respond to requests.

I am an associate professor at the Centre for Computational Biology (CBIO) of ARMINES/Mines Paris, Institut Curie and INSERM (which are all part of PSL Research University), and hold a Springboard Chair at PRAIRIE. My research interests revolve around machine learning techniques for therapeutic research.

En français : ma présentation du 23 novembre 2017 à l'Académie des Sciences sur la caractérisation des cancers par les données génomiques.

Short bio

2005 ­­– 2010 : PhD at UC Irvine with Pierre Baldi, working on chemoinformatics and drug design (more particularly, virtual high-throughput screening).

2011 ­– 2013 : Postdoctoral stay at the Max Planck Institutes for Developmental Biology and Intelligent Systems in Tübingen, working with Karsten Borgwardt on methods for genome-wide association studies.

December 2013 : Joined CBIO.

Supervision (current)

Élise Dumas — Evaluation of the interactions between comedications and recurrence-free survival in breast cancer from SNDS data.
PhD student at Paris Saclay since October 2019.
Joint supervision with Fabien Reyal.

Adeline Fermanian — High-dimensional inference in genomic data.
Postdoctoral researcher since November 2021.

Gwenn Guichaoua — Machine learning and systems biology to identify therapeutic targets for triple negative breast cancer.
MSc student at ENS Paris-Saclay.
Joint supervision with Véronique Stoven.

Gwenaëlle Lemoine — Network-guided analysis of biobank-scale whole genome sequencing data for therapeutic research.
Postdoctoral reasearcher since March 2022.

Ndèye Maguette Mbaye — Learning from multi-modal data to improve cancer treatment.
PhD student since September 2019.


I teach bioinformatics, machine learning and drug discovery at MINES ParisTech. I'm also teaching online courses (in French) at OpenClassrooms. See my teaching page for more details.

Le deuxième tirage de mon Introduction au Machine Learning à destination des élèves ingénieur·e·s ou mastérien·ne·s est parue en juillet 2019 aux éditions Dunod. La version électronique (pdf) sans exercices est disponible ici.

Machine Learning in Computational and Systems Biology (MLCSB)

MLCSB is a community of researchers from systems biology, computational biology and machine learning that promotes the exchange of ideas, interactions and collaborations on these topics. Formally, MLCSB is a community of special interest (COSI) of the International Society for Computational Biology (ISCB). Our main activities are the organization of workshops and the management of a mailing list (Google Group) of more than 1,000 members as of June 2021. Magnus Rattray and I have been presidents of MLCSB since March 2020.

Women in Machine Learning and Data Science

I am the co-founder of the Parisian chapter of Women in Machine Learning and Data Science. We organize events, where we discuss machine learning and data science with the purpose of building a community around women in these fields. As of June 2021, Paris WimLDS counts over 4,000 members. For more details about this and diversity in general, please check out my Diversity in STEM page.