Main research interests
Computational statistics for genomics
Selection of 3 major recent publications
Chiquet J, Gutierrez P, Rigaill G. . Fast tree inference with weighted fusion penalties.
Journal of Computational and Graphical Statistics
Rigaill G, Hocking T, Vert JP, Bach F. Learning sparse penalties for change-point detection using max margin interval regression. ICML : In Proceedings of The 30th International Conference on Machine Learning.
Zaag R, Tamby JP, Guichard C, Tariq Z, Rigaill G, Delannoy E, Renou JP, Balzergue S, Mary-Huard T, Aubourg S, Martin-Magniette ML, Brunaud V. GEM2Net : from gene expression modeling to -omics networks, a new CATdb module to investigate Arabidopsis thaliana genes involved in stress response. Nucleic Acids Research.
accept ́ dans Journal of Computational and Graphical Statisti
Guillem Rigaill is Lecturer at Evry University. He is a member of Genomic Networks Team at the Institute of Plant Sciences Paris-Saclay. He received his PhD from AgroParisTech in 2010 for the development of algorithms and statistical methods for the analysis of breast cancer data.
His research interests focus on the development of efficient algorithms and appropriate statistical methodologies for the analysis of high-dimensional genomic and transcriptomic data. He has been developing new models for change-point detection and differential analysis and proposed inference procedures for these models that are both statistically and algorithmically efficient.Since 2007 he has applied those new tools in a number interdisciplinary projects involving cancer and plant biologists, bioinformaticians and statisticians.
Institute of Plant Sciences Paris-Saclay (IPS2)
Rue de Noetzlin
Plateau du Moulon