My research focuses on the development of biostatistical models, statistical methods and algorithms for the analysis and interpretation of high throughput biological data.

 1. Modeling and inferring dependence in big data settings

• Models: Multiple changepoints, Clustering, Regression
• Methods: Computational statistics, Dynamic Programming, Convex relaxation, Penalized Likelihood

2. Applications in molecular biology and bioinformatics

• DNA copy number, RNA-footprinting, Hi-C, RNA-seq, Gene networks inference
• Practical evaluation of biostatistical tools for genomics

A full list of my publication is available here.
Here are a few recent publications:

• [2019a] Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics. C. Ambroise, A. Dehman, P. Neuvial, G. Rigaill, and N. Vialaneix. In: Algorithms for Molecular

• [2019b] New efficient algorithms for multiple change-point detection with reproducing kernels. A. Celisse, G. Marot, M. Pierre-Jean, and G. Rigaill. In: Computational Statistics and Data Analysis

• [2018a] Changepoint detection in the presence of outliers. P. Fearnhead and G. Rigaill. In: Journal of the American Statistical Association

• [2018b] Homoeologous exchanges cause extensive dosage-dependent gene expression changes in an allopolyploid crop. A. Lloyd, A. Blary, D. Charif, C. Charpentier, J. Tran, S. Balzergue, E. Delannoy, G. Rigaill, and E. Jenczewski. In: New Phytologist

• [2016] Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis. G. Rigaill Guillem et al. In: Briefings in bioinformatics

• [2016] On optimal multiple changepoint algorithms for large data. R. Maidstone, T. Hocking, G. Rigaill and P. Fearnhead. In: Statistics and Computing

• [2015a] Fast tree inference with weighted fusion penalties. J. Chiquet, P. Gutierrez, and G. Rigaill. In: Journal of Computationnal and Graphical Statistics

• [2015b] T. D. Hocking, G. Rigaill, and G. Bourque. PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data. In: Proceedings of The 32nd International Conference on Machine Learning, ICML