Guillem Rigaill

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 (link to google scholar).

Here are a few recent publications:

• [pre-print] Online Multivariate Changepoint Detection: Leveraging Links With Computational Geometry. L. Pishchagina, G. Romano, P. Fearnhead, V. Runge and G. Rigaill. In: arXiv preprint arXiv:2311.01174
• [pre-print] Ms. FPOP: an exact and fast segmentation algorithm with a multiscale penalty. A. Liehrmann, G Rigaill. In: arXiv preprint arXiv:2303.08723

• [2023a] Fast online changepoint detection via functional pruning CUSUM statistics. G. Romano, I.A. Eckley, P. Fearnhead, and G. Rigaill. In: Journal of Machine Learning Research
• [2023b] DiffSegR: an RNA-seq data driven method for differential expression analysis using changepoint detection. A. Liehrmann, E. Delannoy, A. Launay-Avon, E. Gilbault, O. Loudet, B. Castandet and G. Rigaill. In: NAR Genomics and Bioinformatics
• [2023c] Adjusting the adjusted Rand Index: A multinomial story. M. Sundqvist, J. Chiquet, G. Rigaill. In: Computational Statistics

• [2021a] Detecting abrupt changes in the presence of local fluctuations and autocorrelated noise. G. Romano, V. Runge, G. Rigaill, and P. Fearnhead. In: Journal of the American Statistical Association
• [2021b] Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models. A. Liehrmann, G. Rigaill, and Toby Dylan Hocking. In BMC bioinformatics
• [2021c] M. Guilcher, A. Lierhmann, C. Seyman, T. Blein, G. Rigaill, B. Castandet, and E. Delannoy “Full length transcriptome highlights the coordination of plastid transcript processing. In: International journal of molecular sciences

• [2020a] Fast tree aggregation for consensus hierarchical clustering. A. Hulot, J. Chiquet, F. Jaffrézic, and G. Rigaill. In: BMC bioinformatics
• [2020b] A log-linear time algorithm for constrained changepoint detection. T.D. Hocking, G. Rigaill, P. Fearnhead, and G. Bourque. In: JMLR

• [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

Habilitation (in french) : TEL-HAL - slides

Ph.D.: TEL-HAL