Guillem Rigaill

My research focuses on the development of statistical 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: Dynamic Programming, Convex relaxation, Penalized Likelihood

2. Applications in molecular biology and bioinformatics

• RNA analysis (RNA-seq, RNA-footprinting, RIP-seq…), Chip-Seq, DNA copy-number analysis
• Practical evaluation of statistical tools for genomics

A list of my publications can be found on Google Scholar.

• [2025] Online Multivariate Changepoint Detection: Leveraging Links With Computational Geometry.
  L. Pishchagina, G. Romano, P. Fearnhead, V. Runge and G. Rigaill.
  Journal of the Royal Statistical Society Series B: Statistical Methodology Journal ArXiv

• [2024] Ms. FPOP: an exact and fast segmentation algorithm with a multiscale penalty.
  A. Liehrmann, G. Rigaill.
  Journal of Computational and Graphical Statistics Journal and ArXiv

• [2023] Fast online changepoint detection via functional pruning CUSUM statistics.
  G. Romano, I.A. Eckley, P. Fearnhead, and G. Rigaill.
  Journal of Machine Learning Research Journal and ArXiv

• [2023] 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.
  NAR Genomics and Bioinformatics Journal

• [2023] Adjusting the adjusted Rand Index: A multinomial story.
  M. Sundqvist, J. Chiquet, G. Rigaill.
  Computational Statistics Journal ArXiv

Habilitation (a.k.a HDR) in applied Mathematics in 2020 at the Hadamard Doctoral School of Mathematics

My manuscript (in French) and slides are here:

• HDR-Manuscript
• HDR-Slides

Ph.D.in applied Mathematics in 2010 at the ABIES Doctoral School

My manuscript (in English) is here:

• PhD-Manuscript