Dr. Benoit Liquet is a Professor at UPPA (France). In addition he is affiliated with ACEMS as research fellow.
He was previously senior lecturer in Statistics at the University Of Queensland (from 2013-2015), Senior Investigator Statistician at MedicalResearch Council Biostatistics Unit in Cambridge (from 2012-2013), Associate Professor at Bordeaux University (from 2017-2012). Throughout his career he has extensively worked in developing novel statistical models mainly to provide novel tools to analyse clinical, health and biological data arising from epidemiological studies. More recently (since 2011), he moved to the field of computational biology and generalised some of these methods so that they scale to high throughput ("omic") data.
In his new position at UPPA, Benoît Liquet is an active member of the LMAP (Laboratory of Mathematics and Its Applications - UMR CNR 5142) and a member of MIRA ("Milieux et resources aquatiques") where he is developing an interest in how climate change impacts marine ecosystems.
Besides his many overseas collaborations (England, Denmark, Australia, Canada), he is currently working in collaboration with the Basque Country University (UPV/EHU) on novel statistical methods for the analysis of high-dimensional data and Big Data.
To date, he has published 42 articles in international peer-reviewed journals in addition to three book sections and two books on R software. He has also co-authored two books on dynamic biostatistical models. He is the contributor/maintainer of more than 12 R packages available on CRAN(https://cran.r-project.org/). His h-index is 17 (Google Scholar, October 2016) with more than 700 citations.
Benoît Liquet is also an accomplished athlete. After playing in national level in badminton (won the national French University title by team in 2012 and 8th place to the Europeen one), he moved to triathlon competition and more recently to IRONMAN distance. He has qualified three times (2014, 2015 and 2017) for the IRONMAN world championship in Hawaii.
In this project we consider the challenging task of developing fully Bayesian sparse analyses for the situations when the numbers of predictors is larger than observations for complex responses and covariates grouped by blocks with the sparsity for blocks and cases.
Liquet, B.., P. de Micheaux L., Hejblum B. P., & Thiébaut R.
(2016). Group and sparse group partial least square approaches applied in genomics context. Bioinformatics. 32(1), 35-42. doi: 10.1093/bioinformatics/btv535