INRIA Grenoble PhD Seminars

May 2026

Speakers :

• Alexandre Wendling, PhD student at SVH team (LJK) : Analysis of variance on genomic abundance data
  Linkedin
   Advances over the past decade in high-throughput sequencing (HTS) technologies have significantly enhanced the use of molecular methods for species identification via environmental DNA (eDNA). Metabarcoding—a technique that enables the simultaneous tagging, sequencing, and identification of multiple species from a single environmental sample (Taberlet, Coissac, Hajibabaei, & Rieseberg, 2012)—has become a cornerstone of biodiversity research (Compson et al., 2020). This approach relies on PCR amplification of taxonomically informative genetic fragments ('DNA barcodes'), which are sequenced and matched to reference datasets for species identification. However, the analysis of eDNA faces several challenges, the most important of which is the deterministic amplification bias during PCR (Gold et al., 2023). To improve the accuracy and efficiency of taxonomic assignments in eDNA metabarcoding there is a pressing need for advanced tools that can better differentiate genuine environmental signals from the noise inherent in molecular detection processes (Mathon et al., 2021). PCR amplification biases often result in amplicon sequence variants (ASVs) (Callahan, 2017), which do not correspond to real sequences, as has been observed in mock community studies. Currently, the classical way to filter out these spurious sequences is by comparing them with reference databases. However, databases are often much incomplete at least for certain taxonomies, and setting the right similarity threshold can be cumbersome in practice. To improve biodiversity descriptions from eDNA, we propose an alternative approach: leveraging the variability in the abundance of sequences across samples and PCR replicates to delineate between true and supurious ASVs without relying on reference databases. Assuming that there is more biological signal than experimental noise, for a true sequence to be considered, the variation in abundance between samples must be greater than the variation in abundance between PCR replicates. Given the nature of the data, in this analysis of variance we will address the issues of zero-inflation, compositionality and heteroscedasticity of the data, and correlation between sample.


• Florian Vincent, PhD student at Tripop team (Inria) : How is convex analysis useful in computational mechanics?
  Google Scholar Website
   In this short presentation, I will showcase how some simple convex analysis results help us shape the physical laws governing some materials in solid mechanics. This leads to techniques tailored to learn their constitutive laws efficiently with modern machine learning tools which is the main point of my PhD.    We will see how these constraints can imply "automatically" the fulfillment of the second law of thermodynamics, in order to get you convinced that convex analysis is a simple and efficient tool for this type of endeavors.