AI / ML Leader · Product & Platform Strategy · Research ↔ Production
PhD Computer Science · MSc Artificial Intelligence · Four years line-managing ML teams at BASF Digital Solutions. I build the bridge between research rigor and production pragmatism.
Leading ML teams is a translation problem — between research and production, between long-term R&D and short-term commitments, between what models can do and what the business actually needs. I have spent the last four years doing that translation at BASF Digital Solutions, scaling direct reports from 8 to 16 within a 60-person multi-function portfolio, and leading two production ML platforms adopted across multiple internal teams.
My path is deliberate: PhD in Computer Science (Bordeaux), MSc in Artificial Intelligence, postdoctoral research applying ML to gene regulatory networks, first-author publications in Q1 journals and IEEE, and competitive research grants as main author — followed by industry because the most interesting ML problems are the ones that have to ship. Portfolio decisions, team scaling, and platform thinking are where I operate best.
Four initiatives that illustrate how I bridge research and production — from academic tools adopted by bioinformatics communities to enterprise ML platforms with measurable business impact.
Led a Computer Vision platform offering reusable templates for classification, segmentation and detection — now in production across multiple internal teams. Delivered multi-million-euro cumulative cost savings during my ownership.
Led evolution and maintenance of a self-service time-series forecasting platform aimed at business users rather than data scientists — generating significant recurring annual savings for the organisation.
Interactive web platform transforming high-throughput gene data into actionable biological insights. Published in NAR Genomics and Bioinformatics (Q1, 2020). Used by researchers as an alternative to classical enrichment analysis.
Contributed to an internal ontology integrating regulatory and biological data. Enabled flexible data extraction, improved decision-making and reduced manual effort in crop-protection workflows. Published in Frontiers in AI (2023).
First-author peer-reviewed publications in Q1 journals and IEEE proceedings. Click on any item to expand details.
GSAn: a Web server as an alternative to enrichment analysis for annotating gene sets
Presents GSAn, a web server offering an alternative to classical over-representation analysis. Clusters semantic relationships between annotation terms to provide richer, more interpretable gene-set annotations.
A new method for evaluating the impacts of semantic similarity measures on the annotation of gene sets
Introduces a novel evaluation methodology for semantic similarity measures applied to gene set annotation, quantifying how measure choice affects downstream biological interpretation.
Deciphering gene sets annotations with ontology-based visualization
Proposes an ontology-driven visualization approach for interpreting gene set annotations. Invited talk — travel grant awarded by the Société Française de BioInformatique (SFBI).
Development and Characterization of a Fixed Repertoire of Blood Transcriptome Modules Based on Co-expression Patterns Across Immunological States
Large multi-author collaboration producing a reproducible module framework for blood transcriptome analysis across immunological conditions.
EPPO Ontology Application in Crop Protection
Contributed to the development of an internal ontology integrating regulatory and external biological data for crop-protection workflows. Enabled flexible data extraction and reduced manual effort.
rGSAn: An R package dedicated to gene set analysis using semantic similarity measures
Presented the R implementation of GSAn at the useR! Conference, opening the methodology to the broader R and bioinformatics community.
100+ hours of university teaching at the masters and bachelor's level, plus mentoring of 25+ students across four academic years — covering bioinformatics, databases and computer science fundamentals.
| Course | Institution & Level | Year | Hours |
|---|---|---|---|
| Applied Functional Genomics (VT20) | Umeå University · Master in Biology (2nd semester) | 2019–2020 | 40 |
| Introduction to Databases (M1104) | Université de Bordeaux · DUT Informatique | 2017–2018 | 54 |
| Introduction to Environment Systems (M1101) | Université de Bordeaux · DUT Informatique | 2017–2018 | 10 |
| Total | 104 | ||
| Year | Level | Students | Duration |
|---|---|---|---|
| 2019 | 2nd year Master in Software Engineering | 7 | 2 months |
| 2018 | 2nd year Bachelor in Biology | 1 | 2 months |
| 2018 | 2nd year Master in Software Engineering | 8 | 2 months |
| 2017 | 1st year Master in Bioinformatics | 4 | 2 months |
| 2016 | 1st year CS Engineering | 1 | 2 months |
| 2016 | 1st year Master in Bioinformatics | 4 | 2 months |
Plants (MDPI, Q1) — Special issue: "Bridging the Annotation Gap in Non-Model Plant Species". 2020–present. JCR category rank 58/234 (Q1) in Plant Sciences. Keywords: gene annotation, non-model organisms, gene network inference, machine learning.
| Conference | Role | Location | Year |
|---|---|---|---|
| useR! 2020 — The R User Conference | Sub-reviewer | Virtual | 2020 |
| 16th ISCB Student Council Symposium | Sub-reviewer | Virtual | 2020 |
| 15th ISCB Student Council Symposium | Sub-reviewer | Switzerland | 2019 |
| 3rd BR-SCS Network(ING): ISCB Brazilian Student Council Symposium | Sub-reviewer | Brazil | 2018 |
| 4th International Conference on Technologies and Innovation (CITI) | Reviewer | Ecuador | 2018 |
Universidad Internacional de La Rioja (UNIR) · 2023–2024
Université de Bordeaux / LaBRI · Ministerial funding (MESR) · 2016–2019
University of Murcia · 2014–2015
University of Murcia · 2009–2013