I am currently the Engineering Director for the Algolia Recommend product and I oversee our discovery initiatives. I joined Algolia because I wanted to address AI challenges at the root (inside the search or recommendation engines) while before that, I used to work on top of providers like Elasticsearch or Solr. The broad variety of customers and their use cases is what drive me most currently, as they are fantastic challenges to be solved with AI.
Past TL;DR
Lead Machine Learning Engineer at Doctrine, Paris, a blooming French Legaltech startup. I focused on search and recommendation to always propose users the most relevant documents. I loved tackling the challenges of dealing with Legalese and fulfilling user needs in such a specific domain.
Postdoctoral Visiting Fellow in Zhiyong Lu‘s Biomedical Text Mining group at the National Center for Biotechnology Information of the National Institutes of Health, Bethesda, USA. I was privileged to be able to work on PubMed and PubMed Labs search engines — the latter became the new PubMed since then. My work there focused on improving the systems by providing more accurate query answers through a better understanding of queries, documents and relevance. This work has impacted the experience of the millions of users PubMed welcomes every day.
Collaboration during my free time with Moreno Mitrović on topics related to computational linguistics.
PhD student in the LGI2P lab of IMT Mines Alès, France. I happened to work with the MAB team of the LIRMM at Montpellier, on phylogenetics and evolution. On these topics, I also worked with the Ensembl team of the European Bioinformatics Institute on genomic alignments.