Experience

The Jackson Laboratory

• United States
• Biotechnology Research
• 700 & Above Employee

• Full computational scientist

  • Mar 2023 - Present

  Genomics and computational sciences

• Associate computational scientist

  • Jan 2021 - Mar 2023



Frontiers

• Switzerland
• Research Services
• 700 & Above Employee

• Associate Editor

  • Nov 2022 - Present

  Single-cell bioinformatics editorial board: https://www.frontiersin.org/journals/bioinformatics/editors Single-cell bioinformatics editorial board: https://www.frontiersin.org/journals/bioinformatics/editors



UC San Diego School of Medicine

• United States
• Higher Education
• 200 - 300 Employee

• Research scientist (Bioinformatician)

  • Jun 2018 - Jan 2021



University of Hawai'i Cancer Center

• United States
• Biotechnology
• 1 - 100 Employee

• Faculty position (Junior Scientist)

  • Dec 2017 - Jun 2018

• Postdoc in Translational Informatics

  • Feb 2016 - Dec 2017

  http://www2.hawaii.edu/~lgarmire/



Mapado

• France
• Technology, Information and Internet
• 1 - 100 Employee

• Data Scientist

  • Nov 2014 - Dec 2015

  recommendation system, machine-learning (Classification, clustering), knowledge extraction and representation (graph of knowledge, keyword extraction) recommendation system, machine-learning (Classification, clustering), knowledge extraction and representation (graph of knowledge, keyword extraction)



Ecole centrale de Lyon

• France
• Research
• 100 - 200 Employee

• Ph.D.

  • Nov 2011 - Nov 2014

  Summary: Several types of genomics elements could be found on bacterial genomes. In particular, one could distinguish the stable and slowly evolving part: the chromosomes and the unstable and rapidly evolving elements: the plasmids. The question of my PhD project is to study the evolutionary links between those elements and to understand the genomic mechanisms responsible for the stabilization and integration of the plasmids into the stable genomes and thus create complex genomic… Show more Summary: Several types of genomics elements could be found on bacterial genomes. In particular, one could distinguish the stable and slowly evolving part: the chromosomes and the unstable and rapidly evolving elements: the plasmids. The question of my PhD project is to study the evolutionary links between those elements and to understand the genomic mechanisms responsible for the stabilization and integration of the plasmids into the stable genomes and thus create complex genomic structures. Using genomic data from public databases we build analyses to discover trends which could be potentially linked to evolutionary scenarios and to different molecular mechanisms.

• Teaching in informatics

  • Nov 2011 - Dec 2013

  Participation in the teaching modules of informatics for students of ECL (first year) and in the context of tutorial classes. Three submodules were held: Programming in C/C++ (programming object, complexity, UML, Qt and graphical application,...), Databases (relational algebra, SQL) and Web development (HTML, PHP, CSS, database interaction). Correction of homeworks, projects and semester exams. Duration: 192 hours

• MSc student co-supervision

  • Jan 2013 - Jun 2013

  The aim of the project was to compare the efficiency of different algorithms of alignment-free phylogeny reconstruction for sequences comparison. The internship was held in the context of a research internship for Master 2 students in the fields of statistics/bioinformatics.



The University of Queensland

• Higher Education
• 700 & Above Employee

• MSc reasearch internship

  • Apr 2011 - Oct 2011

  Traditional phylogenetic approaches are based on the computation of different evolutionary scenarios for a set of sequences in order to infer the most probables. Thus these methods are very computationally intensive and might not be achievable in practice. The aim of the project was to develop phylogenomic procedure able to analyze a huge amount of data in realistic time. The methodology consisted to skip evolutionary models and rather to focus on the structure of the sequences(particularly… Show more Traditional phylogenetic approaches are based on the computation of different evolutionary scenarios for a set of sequences in order to infer the most probables. Thus these methods are very computationally intensive and might not be achievable in practice. The aim of the project was to develop phylogenomic procedure able to analyze a huge amount of data in realistic time. The methodology consisted to skip evolutionary models and rather to focus on the structure of the sequences(particularly motifs: kmers) Show less Traditional phylogenetic approaches are based on the computation of different evolutionary scenarios for a set of sequences in order to infer the most probables. Thus these methods are very computationally intensive and might not be achievable in practice. The aim of the project was to develop phylogenomic procedure able to analyze a huge amount of data in realistic time. The methodology consisted to skip evolutionary models and rather to focus on the structure of the sequences(particularly… Show more Traditional phylogenetic approaches are based on the computation of different evolutionary scenarios for a set of sequences in order to infer the most probables. Thus these methods are very computationally intensive and might not be achievable in practice. The aim of the project was to develop phylogenomic procedure able to analyze a huge amount of data in realistic time. The methodology consisted to skip evolutionary models and rather to focus on the structure of the sequences(particularly motifs: kmers) Show less