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Lélia Polit

Computational Biologist at WhiteLab Genomics

Lélia Polit has a diverse work experience in the field of biomedical research. Lélia is currently employed at WhiteLab Genomics as a Computational Biologist since February 2023. Prior to this, they worked as a Postdoctoral Researcher at Sorbonne Université from February 2021 to January 2023, where their research focused on specific gene repression mechanisms. Before that, Lélia worked as a PhD Researcher at Institut Cochin from November 2017 to December 2020, where they conducted research on SPI1 gene repression regulation and also developed an R package for ChIP-seq normalization. Lélia also completed a Master Internship at Inserm from February 2017 to August 2017. Lélia's work experience also includes a student internship at CNRS in June 2015 and providing academic support as a tutor from September 2014 to June 2015.

Lélia Polit's education history includes a Doctorat degree in Structures et Dynamiques des Systèmes Vivants, obtained from Université Paris-Saclay between the years 2017 and 2020. Prior to that, Lélia completed a Master's degree in Informatique, Spécialité BioInformatique et Modélisation at Pierre and Marie Curie University from 2015 to 2017. Lélia also holds a Licence bi-disciplinaire in Informatique et Mathématiques Appliquées (PIMA) from the same university, earned between 2014 and 2015. Lélia's education journey began with a Classe préparatoire at Lycée Malherbe - Caen from 2011 to 2014, focusing on Agro-Véto.

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WhiteLab Genomics

WhiteLab Genomics develops a proprietary platform that leverages graph knowledge technology and machine learning to help companies discover and design new genomic therapies. WhiteLab Genomics enables genomic medicine scientists to obtain precise and comprehensive answers to key scientific and technological questions such as optimizing payload andvector design, assessing genotoxicity, and identifying optimal experimental protocols for in vivo and in vitro strategies. Whitelab Genomics guides its clients through the in-silico development phases, allowing them to quickly develop target vectors and payloads while saving valuable time needed to provide patients with access to new genomic therapies.


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