Guillaume Richard
About Guillaume Richard
Guillaume Richard is an AI Research Scientist known for his work in Quality-Diversity Optimization and contributions to the QDax library. He has held research positions at notable institutions and currently works at InstaDeep Ltd, focusing on machine learning and optimization algorithms.
Work at InstaDeep
Guillaume Richard has been employed at InstaDeep Ltd as an AI Research Scientist since 2021. His role involves conducting research and development in artificial intelligence, with a focus on optimization techniques and machine learning applications. He has contributed to various projects within the company, leveraging his expertise in Quality-Diversity Optimization and large-scale training methodologies.
Education and Expertise
Guillaume Richard holds a Doctor of Philosophy (PhD) in Machine Learning from École normale supérieure Paris-Saclay, where he studied from 2018 to 2021. He also completed a Master's degree in Data Sciences at École Polytechnique in 2017 and an Engineering degree in Computational and Applied Mathematics at ENSTA Paris from 2013 to 2017. His educational background provides a strong foundation in mathematics, physics, and advanced computational techniques.
Background
Guillaume Richard began his academic journey at Lycée Blaise Pascal in Orsay, where he completed Classes préparatoires in Mathematics and Physics from 2011 to 2013. He gained practical experience through internships, including a position as a Research Intern at Humboldt-Universität zu Berlin in 2015 and a Research Intern at EDF in 2017. He later worked as a PhD Candidate at EDF from 2018 to 2021, focusing on research in optimization.
Research Contributions
Guillaume Richard has made significant contributions to the field of artificial intelligence, particularly in Quality-Diversity Optimization. He is a main contributor and maintainer of an internal library for Transformers, designed for large-scale training on DNA data using Jax. Additionally, he contributed to the development of the DeepChain platform, implementing optimization and machine learning algorithms for protein design. His research includes co-authoring the paper 'SegmentNT: annotating the genome at single-nucleotide resolution with DNA foundation models.'
Previous Work Experience
Before his current role, Guillaume Richard worked as a Securitization Quantitative Analyst at Crédit Agricole CIB from 2015 to 2016, where he spent time in both Paris and New York. His experience at EDF as a Research Intern and later as a PhD Candidate further solidified his expertise in research and optimization techniques, contributing to his current work in AI.