Thomas Pierrot
About Thomas Pierrot
Thomas Pierrot is a Senior Research Scientist at InstaDeep Ltd, where he has worked since 2022 in Cambridge, Massachusetts. He specializes in AI and biology research, leading teams in both France and the US, with a focus on Deep Reinforcement Learning and Bioinformatics.
Work at InstaDeep
Thomas Pierrot has been a Senior Research Scientist at InstaDeep Ltd since 2022, operating from the company's Boston office in Cambridge, Massachusetts. He focuses on AI and biology research, leading research teams in both France and the United States. His role involves the application of advanced machine learning techniques, particularly in the areas of Evolutionary methods, Deep Reinforcement Learning, Deep Learning, and Bioinformatics. Prior to his current position, he held various roles at InstaDeep, including Research Engineer and Research Scientist.
Education and Expertise
Thomas Pierrot has a strong educational background in engineering and computer science. He studied at Pierre and Marie Curie University, earning a Ph.D. in Computer Science from 2018 to 2021. He also holds a Master of Science in Aerospace Engineering from Imperial College London, completed in 2017, and a Master of Science in Data Science and Applied Mathematics from ISAE-SUPAERO, achieved in 2018. His foundational studies were completed at Classes Préparatoires du Lycée Kléber, where he focused on computer science from 2012 to 2014.
Background
Before transitioning to machine learning, Thomas Pierrot had a background in Computational Fluid Mechanics and Aerospace Design. He began his career in research as an intern at ISIR, where he focused on Deep Reinforcement Learning in 2018. He subsequently worked at InstaDeep in various capacities, including Research Engineer and Research Scientist, before becoming a Senior Research Scientist.
Research Contributions
Thomas Pierrot has contributed to several significant research papers in the field of AI and genomics. His work includes contributions to 'Exploring Genomic Language Models on Protein Downstream Tasks,' 'SegmentNT: annotating the genome at single-nucleotide resolution with DNA foundation models,' 'Advancing DNA Language Models: The Genomics Long-Range Benchmark,' and 'BioCLIP: Contrasting Sequence with Structure: Pre-training Graph Representations with Protein Language Models.' These publications reflect his expertise in applying machine learning techniques to biological data.