Emma Pierce Hoffman
About Emma Pierce Hoffman
Emma Pierce Hoffman is a Senior Computational Associate at the Broad Institute of MIT and Harvard, where she has worked since 2022. She specializes in computational biology methods and contributes to structural variant methods for whole genome sequencing.
Work at Broad Institute
Emma Pierce Hoffman has been serving as a Senior Computational Associate at the Broad Institute of MIT and Harvard since 2022. In this role, she contributes to the development of structural variant methods for whole genome sequencing, focusing on large-scale datasets such as gnomAD and All of Us. Previously, she worked at the Broad Institute as a Computational Associate II from 2020 to 2022 and as a Research Student in Computational Biology from 2015 to 2017. Her work is integrated within the Broad Data Sciences Platform and the Talkowski Lab at Massachusetts General Hospital.
Education and Expertise
Emma Pierce Hoffman earned her Bachelor of Science degree from Yale University, where she studied from 2016 to 2020. Her academic background has equipped her with a strong foundation in computational biology methods and genomic data analysis. This expertise is reflected in her contributions to various projects at the Broad Institute, particularly in the realm of structural variant analysis.
Previous Work Experience
Prior to her current position, Emma gained diverse experience in various roles. She worked as an Undergraduate Learning Assistant in Computer Science at Yale University from 2019 to 2020. Additionally, she completed a Full-Stack Developer Internship at IBM in 2019. Emma also interned at Wave Life Sciences as a Process Development Intern in 2018. These roles have provided her with a broad skill set applicable to her current work in computational biology.
Research Contributions
Emma Pierce Hoffman is involved in significant research contributions within the field of computational biology. Her work focuses on the development of methods for analyzing structural variants in genomic data. She plays a key role in projects that utilize large-scale datasets, contributing to advancements in understanding genomic variations and their implications for health and disease.