Lauren Alex Golden
About Lauren Alex Golden
Lauren Alex Golden is a Computational Scientist II at the Broad Institute of MIT and Harvard, specializing in data science, machine learning, and quantitative analysis. She has an extensive academic and professional background, including a PhD in Applied Physics from the University of Michigan and previous roles at institutions such as Boston University and Wayfair.
Work at Broad Institute
Lauren Alex Golden has been employed at the Broad Institute of MIT and Harvard as a Computational Scientist II since April 2023. In this role, she focuses on applying her expertise in data science and machine learning to support various research initiatives. The Broad Institute is known for its collaborative approach to biomedical research, and Golden contributes to this environment through her skills in quantitative analysis and mathematical modeling.
Education and Expertise
Lauren Alex Golden holds a Bachelor of Science in Mathematics and a Bachelor of Science in Electrical Engineering from the University of Maryland, where she studied from 2007 to 2012. She furthered her education at the University of Michigan, earning a Doctor of Philosophy in Applied Physics between 2012 and 2018. Her academic background provides a strong foundation for her specialization in data science, machine learning, and quantitative analysis.
Professional Background
Before joining the Broad Institute, Lauren Alex Golden held several positions in academia and industry. She was a Graduate Student at the University of Michigan from 2012 to 2018, followed by a role as a Postdoctoral Fellow at Boston University from 2018 to 2022. She also worked as a Manager of Data Science at Wayfair for one year, from 2022 to 2023. Earlier in her career, she gained experience as a SURF Student at the National Institute of Standards and Technology in 2011 and as an Engineering Technician at the U.S. Naval Research Laboratory from 2007 to 2011.
Research Interests
Lauren Alex Golden has a strong interest in applying mathematical modeling and analysis to address real-world problems. Her research focuses on utilizing data science and machine learning techniques to enhance understanding and solutions in various fields. This interest aligns with her educational background and professional experiences, allowing her to contribute effectively to research projects at the Broad Institute.