Alec Barrett

Graduate Student Researcher @ Yale University

About Alec Barrett

Alec Barrett is a Graduate Student Researcher at Yale University, focusing on neuronal diversity through RNA sequencing. He has a background in research from institutions such as Lewis & Clark College and the University of California, Santa Cruz.

Current Role at Yale University

Alec Barrett has been working as a Graduate Student Researcher at Yale University since 2017. In this role, he conducts research in Marc Hammarlund's lab, focusing on neuronal diversity through individual neuron RNA sequencing. His work contributes to the understanding of genetic variations and their implications in neuroscience.

Previous Experience at Lewis & Clark College

Before joining Yale University, Alec Barrett held multiple positions at Lewis & Clark College. He served as an Undergraduate Researcher from 2011 to 2014, a Community Education & Leadership Research Mentor for two months in 2014, and a Research Technician for ten months in 2014 to 2015. These roles provided him with foundational research experience in a collaborative academic environment.

Experience at University of California, Santa Cruz

Alec Barrett worked as a Research Technician at the University of California, Santa Cruz from 2015 to 2017. During this two-year period, he gained additional research skills and experience, further enhancing his expertise in the field of biochemistry and molecular biology.

Educational Background in Biochemistry and Molecular Biology

Alec Barrett earned his Bachelor of Arts in Biochemistry and Molecular Biology from Lewis & Clark College, completing his studies from 2010 to 2014. He later pursued a Ph.D. in Genetics at Yale University, where he studied from 2017 to 2023. His educational background has equipped him with a strong foundation in genetic research methodologies.

Research Focus and Methodologies

In his current research, Alec Barrett utilizes computational approaches to integrate bulk and single-cell RNA sequencing data. This methodology aims to enhance gene detection accuracy while minimizing false positives, contributing to the advancement of genetic research and its applications in neuroscience.

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