Zijiang (River) Yang

Zijiang (River) Yang

Associate Computational Biologist Ii @ Broad

About Zijiang (River) Yang

Zijiang Yang is an Associate Computational Biologist II at the Broad Institute of MIT and Harvard, specializing in data analysis and behavioral studies of macaques. He holds a B.S. in Mathematics, Computer Science, and Economics, as well as an M.S. in Biostatistics, both from the University of Michigan.

Current Role at Broad Institute

Zijiang Yang currently serves as an Associate Computational Biologist II at the Broad Institute of MIT and Harvard. In this role, he focuses on developing analytical pipelines for biological data. His work includes managing large datasets and optimizing data storage solutions. He has been with the Broad Institute since 2020.

Previous Experience at University of Michigan

Zijiang Yang has held multiple positions at the University of Michigan. He worked as a Graduate Student Instructor for three months in 2016 and as a Lab Assistant for six months from 2015 to 2016. His responsibilities included assisting in laboratory settings and supporting educational activities.

Data Analyst Role at SVIEF

From 2017 to 2020, Zijiang Yang worked as a Data Analyst at SVIEF in Santa Clara, California. During his tenure, he contributed to various data analysis projects, leveraging his skills in mathematics and computer science to derive insights from complex datasets.

Educational Background

Zijiang Yang earned a Bachelor of Science (B.S.) in Mathematics, Computer Science, and Economics from the University of Michigan, completing his studies from 2010 to 2014. He later pursued a Master of Science (M.S.) in Biostatistics at the same institution, graduating in 2017.

Technical Contributions and Achievements

Zijiang Yang has made significant technical contributions in his field. He developed a pipeline for analyzing macaque social behaviors and created a method for efficient data management, handling over 20 TB of video data. He has authored extensive Python code for data preprocessing and performed complex analyses on single-cell RNA-seq data.

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