Jun Wang
About Jun Wang
Jun Wang is a Postdoctoral Associate at Yale University, specializing in genomic data processing and machine learning. He holds a PhD in Genetics and Molecular Biology from Clemson University and has extensive experience in analyzing multi-omics next-generation sequencing data.
Work at Yale University
Jun Wang has been serving as a Postdoctoral Associate at Yale University since 2020. In this role, he engages in advanced research within the field of genetics and molecular biology. His position is based in New Haven County, Connecticut, where he contributes to various projects that leverage his expertise in genomic data processing and machine learning.
Education and Expertise
Jun Wang holds a Doctor of Philosophy (PhD) in Genetics and Molecular Biology from Clemson University, where he studied from 2015 to 2020. He also earned a Bachelor's degree in System Biology from the Technical University of Denmark in 2013. Additionally, he obtained a Bachelor of Engineering (BE) in Biotechnology from Beijing University of Technology in 2013. His educational background equips him with a solid foundation in bioinformatics and data analysis.
Background
Jun Wang's academic journey began at Beijing University of Technology, where he completed his Bachelor of Engineering in Biotechnology from 2009 to 2013. He then pursued further studies at the Technical University of Denmark, earning a Bachelor's degree in System Biology in 2013. Following this, he attended Clemson University, where he completed his PhD in Genetics and Molecular Biology from 2015 to 2020. His diverse educational experiences have shaped his research focus and technical skills.
Research Experience and Skills
During his time at Clemson University, Jun Wang worked as a Graduate Research and Teaching Assistant from 2015 to 2020. He gained practical experience in analyzing large-scale multi-omics next-generation sequencing (NGS) data. His skill set includes proficiency in R and Python, which he utilizes for genomic data processing and machine learning model evaluation. He applies both conventional machine learning models and advanced deep learning techniques for genomic data mining.