Zan Koenig
About Zan Koenig
Zan Koenig is an Associate Computational Biologist at the Broad Institute of MIT and Harvard, where he has worked since 2019. He holds a Bachelor of Science in Bioinformatics and Molecular Biology from Rensselaer Polytechnic Institute, where he also served as an Admissions Ambassador and Undergraduate Research Assistant.
Work at Broad Institute
Zan Koenig has been employed at the Broad Institute of MIT and Harvard as an Associate Computational Biologist since 2019. In this role, Koenig focuses on computational analyses to enhance data interpretation in various research projects. The Broad Institute is known for its collaborative approach to biomedical research, and Koenig's contributions support this mission by integrating technology with biological data.
Education and Expertise
Zan Koenig earned a Bachelor of Science degree in Bioinformatics and Molecular Biology, with a minor in Ecology, from Rensselaer Polytechnic Institute. This program spanned from 2015 to 2019, providing a solid foundation in both biological sciences and computational techniques. Koenig's educational background supports a strong understanding of the intersection between biology and computer science.
Background in Research and Admissions
Prior to joining the Broad Institute, Zan Koenig worked at Rensselaer Polytechnic Institute as an Undergraduate Research Assistant and Admissions Ambassador from 2017 to 2019. As an Undergraduate Research Assistant, Koenig implemented computational analyses to enhance data interpretation during research projects. In the role of Admissions Ambassador, Koenig engaged with prospective students, sharing insights about the academic environment.
Passion for Technology and Biology
Zan Koenig has a strong passion for integrating biology and computer science. This interest drives Koenig's work in computational biology, where technology is leveraged to improve data interaction and interpretation in biological research. This commitment to merging these fields reflects a broader trend in scientific research that emphasizes the importance of computational methods in understanding complex biological systems.