Kiran Garimella
About Kiran Garimella
Kiran Garimella is a Senior Computational Scientist I at the Broad Institute of MIT and Harvard, where he has worked since 2019. He holds a DPhil in Genomic Medicine and Statistics from the University of Oxford and has extensive experience in developing computational methods for biotechnology applications.
Work at Broad Institute
Kiran Garimella has been serving as a Senior Computational Scientist I at the Broad Institute of MIT and Harvard since 2019. In this role, he focuses on developing computational methods for third-generation sequencing technologies, including PacBio and Nanopore. His prior experience at the Broad Institute includes a position as a Computational Biologist from 2007 to 2011, where he contributed to various projects in computational biology.
Education and Expertise
Kiran Garimella earned a DPhil in Genomic Medicine and Statistics from the University of Oxford, where he studied from 2011 to 2016. He also holds a Master of Science in Physics from Clemson University, completed between 2004 and 2006, and a Bachelor's Degree in Physics from the University of Massachusetts, Amherst, obtained from 2000 to 2004. His expertise includes Java programming, which he applies in developing computational tools for biotechnology applications, as well as strong skills in data visualization for interpreting complex sequencing data.
Previous Experience
Before his current role, Kiran Garimella worked at the Wellcome Trust Centre for Human Genetics at the University of Oxford as a DPhil Student from 2011 to 2016. He also held a position as a Senior Research Technologist at The MGH/HST Martinos Center for Biomedical Imaging from 2006 to 2007. Additionally, he briefly worked as a Consultant at Eli Lilly and Company in 2011 and was an MS Student at Clemson University from 2004 to 2006.
Research Focus
Kiran Garimella specializes in developing novel computational methods tailored for third-generation sequencing technologies. His work is significant in the field of genomics, particularly in enhancing the capabilities of sequencing technologies like PacBio and Nanopore. His strong background in data visualization complements his research, allowing for effective interpretation of complex sequencing data.