Harish Rithish Sethuram
About Harish Rithish Sethuram
Harish Rithish Sethuram is a Computational Associate II at the Broad Institute of MIT and Harvard, with a background in computer science and engineering. He has held various roles in research and software development across institutions in India and the United States, focusing on computational biology and computer vision.
Work at Broad Institute
Harish Rithish Sethuram currently serves as a Computational Associate II at the Broad Institute of MIT and Harvard. He began this role in 2023 and has been contributing to various computational biology projects. His work involves applying his expertise in computational methods to support research initiatives at the institute.
Previous Experience in Computational Biology
Before joining the Broad Institute, Sethuram worked as a Computational Biologist at UC San Diego from 2022 to 2023. In this position, he was involved in significant research efforts, including the engineering of a high-throughput pipeline capable of processing over 100 million RNA-seq reads, which enhanced the efficiency of genomic data analysis.
Internships in Software Development
Sethuram has gained valuable experience through various internships in software development. He worked as a Software Development Intern at NestAway Technologies Pvt Ltd in 2017 and at Zippr Private Limited in 2015. Additionally, he interned at WeCP (We Create Problems) from 2017 to 2018 and briefly at Amazon as a Software Development Engineer in 2018.
Educational Background in Computer Science
Sethuram completed his Master's degree in Computer Science at UC San Diego from 2021 to 2023. Prior to that, he earned his Bachelor's degree in Computer Science & Engineering from the National Institute of Technology, Tiruchirappalli, from 2014 to 2018. His academic background has provided him with a strong foundation in computational methods and software development.
Research Contributions
During his time in research roles, Sethuram developed a transcription-informed algorithm aimed at optimizing probe-sets for Hybridization Capture. This work focused on enhancing cost-efficiency in RNA molecule targeting, contributing to advancements in genomic research methodologies.