Gaurav Sharma
About Gaurav Sharma
Gaurav Sharma is a Computational Biologist at Ocean Genomics, Inc. with a background in Biomedical Engineering and Mechanical Engineering.
Work at Ocean Genomics, Inc.
Gaurav Sharma currently holds the position of Computational Biologist at Ocean Genomics, Inc., a role he has been in since 2020. His work involves utilizing computational methods to analyze biological data, contributing to the company's research initiatives. He previously served as a Senior Computational Biologist at the same organization, indicating a progression in his career within the company.
Education and Expertise
Gaurav Sharma earned a Master's degree in Biomedical/Medical Engineering from The Johns Hopkins University, where he studied from 2018 to 2020. Prior to this, he completed a Bachelor of Technology (B.Tech.) in Mechanical Engineering at the Indian Institute of Technology Gandhinagar from 2012 to 2016. His educational background provides a strong foundation in both engineering principles and biological sciences.
Background
Gaurav Sharma has a diverse background in computational biology and research. He began his career as a Core Committee Member at Amalthea, IIT Gandhinagar, and later worked as a Research Assistant at the Indian Institute of Technology Gandhinagar from 2016 to 2018. He gained further experience as a Graduate Research Student at The Johns Hopkins University from 2018 to 2020, followed by a role as a Course Assistant in 2019.
Achievements
Gaurav Sharma has co-authored 10 publications in reputed journals and conferences, which have collectively received over 100 citations. His research contributions reflect his expertise in bioinformatics and computational biology. He has developed bioinformatics software and created analysis pipelines that deliver biological insights, showcasing his technical skills in the field.
Technical Skills and Research Focus
Gaurav Sharma has experience working with various data modalities, including genomics, transcriptomics, single-cell transcriptomics, and proteomics. He is proficient in multiple programming languages and tools such as R, Python, C++, Snakemake, and Go. His recent research focus includes the application of deep learning models to enhance label prediction in clinical data, indicating his commitment to advancing methodologies in computational biology.