Richa Gupta, Ph.D.
About Richa Gupta, Ph.D.
Richa Gupta, Ph.D., is a Principal Scientist and Manager of Translational Informatics at DNAnexus in Mountain View, California, where she has worked since 2021. She holds a Master of Science from King's College London and a Doctor of Philosophy from the University of Helsinki.
Work at DNAnexus
Richa Gupta has been serving as Principal Scientist and Manager of Translational Informatics at DNAnexus since 2021. In this role, she focuses on advancing the integration of informatics in translational research, contributing to the development of innovative solutions in the field of genomics and data analysis. Her work supports the company's mission to accelerate biomedical research and improve patient outcomes through data-driven insights.
Education and Expertise
Richa Gupta holds a Doctor of Philosophy (PhD) from the University of Helsinki, where she specialized in areas relevant to her current work. She also earned a Master of Science degree from King's College London, University of London. Her educational background provides a strong foundation in both theoretical and practical aspects of translational informatics, equipping her with the necessary skills to lead research initiatives.
Background
Richa Gupta has a robust academic and professional background in informatics and translational research. Her experience spans several years in the field, where she has developed expertise in managing complex data sets and applying computational methods to biological research. Her current position at DNAnexus allows her to leverage her knowledge to facilitate advancements in genomics.
Achievements
Since joining DNAnexus, Richa Gupta has contributed to various projects aimed at enhancing translational research capabilities. Her role as Principal Scientist and Manager involves overseeing research initiatives that utilize informatics to bridge the gap between laboratory findings and clinical applications. Specific achievements during her tenure include the development of methodologies that improve data integration and analysis in translational studies.