Greeshma Koyithatta Meethaleveedu, Ph.D. Physics
About Greeshma Koyithatta Meethaleveedu, Ph.D. Physics
Greeshma Koyithatta Meethaleveedu, Ph.D. in Physics, is a Machine Learning Engineer at Hinge Health in Bengaluru, India, with extensive experience in data science and machine learning across various prestigious institutions.
Work at Hinge Health
Greeshma Koyithatta Meethaleveedu has been employed as a Machine Learning Engineer at Hinge Health since 2023. Based in Bengaluru, Karnataka, India, she works in a hybrid capacity. In this role, she focuses on applying machine learning techniques to solve complex problems, contributing to the company's mission of improving healthcare outcomes through technology.
Previous Experience in Machine Learning
Before joining Hinge Health, Greeshma worked as a Senior Machine Learning Engineer at VerSe Innovation from 2021 to 2023. Her career also includes a position as a Data Scientist - AI Fellow at Feynman.ai for five months in 2021. These roles allowed her to develop her expertise in machine learning applications and data analysis.
Academic Background and Research
Greeshma holds a Doctor of Philosophy (PhD) in Experimental High Energy Physics from the Indian Institute of Technology, Bombay, where she studied from 2010 to 2017. She also earned a Master's degree in Theoretical and Mathematical Physics from the same institution between 2008 and 2010. Her academic journey began with a Bachelor's degree in Physics from Kannur University, completed from 2005 to 2008.
Research Contributions at CERN
Greeshma has significant research experience, having worked at CERN as a Visiting Researcher in Data Science/Analysis for a total of 11 months across two separate periods in 2010 and from 2010 to 2017. During her time at CERN, she contributed to data science projects in the field of high energy physics, leveraging her skills in data analysis to derive new insights.
Skills and Expertise
Greeshma possesses expertise in various areas including Natural Language Processing, Statistical Data Modeling, and Predictive Analytics. Her strong background in both theoretical and experimental physics enhances her ability to apply machine learning techniques effectively in data science applications.