Ramansh Sharma is a PhD student and researcher in physics-informed machine learning with nine years of practical experience applying ML to scientific problems across academia and national labs. Currently a summer intern at Los Alamos National Laboratory and affiliated with the Kahlert School of Computing at the University of Utah, he develops novel methods for solving partial differential equations and has collaborated with RIKEN and domain teams in fluid dynamics and astrodynamics. His background spans end-to-end ML systems, from research-grade physics-aware models to production tooling—building reproducible NLP pipelines, automated experiment tracking with Weights & Biases, and deployment workflows for community-facing apps. He combines strong theoretical training (BTech with a 9.74 GPA and rigorous math/CS roots at Bronx Science) with hands-on contributions to climate and environmental projects that emphasize real-world impact. Notably, he has led efforts on reproducibility in policy-instrument NLP and integrated scientific ML into mission-focused research like Arcanum and Astraeus.
9 years of coding experience
1 year of employment as a software developer
Bachelor of Technology - BTech, Computer Science, CGPA: 9.74, Bachelor of Technology - BTech, Computer Science, CGPA: 9.74 at SRM University
The University of Utah
Mathematics and Computer Science, GPA: 3.8, Mathematics and Computer Science, GPA: 3.8 at The Bronx High School of Science
High School Diploma, Physics, Chemistry, Mathematics, 32/45, High School Diploma, Physics, Chemistry, Mathematics, 32/45 at Eastern Public School
Contributions:81 commits, 68 PRs, 91 pushes in 6 months
my-blog
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.
Request Free Trial
Ramansh Sharma - Summer Intern at Los Alamos National Laboratory