From exoplanet enthusiast to creator of a novel habitability framework
Ruthwik expanded his initial idea from analyzing a single planet into building an entirely new habitability index. METHI uses binary classification, unsupervised clustering and ensemble-based regression to improve on fixed-heuristic indices like ESI, PHI and SEPHI, achieving a 0.903 score and identifying the top 10 habitable exoplanet candidates. He also built a public web interface so anyone can input a planet name and retrieve a real-time habitability score.
Physics & Space Science
First-author IEEE publication
IEEE CONMEDIA 2025, 2025
Ruthwik Dhama, YRI Fellow
An 11th grader with a strong physics foundation who wanted to analyze the habitability of one exoplanet, K2-18b.
Created METHI, a novel machine learning framework that scores all exoplanets, published in IEEE.
First-author publication at the 2025 8th International Conference on New Media Studies
Built METHI, achieving a 0.903 score and ranking the top 10 habitable exoplanet candidates
Released a public web interface returning real-time habitability scores
Prior work discovering a novel periodicity for a supergiant star with a UNC-Greensboro professor
METHI: An Ensemble-based Machine Learned Exoplanetary Habitability IndexAarav Brahmbhatt, Published in IEEE Xplore