Arjun Sharma has always been passionate about STEM and using his knowledge to address real-world challenges.
Arjun’s paper, titled “The First Use of Positive and Unlabeled Machine Learning to Identify Fast Radio Burst Repeater Candidates,” delves into utilising positive and unlabeled (PU) machine learning methods to distinguish potential fast radio burst (FRB) repeaters. With FRBs displaying repeating characteristics, this research breaks ground by employing PU-specific techniques on 536 FRBs, unveiling two new potential repeater candidates. These findings emphasise the physical differences between repeaters and non-repeaters, indicating exciting possibilities for future FRB investigations using PU learning methods.
We are delighted to announce that his research paper has been accepted at the prestigious MIT URTC 2023 research conference. This conference is a platform that celebrates the exceptional engineering and technological research achievements by undergraduate-level student researchers from across the country.
Arjun’s accomplishments are outstanding, and we extend our heartfelt congratulations to him on this remarkable achievement.
