Rushat Aboti, a high school student at Dougherty Valley High School in California, has a strong academic focus on physics and artificial intelligence. He has earned recognition as the best delegate in MUN Debate.
Rushat was a Mulesoft Developer Intern during his journey, utilising Mulesoft Anypoint Studio, Postman, and MySQL to craft a customisable database. He also contributed to building a mobile app using Flutter and Dart. Additionally, he gained experience as a Media Intern at Sakshi TV USA.
Rushat has shared his knowledge by tutoring high school and middle school students in Math and Physics. His guidance has helped students succeed in competitive talent tests, particularly middle school olympiads. He is well-versed in international curriculum and has extended support to students from underrepresented backgrounds in India, bridging gaps in educational resources.
Within the CCIR Academy, Rushat pursued a research project titled “Enhancing Photometric Redshift Predictions and Uncertainty Quantification using Deep Learning Methods.” This research paper aims to enhance the accuracy of predicting the redshift of astronomical objects based on their photometric properties. Accurate redshift estimation is vital for understanding celestial objects’ distances and characteristics. To achieve this, the paper explores applying deep learning techniques, a subset of machine learning, to improve redshift predictions. Furthermore, the research delves into developing methods to quantify the uncertainty associated with these predictions, offering a more comprehensive assessment of result reliability.
Recently, Rushat had the opportunity to share his invaluable insights and reflections on his research journey in an episode of the CCIR Academy Student Spotlights.
