Greetings, everyone. I am Adrita Khan. I am a graduate of the University of Science, Malaysia (USM) with a major in Physics and a minor in Astronomy. My research interests lie in theoretical and observational cosmology, including large-scale structure, dark matter, dark energy, high-energy astroparticle physics, and machine learning applications in astrophysics.
My research focuses on modified gravity theories through cross-correlation analyses of the Cosmic Microwave Background (CMB) and galaxy data. I develop machine learning and deep learning methods for radio astronomy, including automated classification of bent radio AGN and semi-supervised learning approaches. I am currently working on gamma-ray burst redshift estimation and on developing the Explainable Particle Chebyshev Network (EPCN), a neural network architecture that incorporates interpretable interaction features into Chebyshev-based frameworks.
My research goal is to advance precision cosmology at the intersection of theory and observation, particularly using data from next-generation surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC), the Euclid mission, the Dark Energy Spectroscopic Instrument (DESI), the Simons Observatory, CMB-S4, and the Atacama Cosmology Telescope (ACT). I use computational approaches including machine learning, Bayesian inference, and cosmological emulation to extract constraints on the universe's origin, evolution, and matter–energy composition. I am committed to fostering diversity, equity, and inclusion within the academic community.
