Dr. Moonzarin Reza

Education

• Ph.D. – Astronomy, Texas A&M University – 2026
• M.Sc. – Astronomy, Texas A&M University – 2023
• B.Sc. – EEE, Bangladesh University of Engineering and Technology – 2019

Biography

Moonzarin Reza was born and raised in Bangladesh. She received her B.Sc. in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology. She completed her M.Sc. from Texas A&M University, where her Master's project focused on cluster cosmology using simulation-based inference. She received her PhD from Texas A&M University under the supervision of Prof. Lifan Wang. Her dissertation developed a data-driven, machine learning–based framework for Type Ia supernova classification and cosmology. Her broader research has also drawn on machine learning across a range of astrophysical problems, including galaxy morphology classification, photometric redshift estimation, and predicting dark matter properties from baryonic signatures.

Research Interests

Supernova Cosmology —Transient characterization— Galaxy Clusters — Machine Learning

Publications

• M. Reza, L. Wang, L. Hu, “An FPCA-Enhanced Ensemble Learning Framework for Photometric Identification of Type Ia Supernovae”, The Astrophysical Journal, 1003, 223 (2026) [DOI]
• M. Reza and L. Wang, “FPCA-Enhanced Simulation-Based Inference for Robust Type Ia Supernova Cosmology”, arXiv: 2510.09990 [DOI]
• M. Reza, “Predicting the Physical Properties of Dark Matter Subhalos from Baryonic Parameters using Machine Learning”, New Astronomy, Vol. 115, 102316 (2025) [DOI]
• M. Reza, Y. Zhang, C. Avaestruz, et al., “Constraining Cosmology with Simulation-based inference and Optical Galaxy Cluster Abundance”, arXiv:2409.20507 [DOI]
• M. Reza, “Galaxy morphology classification using automated machine learning”, Astronomy and Computing, Vol. 37, 100492 (2021) [DOI]
• M. Reza and M. A. Haque, “Photometric redshift estimation using ExtraTreesRegressor: Galaxies and quasars from low to very high redshifts”, Astrophysics and Space Science, Vol. 365, 50 (2020) [DOI]