PhD Candidate in Astrophysics
Department of Physics and Astronomy, Tufts University
I am a fourth-year PhD candidate at Tufts University working with Prof. Anna Sajina. My research focuses on galaxy evolution and applying machine learning to analyze large astronomical datasets.
In my spare time I enjoy exploring new places, playing the piano, immersing myself in nature, and engaging in improv.
I am passionate about studying how galaxy properties change over cosmic time. In my PhD research, I am exploring the use of an unsupervised machine learning technique, Self-Organizing Maps (SOMs), to derive redshift and galaxy parameters from photometry for large data surveys. The ultimate goal is to apply this method in synergy with state-of-the-art model fitting to provide stellar population properties for millions of galaxies within the joint HSC-Deep survey in an efficient way. In this context, I investigate how the estimation from SOM is influenced when considering more realistic cases, such as photometric errors and non-detection (missing data and upper limits).
I am conducting comparisons with other more commonly used machine learning techniques, such as random forest, extreme gradient boosting, fully connected neural networks.
Stay tuned for more science based on the derived properties for HSC-Deep survey using SOM.