Astronomy Ph.D. Candidate at Harvard University.
My research focuses on early universe cosmology. I study different models of cosmic inflation, analyzing the signals that we expect them to produce and how we can detect these signals with future CMB experiments.
I received my Bachelor of Science (B.S.) from Villanova University, where I majored in Astrophysics and minored in Physics, Mathematics, and Classical Studies. During my time at Villanova, I researched the orbital patterns of black-hole X-ray binaries. I received internships at MIT Haystack Observatory, where I was involved in multiple projects studying the configurations of upcoming VLBI arrays, namely, an early version of the Black Hole Explorer (BHEX) and the Next Generation Very Large Array (ngVLA). I began my Ph.D. in 2022 at Harvard University, where my research focus has shifted to inflation and early universe cosmology.
Inflation is a scenario in which the universe underwent a rapid exponential expansion ~10-34 seconds after the Big Bang. Our most direct observations of inflation come from the Cosmic Microwave Background (CMB) temperature and polarization anisotropies. However, these measurements do not offer enough precision to probe inflation directly. Furthermore, there are many different proposed models of inflation that are consistent with observations, and without more precise measurements, we cannot distinguish between these models. However, there are a number of CMB experiments planned for the next decade, such as SPT, CMB Stage-4, Simons Observatory, and LiteBIRD, that will aim to measure the CMB with uprecedented precision.
My work is at the itnersection of theory and observations. My research involves fundamental physics of inflationary field theories to determine their expected observational signals. In addition, I analyze current CMB data and simulating measurements from upcoming missions to see if we will be able to detect or distinguish between different inflationary models, and other beyond-ΛCDM cosmological models, in the coming decades. (Image credit: Big Think / Ben Gibson)
