I am currently a graduate fellow in astrophysics at the University of California, Berkeley, Berkeley, where I work in the Transients, Extragalactic group (TREX) studying the most extreme explosions in the universe. This includes supernovae, gamma ray bursts (GRBs), tidal disruption events (TDEs), and other transient phenomena. My research focuses bridging theory and observation to model and understand these transients, their host environments, and their emission mechanisms. Before this, I held research roles at the University of Utah in both astrophysics and biomedical data science.

My present research focuses on theory, modeling, and observation of various classes of astrophysical transients. In particular, I am currently interested in some of the following topics:

• The nature of luminous fast blue optical transients (LFBOTs) and their progenitors.
• Methods for improving the study of transients in the radio band, particularly synchrotron modeling beyond the standard equipartition assumption.
• The role of binary interactions in shaping the circumstellar environments of massive stars and their subsequent explosive deaths.
• The use of machine learning and data-driven approaches to classify and analyze transient events in large-scale surveys.
• The source of FRB dispersion measures and their use in cosmological inference.

X-Ray Astrophysics

As an undergraduate (and externally as a graduate student), I have been a member of the X-Ray Astrophysics Group at the University of Utah, working under Dr. Daniel Wik. The group focuses on the X-ray emission of galaxy clusters and active galactic nuclei (AGN), and how these observations can be used to constrain various astrophysical processes. As part of my work in the group, I have

• worked with the EROSITA all-sky survey team to identify anomalous hard-band X-ray sources.
• Contributed to work on the cross-calibration of X-ray observatories, including Chandra, XMM-Newton, NuSTAR, and XRISM. My work explores the use of deep-learning and simulation-based (likelihood-free) inference to identify calibration systematics between these observatories.
• Worked on our understanding / interpretation of results from XRISM’s RESOLVE instrument, a high-resolution X-ray microcalorimeter. This instrument’s small field of view and high spectral resolution make it ideal for probing the microphysics of the intracluster medium, particularly in the context of turbulence; however, its small field of view also makes it challenging to interpret. I have worked on theoretical models of XRISM observations of galaxy clusters to help inform observing strategies and data analysis.

Data Science

My work in epidemiology has focused on modeling the dynamics of rare diseases and understanding the intersection between infectious diseases and autoimmune conditions. I leverage advanced statistical analysis, machine learning, and big data techniques to uncover insights from large-scale patient datasets.

As the lead data scientist in the Weller Lab at the University of Utah School of Dentistry, I managed patient datasets that were 10 to 100 times larger than those typically seen in the literature. This positioned our work at the forefront of rare disease research, enhancing both the quality and statistical rigor of our findings. The recent emergence of massive-scale electronic health record (EHR) datasets has provided a novel tool in precision epidemiology research which we have harnessed to understand the dynamics of even extremely rare disease phenotypes.

My role involved not only technical development but also cross-disciplinary collaboration. I have applied my computational skills to open new avenues of research within the lab, bridging gaps between disciplines and pushing the boundaries of what’s possible in understanding autoimmune disease mechanisms. This has led to impactful research that has been presented at both national and international conferences.

Publications