I am currently a graduate fellow in astrophysics at the University of California, Berkeley, Berkeley, where my work centers on the physics of large-scale cosmic structures and the plasma processes that shape them. Before this, I held research roles at the University of Utah in both astrophysics and biomedical data science.

My present research focuses on using simulations, theory, and observational analysis to probe the interplay between gravity, dark matter, and baryonic physics in cosmic environments. In particular, I:

• Model the thermodynamics and dynamics of galaxy clusters to constrain fundamental physics and understand structure formation.
• Investigate plasma microphysics — turbulence, shocks, conduction, and viscosity — in the intracluster and intergalactic medium, and their observational consequences.
• Develop and apply numerical methods for non-Cartesian geometries and high-performance simulation analysis.
• Explore interdisciplinary applications of astrophysical modeling techniques to large-scale datasets in other domains.

Astrophysical Modeling and Theory

My primary research focuses on astrophysical dynamics, modeling, and simulation. A few of the things that I have worked on include:

• the modeling of equilibrated galaxy clusters with various, non-ideal properties including anisotropic velocity dispersions, non-thermal pressure support, and non-spherical geometries. By developing methods to study these more realistic systems, we can better understand the biases that arise when using galaxy clusters as cosmological probes.
• Galaxy cluster models in non-Newtonian gravitational paradigms, including Modified Newtonian Dynamics (MOND) and emergent gravity. These models help us understand how galaxy clusters can be used to test alternative theories of gravity, and what the implications are for our understanding of dark matter. This work resulted in constraints for extensions of MOND using galaxy clusters (Diggins+2025)
• The development (in collaboration with John ZuHone) of the cluster generator code, a Python package for generating equilibrium models of galaxy clusters with various properties. Additionally, the need for a universal toolkit for astrophysical modeling has motivated my development of the Pisces Project, a modular framework for building and initializing astrophysical systems in arbitrary geometries and with a variety of physical properties. Pisces is designed to be extensible and user-friendly, with the goal of making astrophysical modeling more accessible to a broader community.

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