About
From the universe
to the world of data.
I’m a data scientist with a background in computational astrophysics. Years spent studying some of the universe’s most complex systems taught me how to frame hard questions, build quantitative models, and find signal in uncertainty.
The through-line
Curiosity, computation, and clarity.
My career began in astrophysics, where I used large-scale numerical simulations to study plasma, cosmic rays, magnetic fields, and the evolution of galaxies. The work lived at the intersection of physics, mathematics, and high-performance computing.
Today, I apply that same research mindset as a data scientist: breaking ambiguous problems into testable pieces, extracting insight from complex datasets, and communicating results with precision.
Trajectory
A path across disciplines.
Professional chapter
Data science
Applying a research-trained approach to real-world data: analytical rigor, computation, visualization, and clear communication.
Postdoctoral research
JILA · University of Colorado Boulder
Research in computational plasma astrophysics, including the interaction of kinetic instabilities and particle acceleration.
PhD
University of California, Santa Barbara
Doctoral work in computational astrophysics and the dynamics of cosmic rays in galactic environments.
Undergraduate
University of Oxford
The start of a formal journey through physics, scientific computing, and the universe.
Perspective
What astrophysics brought to data science.
Comfort with complexity
Real systems are noisy, coupled, and incomplete. That is where careful framing matters most.
Evidence over intuition
Ideas become useful when they survive contact with data, diagnostics, and reproducible tests.
Clarity as a deliverable
An analysis is only finished when its assumptions, findings, and implications are understood.