Physics Ph.D. candidate studying fluctuating and evolving systems—Markov chains, Langevin dynamics, population and neural dynamics—with analytical theory, numerical computation and experimental data.
I am in the physics Ph.D. program at the University of Virginia, advised by Prof. Marija Vucelja (advanced to candidacy; expected 2028). My main interest is stochastic dynamics and the numerical methods that make it tractable, most recently the Mpemba effect: a system that starts farther from equilibrium can relax faster than one that starts closer. I also like working across fields: recently population dynamics and EEG neuroscience, and earlier cosmic-ray data analysis, supermultiplet representation spaces, discrete-spacetime geometry and wet-lab research identifying and cloning a gene for transgenerational silencing. Physics training lets me get up to speed in each quickly, and I am drawn to fields that are still changing fast.
I am lead author of a study of irreversible (lifted) Markov chains—Glauber dynamics of the 1D Ising model under skew detailed balance—and of when breaking detailed balance induces a Mpemba-like relaxation speedup. I mapped the parameter space with Monte Carlo simulation and spectral analysis of the generator in Python, explained the regimes analytically, and explored MCMC acceleration. To be presented at the 2027 APS Global Physics Summit.
I model alpha-band EEG as active particles driven by colored noise to capture signatures of criticality, and estimate the distance from criticality in experimental recordings. The work uses signal processing, regression and highly comparative time-series analysis (hctsa) in MATLAB, with neural field theory for model building across physics and neuroscience.
I solved the Klein–Kramers operator for a Brownian particle in a potential by discretization, matrix continued fractions and sparse eigensolvers in Mathematica on UVA’s Rivanna cluster. The resulting paper, which I co-authored, shows that the overdamped Mpemba effect persists at finite damping. It is submitted to J. Phys. A: Math. Theor.; the preprint is arXiv:2607.11797.
I applied analytical mechanics to predator–prey dynamics. The work was a contributed talk at the 2026 APS Global Physics Summit.
I derived channel gains to convert raw ISS-CREAM calorimeter ADC signals into energy for the cosmic-ray spectrum, with analysis in Python (pandas) on remote Linux servers over SSH. I am a co-author on the ICRC 2021 proceedings.
APS Global Physics Summit 2027 upcoming
Lifting a Markov chain into the Mpemba effect (contributed talk; working title).
APS Global Physics Summit 2026
Lagrangian formulation of Lotka–Volterra dynamics (contributed talk).