Celestial Mechanics
Orbital dynamics, stability, and resonances in binary and multi-body systems with an emphasis on model interpretability.
Graduate researcher at Tufts University exploring celestial mechanics and the dynamics of planetary systems through theory, simulation, and observation-driven methods.
Orbital dynamics, stability, and resonances in binary and multi-body systems with an emphasis on model interpretability.
End-to-end pipelines for eclipsing binaries; transit stacking and validation for small circumbinary planets.
Numerical methods, N-body integrations, and performance-minded Python for large photometric datasets.
I am a Tulane University alumna with a B.S. in Engineering Physics, a Certificate in Computational Engineering, and a minor in French. Since August 2024, I have been pursuing an M.S. in Physics at Tufts University with a concentration in Astrophysics. My work sits at the intersection of theory, computation, and observation-building tools that convert complex light curves into physical insight.
Outside of research, I’m classically trained in the visual arts and often paint as a counterbalance to technical work. I also study languages to broaden communication and perspective across disciplines.
Stanley algorithm designed for Kepler adapted into EB pipeline for TESS combining gap-aware preprocessing, robust detrending, BIC-gated harmonic removal, and a coarse-to-fine parallel BLS with iterative validation and BJD0 alignment. Models eclipses (Gaussian vs. tanh) and vets secondaries with e (eccentricity), ω (argument of periastron) estimates; validated by injection–recovery and runtime scaling.
Clip-on haptics for cardiac catheterization: glove prototype with vibration motors, custom amplification/driver circuitry, and clinical GUI (waveform view, diagnostics, Boolean/analog I/O, gain). Designed to intravascular standards-biocompatibility, cleanability, corrosion/fluid ingress protection, labeling/packaging-and verified with materials/response-time tests.
Tutorial + sampler with pluggable models and user-defined priors via a dictionary (e.g., log-normal amplitudes/noise; Gaussian frequency/phase/offset; bounded physical priors like eccentricity). Explains likelihoods/posteriors and visualizes convergence and sampler oscillation around the mean. Example application to spot modulation in CM Draconis.