Research
Lab work in Professor Daniel Scolnic's research group.
Overview
My research focuses on computational tools for space situational awareness and orbital mechanics. I built Python pipelines to visualize and analyze ~40,000 satellites and space debris objects in low Earth orbit (LEO), using NORAD TLE data and the SGP4 propagation model. The work emphasizes modeling rigor, simulation scale, and reproducible workflows.
Modeling & Simulation
I implemented Monte Carlo simulations to model and predict collision risk in LEO. The framework samples over orbital uncertainty distributions, propagates trajectories, and estimates collision probabilities with confidence intervals. This supports conjunction assessment and mission planning.
Optimization
I developed QUBO (Quadratic Unconstrained Binary Optimization) frameworks for debris removal mission planning. The formulation selects removal targets and sequencing to minimize total delta-V, with support for D-Wave and classical annealers.
Publications & Posters
Papers and posters will be linked here when available. Placeholder: [Paper placeholder], [Poster placeholder]