The primary focus is superradiance in neutral atom ensembles — modeling collective decay in multi-atom systems and determining which approximations actually hold in physically realistic configurations, not just the textbook cases. An approximation that works in the single-excitation regime can break down completely in the many-body case; finding that boundary requires working through the problem analytically before trusting any numerics.

• Developing analytic solutions to the superradiance problem, including compressed representations that admit O(N³) solutions for tractable subsets of states — a precondition for validating the numerics rather than just running them.
• Designing the next phase of the simulation pipeline using tensor-network methods on GPU-capable libraries (PyTorch, JAX, Quimb) to reach atom-ensemble sizes that are intractable with exact methods.
• Transforming research prototypes into structured Python library code: building a package for Pauli String Hamiltonian summation and exponentiation within the ZXW Calculus framework.

• Developed and maintained hardware control tooling in Python using the ARTIQ quantum OS; designed and documented the internal API surface for the lab's experimental sequences.
• Built automated data pipelines for extracting key experimental parameters — trap depth, loading rate, single-atom fidelity — replacing manual workflows that had been the bottleneck for iteration speed.
• Automated polarization drift correction in single-mode fiber optics via a Python feedback loop reading analog photodiode signals through an NI DAQ — eliminating a daily manual alignment step that had been a persistent source of experimental downtime.
• Designed electromagnetic field simulations at 6.8 GHz in COMSOL; used pyLCP for hardware-aware validation of trap geometry configurations before committing to physical changes.
• Outcome: higher single-atom trap fidelity and longer experimental uptime — concrete measures that reflected how much the rebuilt system outperformed its predecessor.