About me

I’m a grad student in the Moler Group at Stanford University, working on scanning magnetic microscopy of quantum materials and devices using superconducting sensors.

My interests and experiences include scientific computing, quantum sensors and quantum computing, and experimental low temperature physics. In my free time, I enjoy baking and backpacking.

Backpacking in Sequoia National Park, July 2022.

Backpacking in Sequoia National Park, July 2022.

A major theme of my research career so far has been turning theory into efficient, intuitive code, and then into actionable insights about physical systems. I aim to identify the simplest model that contains the essential physics of a system, identify and implement a computational strategy to solve the model, and then iterate quickly, adding complexity as needed.

Examples of this pattern include:

  • SeQuencing, an open-source Python package for simulating realistic quantum control sequences, used for optimizing and benchmarking gates on bosonic qubits (Quantum Circuits, Inc., 2020 [repo]).
  • Time-dependent Ginzburg-Landau (TDGL) modeling of nonlinearities and vortex dynamics in measurements of the local magnetic response of 2D superconductors (Stanford University, ongoing).
  • SuperScreen, an open-source Python package that solves the London equation for thin film superconducting devices of arbitrary geometry (Stanford University, 2022 [paper and repo]).
  • Using large-scale nonlinear programming (NLP) to model the local magnetic properties of disordered Josephson junction arrays (Stanford University, 2022 [paper]).
  • GPU-accelerated micromagnetic simulations to understand nonidealities in spin transfer torque-driven ferromagnetic resonance (STFMR) measurements of spintronic devices (Cornell University Center for Materials Research, 2016 [report and slides]).