I’m a postdoctoral researcher at Stanford working on machine learning for computational imaging and inverse problems in electron microscopy, including 4DSTEM, ptychography, tomography, and Lorentz TEM.
My main interestas the moment include self-supervised learning, deep priors, physics-informed reconstruction, and open-source scientific software.

• ML for inverse problems in (S)TEM: reconstruction, phase retrieval, robust and quantitative analysis on real-world data
• Computational imaging: ptychography, 4DSTEM workflows
• Scientific Python + PyTorch; reproducible research software and tooling
• Magnetic materials / spin textures / vdW ferromagnets

Repo and tutorials

• Core developer for ML modules in a growing EM analysis toolkit
• Developed ML-enabled iterative ptychography methods and code
• Contributed to core infrastructure (data structures, visualization, utilities, configuration backend)
• Wrote tutorials and examples to help new users adopt the toolkit

Repo

• Sole author and maintainer of a widely adopted Python codebase for Lorentz TEM (LTEM) simulation and analysis
• Used by the LTEM community for simulation-assisted interpretation and quantitative analysis of magnetic textures

• Deep generative priors for robust and efficient electron ptychography (2025)
• Accelerating iterative ptychography with an integrated neural network (Journal of Microscopy, 2025)
• AI-enabled Lorentz microscopy for quantitative imaging of nanoscale magnetic spin textures (npj Comput. Mater., 2024)
• Understanding Complex Magnetic Spin Textures with Simulation-Assisted Lorentz Transmission Electron Microscopy (Phys. Rev. Appl., 2022)
  Full list at Google Scholar

• amccray@stanford.edu
• LinkedIn