Faustin Carter is a physicist with a decade of experience designing and instrumenting superconducting detectors and microwave electronics for cosmic microwave background experiments and advanced laboratories. Currently at HRL Laboratories after a postdoc at Argonne, he led detector development and coordination for the 16,000‑sensor SPT-3G camera, including an Antarctic deployment and cross‑institutional wafer testing. He combines hands‑on cryogenic fabrication and materials characterization with software tooling—authoring instrument automation and contributing enhancements to the widely used lmfit Python library for complex-valued model plotting and error propagation. Based in Santa Monica, he pairs deep experimental expertise with outreach and leadership, lecturing at the Adler Planetarium and serving on two Yale alumni boards. Notably, his background spans industrial engineering, national labs, and successful integration of hardware, firmware, and analysis software across large collaborations.
10 years of coding experience
12 years of employment as a software developer
Master of Science (MS), Physics, 3.97/4.0, Master of Science (MS), Physics, 3.97/4.0 at San Francisco State University
Semester Exchange Program, Semester Exchange Program at University of Newcastle
Doctor of Philosophy (Ph.D.), Physics, Doctor of Philosophy (Ph.D.), Physics at Yale University
Bachelor of Science (BS), Industrial Engineering, 3.28/4.0, Bachelor of Science (BS), Industrial Engineering, 3.28/4.0 at Clarkson University
Non-Linear Least Squares Minimization, with flexible Parameter settings, based on scipy.optimize, and with many additional classes and methods for curve fitting.
Role in this project:
Back-end Developer
Contributions:23 commits, 2 PRs, 20 comments in 1 month
Contributions summary:Faustin primarily focused on enhancing the `lmfit-py` library, specifically addressing plotting of complex-valued models. They implemented functionalities to convert complex data for plotting, including options for real, imaginary, absolute, and angle representations. Furthermore, the user made significant improvements to error propagation, handling cases with zero magnitude and fixing related docstrings. Several updates were done to the `ModelResult` class to facilitate these new features.
Make a sweet giant triangle confusogram (GTC) plot
Contributions:9 releases, 2 reviews, 215 commits in 5 years 9 months
trianglepythonplotdata-visualizationgtc
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