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I build scientific Python software, ideally with PyTorch/JAX, focusing on interpretability, flexibility, uncertainty, and impact.

• blasé: Interpretable Machine Learning for super-resolution semi-empirical spectroscopy in PyTorch and JAX (paper)
• gollum: Python API to precomputed synthetic spectral models
• muler: Python API to échelle spectra from IGRINS, HPF, and Keck NIRSPEC (paper)
• ynot: A spectrograph digital twin for pixel-perfect échellogram forward modeling in PyTorch (paper draft)

• lightkurve: Python API for precision time series photometry from Kepler/K2/TESS
• Starfish: Robust likelihood function for astronomical spectral inference

• A Large and Variable Leading Tail of Helium in a Hot Saturn Undergoing Runaway Inflation (paper, code)
• Observationally Constraining the Starspot Properties of Magnetically Active M67 Sub-subgiant S1063 (paper, code)
• Placing the Spotted T Tauri Star LkCa 4 on an HR Diagram (paper, code)
• Optical characterization of gaps in directly bonded Si compound optics using infrared spectroscopy (paper, code)
• My PhD Thesis at UT Austin Astronomy (dissertation, revision history)

Starspots: contracosta, acdc, 🔒monhegan, 🔒 star-witness, 🔒hopful, 🔒 calico, xveganx, 🔒 KaneDoe, 🔒 varasly
Brown Dwarfs: 🔒gandules, varsity, 🔒 lombok, 🔒 ucdwhpf, jammer, 🔒 BAADE, 🔒zoja
Methodological: coldrum (ft. pysr), fiatlux, TgiF, HelloWorldNet, gpytorch-astro, ffi-motion, bombcat, probabilisticAGN
Hardware: nubble, postale