Predicting galaxy spectra from images with hybrid convolutional neural networks. - John F. Wu; Joshua E. G. Peek (CoRR, 2020)

Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers. - Daniela Huppenkothen; Michelle Ntampaka; M. Ho; M. Fouesneau; Brian Nord; Joshua E. G. Peek; Mike Walmsley; John F. Wu; Camille Avestruz; T. Buck; M. Brescia; D. P. Finkbeiner; et al. (CoRR, 2023)

The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data. - Eirini Angeloudi; Jeroen Audenaert; Micah Bowles; Benjamin M. Boyd; David Chemaly; Brian Cherinka; Ioana Ciuca; Miles D. Cranmer; Aaron Do; Matthew Grayling; Erin E. Hayes; Tom Hehir; et al. (NeurIPS, 2024)

Designing an Evaluation Framework for Large Language Models in Astronomy Research. - John F. Wu; Alina Hyk; Kiera McCormick; Christine Ye; Simone Astarita; Elina Baral; Jo Ciuca; Jesse Cranney; Anjalie Field; Kartheik Iyer; Philipp Koehn; Jenn Kotler; et al. (CoRR, 2024)

Disentangling Dense Embeddings with Sparse Autoencoders. - Charles O'Neill; Christine Ye; Kartheik Iyer; John F. Wu (CoRR, 2024)

pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy. - Kartheik Iyer; Mikaeel Yunus; Charles O'Neill; Christine Ye; Alina Hyk; Kiera McCormick; Ioana Ciuca; John F. Wu; Alberto Accomazzi; Simone Astarita; Rishabh Chakrabarty; Jesse Cranney; et al. (CoRR, 2024)

Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks. - Nikhil Garuda; John F. Wu; Dylan Nelson; Annalisa Pillepich (CoRR, 2024)

Deep learning cosmic ray transport from density maps of simulated, turbulent gas. - Chad Bustard; John F. Wu (Mach. Learn. Sci. Technol., 2024)

Insights on Galaxy Evolution from Interpretable Sparse Feature Networks. - John F. Wu (CoRR, 2025)

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs. - Alina Hyk; Kiera McCormick; Mian Zhong; Ioana Ciuca; Sanjib Sharma; John F. Wu; Joshua E. G. Peek; Kartheik Iyer; Ziang Xiao; Anjalie Field (CoRR, 2025)