Crowdsourcing quality control for Dark Energy Survey images. - Peter Melchior; Erin Sheldon; Alex Drlica-Wagner; Eli S. Rykoff; Timothy M. C. Abbott; Filipe B. Abdalla; Sahar Allam; Aurélien Benoit-Lévy; David D. Brooks; Elizabeth Buckley-Geer; Aurelio Carnero Rosell; Matias Carrasco Kind; et al. (Astron. Comput., 2016)

SPOKES: An end-to-end simulation facility for spectroscopic cosmological surveys. - Brian Nord; Adam Amara; Alexandre Réfrégier; La. Gamper; Lukas Gamper; B. Hambrecht; Chihway L. Chang; Jaime E. Forero-Romero; Santiago Serrano; Carla Cunha; O. Coles; Andrina Nicola; et al. (Astron. Comput., 2016)

Challenges and Approaches for Mining Astronomical Data and Complex Models. - M. J. Marquez; Brian Nord (BCD, 2019)

DeepCMB: Lensing reconstruction of the cosmic microwave background with deep neural networks. - João Caldeira; W. L. K. Wu; Brian Nord; Camille Avestruz; Shubhendu Trivedi; K. T. Story (Astron. Comput., 2019)

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era. - Brian Nord; Andrew J. Connolly; Jamie Kinney; Jeremy Kubica; Gautaum Narayan; Joshua E. G. Peek; Chad Schafer; Erik J. Tollerud; Camille Avestruz; Gutti Jogesh Babu; Simon Birrer; Douglas Burke; et al. (CoRR, 2019)

Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan". - James F. Amundson; James Annis; Camille Avestruz; D. Bowring; João Caldeira; Giuseppe Cerati; Chihway L. Chang; Scott Dodelson; D. Elvira; A. Farahi; Krzysztof L. Genser; Lindsey Gray; et al. (CoRR, 2019)

Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer. - João Caldeira; Joshua A. Job; Steven H. Adachi; Brian Nord; Gabriel N. Perdue (CoRR, 2019)

Unsupervised Resource Allocation with Graph Neural Networks. - Miles D. Cranmer; Peter Melchior; Brian Nord (Preregister@NeurIPS, 2020)

DeepMerge: Classifying high-redshift merging galaxies with deep neural networks. - Aleksandra Ciprijanovic; Gregory F. Snyder; Brian Nord; Joshua E. G. Peek (Astron. Comput., 2020)

Domain adaptation techniques for improved cross-domain study of galaxy mergers. - Aleksandra Ciprijanovic; Diana Kafkes; S. Jenkins; K. Downey; Gabriel N. Perdue; Sandeep Madireddy; T. Johnston; Brian Nord (CoRR, 2020)

Deep learning insights into cosmological structure formation. - Luisa Lucie-Smith; Hiranya V. Peiris; Andrew Pontzen; Brian Nord; Jeyan Thiyagalingam (CoRR, 2020)

A machine learning approach to the detection of ghosting and scattered light artifacts in dark energy survey images. - Chihway L. Chang; Alex Drlica-Wagner; S. M. Kent; Brian Nord; Donah Michelle Wang; Michael H. L. S. Wang (Astron. Comput., 2021)

DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning. - Zhen Lin 001; Nicholas Huang; Camille Avestruz; W. L. Kimmy Wu; Shubhendu Trivedi; João Caldeira; Brian Nord (CoRR, 2021)

DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains. - Aleksandra Ciprijanovic; Diana Kafkes; K. Downey; S. Jenkins; Gabriel N. Perdue; Sandeep Madireddy; T. Johnston; Gregory F. Snyder; Brian Nord (CoRR, 2021)

Robustness of deep learning algorithms in astronomy - galaxy morphology studies. - Aleksandra Ciprijanovic; Diana Kafkes; Gabriel N. Perdue; Kevin Pedro; Gregory F. Snyder; F. Javier Sánchez; Sandeep Madireddy; Stefan M. Wild; Brian Nord (CoRR, 2021)

SkyPy: A package for modelling the Universe. - Adam Amara; Lucia de la Bella; Simon Birrer; Sarah Bridle; Juan Cordero; Ginevra Favole; Ian Harrison; Ian Harry; William G. Hartley; Coleman Krawczyk; Andrew P. Lundgren; Brian Nord; et al. (J. Open Source Softw., 2021)

deeplenstronomy: A dataset simulation package for strong gravitational lensing. - Robert Morgan; Brian Nord; Simon Birrer; Joshua Yao-Yu Lin; Jason Poh (J. Open Source Softw., 2021)

Deeply uncertain: comparing methods of uncertainty quantification in deep learning algorithms. - João Caldeira; Brian Nord (Mach. Learn. Sci. Technol., 2021)

Ten simple rules to cultivate transdisciplinary collaboration in data science. - Faryad Sahneh; Meghan A. Balk; Marina Kisley; Chi-Kwan Chan; Mercury Fox; Brian Nord; Eric Lyons 002; Tyson Lee Swetnam; Daniela Huppenkothen; Will Sutherland; Ramona L. Walls; Daven P. Quinn; et al. (PLoS Comput. Biol., 2021)

DeepGhostBusters: Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts from optical survey images. - Dimitrios Tanoglidis; Aleksandra Ciprijanovic; Alex Drlica-Wagner; Brian Nord; Michael H. L. S. Wang; A. Jacob Amsellem; K. Downey; S. Jenkins; Diana Kafkes; Z. Zhang (Astron. Comput., 2022)

Machine Learning and Cosmology. - Cora Dvorkin; Siddharth Mishra-Sharma; Brian Nord; V. Ashley Villar; Camille Avestruz; Keith Bechtol; Aleksandra Ciprijanovic; Andrew J. Connolly; Lehman H. Garrison; Gautham Narayan; Francisco Villaescusa-Navarro (CoRR, 2022)

Learning Representation for Bayesian Optimization with Collision-free Regularization. - Fengxue Zhang; Brian Nord; Yuxin Chen 001 (CoRR, 2022)

Discovering the building blocks of dark matter halo density profiles with neural networks. - Luisa Lucie-Smith; Hiranya V. Peiris; Andrew Pontzen; Brian Nord; Jeyan Thiyagalingam; Davide Piras (CoRR, 2022)

Interpretable Uncertainty Quantification in AI for HEP. - Thomas Y. Chen; Biprateep Dey; Aishik Ghosh; Michael Kagan; Brian Nord; Nesar Ramachandra (CoRR, 2022)

Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection. - Aleksandra Ciprijanovic; Ashia Lewis; Kevin Pedro; Sandeep Madireddy; Brian Nord; Gabriel N. Perdue; Stefan M. Wild (CoRR, 2022)

Neural Inference of Gaussian Processes for Time Series Data of Quasars. - Egor Danilov; Aleksandra Ciprijanovic; Brian Nord (CoRR, 2022)

DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification. - Aleksandra Ciprijanovic; Diana Kafkes; Gregory F. Snyder; F. Javier Sánchez; Gabriel Nathan Perdue; Kevin Pedro; Brian Nord; Sandeep Madireddy; Stefan M. Wild (Mach. Learn. Sci. Technol., 2022)

DIGS: deep inference of galaxy spectra with neural posterior estimation. - Gourav Khullar; Brian Nord; Aleksandra Ciprijanovic; Jason Poh; Fei Xu (Mach. Learn. Sci. Technol., 2022)

WavPool: A New Block for Deep Neural Networks. - Samuel D. McDermott; Maggie Voetberg; Brian Nord (CoRR, 2023)

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)

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets. - Andrea Roncoli; Aleksandra Ciprijanovic; Maggie Voetberg; Francisco Villaescusa-Navarro; Brian Nord (CoRR, 2023)

Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning. - Franco Terranova; Maggie Voetberg; Brian Nord; Amanda Pagul (CoRR, 2023)

DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection. - Aleksandra Ciprijanovic; Ashia Lewis; Kevin Pedro; Sandeep Madireddy; Brian Nord; Gabriel N. Perdue; Stefan M. Wild (Mach. Learn. Sci. Technol., 2023)

A robust estimator of mutual information for deep learning interpretability. - Davide Piras; Hiranya V. Peiris; Andrew Pontzen; Luisa Lucie-Smith; Ningyuan Guo; Brian Nord (Mach. Learn. Sci. Technol., 2023)

Domain-Adaptive Neural Posterior Estimation for Strong Gravitational Lens Analysis. - Paxson Swierc; Marcos Tamargo-Arizmendi; Aleksandra Ciprijanovic; Brian Nord (CoRR, 2024)

Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification. - Shrihan Agarwal; Aleksandra Ciprijanovic; Brian Nord (CoRR, 2024)

DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods. - Rebecca Nevin; Aleksandra Ciprijanovic; Brian Nord (CoRR, 2024)

Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks. - Ashwin Samudre; Mircea Petrache; Brian Nord; Shubhendu Trivedi (AISTATS, 2025)

SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networks. - Sneh Pandya; Purvik Patel; Brian Nord; Mike Walmsley; Aleksandra Ciprijanovic (CoRR, 2025)

DeepBench: A simulation package for physical benchmarking data. - Maggie Voetberg; Ashia Livaudais; Becky Nevin; Omari Paul; Brian Nord (J. Open Source Softw., 2025)

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning. - Shubhendu Trivedi; Brian Nord (CoRR, 2025)