Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning. - Mike Walmsley; Lewis Smith; Chris J. Lintott; Yarin Gal; Steven Bamford; Hugh Dickinson; Lucy Fortson; Sandor Kruk; Karen Masters; Claudia Scarlata; Brooke Simmons; Rebecca J. Smethurst; et al. (CoRR, 2019)

Help Me to Help You: Machine Augmented Citizen Science. - Darryl Wright; Lucy Fortson; Chris J. Lintott; Michael Laraia; Mike Walmsley (ACM Trans. Soc. Comput., 2019)

Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314, 000 Galaxies. - Mike Walmsley; Chris J. Lintott; Tobias Géron; Sandor Kruk; Coleman Krawczyk; Kyle W. Willett; Steven Bamford; William Keel; Lee S. Kelvin; Lucy Fortson; Karen L. Masters; Vihang Mehta; et al. (CoRR, 2021)

Revisiting Citizen Science Through the Lens of Hybrid Intelligence. - Janet Rafner; Miroslav Gajdacz; Gitte Kragh; Arthur Hjorth; Anna Jia Gander; Blanka Palfi; Aleks Berditchevskaia; François Grey; Kobi Gal; Avi Segal; Mike Walmsley; Josh Aaron Miller; et al. (CoRR, 2021)

Practical Galaxy Morphology Tools from Deep Supervised Representation Learning. - Mike Walmsley; Anna M. M. Scaife; Chris J. Lintott; Michelle Lochner; Verlon Etsebeth; Tobias Géron; Hugh Dickinson; Lucy Fortson; Sandor Kruk; Karen L. Masters; Kameswara Bharadwaj Mantha; Brooke D. Simmons (CoRR, 2021)

A new Workflow for Human-AI Collaboration in Citizen Science. - Ya'akov (Kobi) Gal; Avi Segal; Ece Kamar; Eric Horvitz; Chris J. Lintott; Mike Walmsley (GoodIT, 2022)

Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification. - Devina Mohan; Anna M. M. Scaife; Fiona Porter; Mike Walmsley; Micah Bowles (CoRR, 2022)

Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift. - Inigo V. Slijepcevic; Anna M. M. Scaife; Mike Walmsley; Micah Bowles; Ivy Wong; Stanislav S. Shabala; Hongming Tang (CoRR, 2022)

Towards Galaxy Foundation Models with Hybrid Contrastive Learning. - Mike Walmsley; Inigo Val Slijepcevic; Micah Bowles; Anna M. M. Scaife (CoRR, 2022)

A New Task: Deriving Semantic Class Targets for the Physical Sciences. - Micah Bowles; Hongming Tang; Eleni Vardoulaki; Emma L. Alexander; Yan Luo; Lawrence Rudnick; Mike Walmsley; Fiona Porter; Anna M. M. Scaife; Inigo Val Slijepcevic; Gary Segal (CoRR, 2022)

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)

Deep Learning Segmentation of Spiral Arms and Bars. - Mike Walmsley; Ashley Spindler (CoRR, 2023)

Rare Galaxy Classes Identified In Foundation Model Representations. - Mike Walmsley; Anna M. M. Scaife (CoRR, 2023)

Zoobot: Adaptable Deep Learning Models for Galaxy Morphology. - Mike Walmsley; Campbell Allen; Ben Aussel; Micah Bowles; Kasia Gregorowicz; Inigo Val Slijepcevic; Chris J. Lintott; Anna M. M. Scaife; Maja Jablonska; Kosio Karchev; Denise Lanzieri; Devina Mohan; et al. (J. Open Source Softw., 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)

Scaling Laws for Galaxy Images. - Mike Walmsley; Micah Bowles; Anna M. M. Scaife; Jason Shingirai Makechemu; Alexander J. Gordon; Annette M. N. Ferguson; Robert G. Mann; James Pearson; Jürgen J. Popp; Jo Bovy; Joshua S. Speagle; Hugh Dickinson; et al. (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)

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

Sparks of Science: Hypothesis Generation Using Structured Paper Data. - Charles O'Neill; Tirthankar Ghosal; Roberta Raileanu; Mike Walmsley; Thang D. Bui; Kevin Schawinski; Ioana Ciuca (CoRR, 2025)

DataS^3: Dataset Subset Selection for Specialization. - Neha Hulkund; Alaa Maalouf; Levi Cai; Daniel Yang; Tsun-Hsuan Wang; Abigail O'Neil; Timm Haucke; Sandeep Mukherjee; Vikram Ramaswamy; Judy Hansen Shen; Gabriel Tseng; Mike Walmsley; et al. (CoRR, 2025)