Invariance-based Multi-Clustering of Latent Space Embeddings for Equivariant Learning. - Chandrajit Bajaj; Avik Roy; Haoran Zhang (CoRR, 2021)

Robust Learning of Physics Informed Neural Networks. - Chandrajit Bajaj; Luke McLennan; Timothy Andeen; Avik Roy (CoRR, 2021)

Explainable AI for High Energy Physics. - Mark S. Neubauer; Avik Roy (CoRR, 2022)

Data Science and Machine Learning in Education. - Gabriele Benelli; Thomas Y. Chen; Javier M. Duarte; Matthew Feickert; Matthew J. Graham; Lindsey Gray; Dan Hackett; Philip C. Harris; Shih-Chieh Hsu; Gregor Kasieczka; Elham E Khoda; Matthias Komm; et al. (CoRR, 2022)

FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective. - Eliu A. Huerta; Ben Blaiszik; L. Catherine Brinson; Kristofer E. Bouchard; Daniel Diaz 003; Caterina Doglioni; Javier M. Duarte; Murali Emani; Ian T. Foster; Geoffrey C. Fox; Philip C. Harris; Lukas Heinrich; et al. (CoRR, 2022)

Recipes for when physics fails: recovering robust learning of physics informed neural networks. - Chandrajit Bajaj; Luke McLennan; Timothy Andeen; Avik Roy (Mach. Learn. Sci. Technol., 2023)

A detailed study of interpretability of deep neural network based top taggers. - Ayush Khot; Mark S. Neubauer; Avik Roy (Mach. Learn. Sci. Technol., 2023)

FAIR AI models in high energy physics. - Javier M. Duarte; Haoyang Li; Avik Roy; Ruike Zhu; Eliu A. Huerta; Daniel Diaz 003; Philip C. Harris; Raghav Kansal; Daniel S. Katz; Ishaan H. Kavoori; Volodymyr V. Kindratenko; Farouk Mokhtar; et al. (Mach. Learn. Sci. Technol., 2023)

Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks. - Ayush Khot; Xiwei Wang; Avik Roy; Volodymyr V. Kindratenko; Mark S. Neubauer (CoRR, 2025)