Publications

This is an list of my recent publications (since 2023). See my Google Scholar page for more.

2025

Hannah Lawrence*, Elyssa Hofgard*, Vasco Portilheiro, Yuxuan Chen, Tess Smidt, Robin Walters, “To Augment or Not to Augment? Diagnosing Distributional Symmetry Breaking,” 2025, arXiv preprint. A shorter version of this paper was also presented at the ICLR AI4Mat workshop in April 2025 OpenReview.

Zhang et al, “Artificial intelligence for science in quantum, atomistic, and continuum systems,” Foundations and Trends in Machine Learning, 2025, Vol. 18: No. 4, pp 385-912, link.

2024

Elyssa Hofgard, Rui Wang, Robin Walters, Tess Smidt, “Relaxed Equivariant Graph Neural Networks,” ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling, 2024, arXiv preprint.

Rui Wang, Elyssa Hofgard, “Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution,” Forty-first International Conference on Machine Learning, 2024, OpenReview.

2023

Garg et al, “Potentiality of automatic parameter tuning suite available in ACTS track reconstruction software framework,” 26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023), 2023, link.

The ATLAS Collaboration, “Search for dark photons from Higgs boson decays via ZH production with a photon plus missing transverse momentum signature from pp collisions at √s = 13 TeV with the ATLAS detector,” Journal of High Energy Physics, 2023, link.

Daniel J. Cotter, Elyssa Hofgard, John Novembre, Zachary A. Szpiech, Noah A. Rosenberg, “A rarefaction approach for measuring population differences in rare and common variation,” Genetics, Volume 224, Issue 2, 2023, link.

*denotes equal contribution