Publications

(2023). Lorentz group equivariant autoencoders. Eur. Phys. J. C.

PDF arXiv Cite Code Project Project DOI

(2023). Evaluating generative models in high energy physics. Phys. Rev. D.

PDF arXiv Cite Code Project DOI

(2022). FAIR AI Models in High Energy Physics. Submitted to Machine Learning: Science and Technology.

PDF arXiv Cite

(2022). Do graph neural networks learn traditional jet substructure?. ML and the Physical Sciences Workshop @ NeurIPS 2022.

PDF arXiv Cite Project Poster

(2022). Particle-based fast jet simulation at the LHC with variational autoencoders. Machine Learning: Science and Technology.

PDF arXiv Cite Project DOI

(2022). Improving Di-Higgs Sensitivity at Future Colliders in Hadronic Final States with Machine Learning. Contribution to Snowmass 2022 Summer Study.

PDF arXiv Cite Project

(2022). A FAIR and AI-ready Higgs boson decay dataset. Nature Scientific Data.

PDF arXiv Cite DOI

(2021). Particle Cloud Generation with Message Passing Generative Adversarial Networks. NeurIPS 2021.

PDF arXiv Cite Code Dataset Project

(2021). Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance. ML and the Physical Sciences Workshop @ NeurIPS 2021.

PDF arXiv Cite Project Project Poster

(2021). Explaining machine-learned particle-flow reconstruction. ML and the Physical Sciences Workshop @ NeurIPS 2021.

PDF arXiv Cite Project Project Poster

(2021). Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC. LatinX in AI Research Workshop @ ICML 2021.

PDF arXiv Cite Project

(2020). Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics. ML and the Physical Sciences Workshop @ NeurIPS 2020.

PDF arXiv Cite Code Project