Selected Papers

Combinatorial Optimization / Hierarchical Clustering / Reinforcement Learning

Computing the Bayes-optimal classifier and exact maximum likelihood estimator with a semi-realistic generative model for jet physics
Matthew Drnevich, Lauren Greenspan, Sebastian Macaluso, Kyle Cranmer, Duccio Pappadopulo
NeurIPS workshop on Machine Learning and the Physical Sciences (ML4PS 2022) [ workshop paper ]

The Quantum Trellis: A classical algorithm for sampling the parton shower with interference effects
Sebastian Macaluso and Kyle Cranmer
NeurIPS workshop on Machine Learning and the Physical Sciences (ML4PS 2021) [ workshop paper ][ arXiv ]

Exact and Approximate Hierarchical Clustering Using A*
Craig S Greenberg*, Sebastian Macaluso*, Nicholas Monath*, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum
37th Conference on Uncertainty in Artificial Intelligence (UAI 2021) [ conference paper ] [ arXiv ] [ code ]

Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering
C. S. Greenberg*, S. Macaluso*, N. Monath, J.-A. Lee, P. Flaherty, K. Cranmer, A. McGregor, and A. McCallum
The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021) [ conference paper ] [ arXiv ]) [ code ]
(Also: Machine Learning and the Physical Sciences workshop at NeurIPS 2020 [ workshop paper ] The 4th Workshop on Tractable Probabilistic Modeling [ workshop paper ] )

Hierarchical clustering in particle physics through reinforcement learning
Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, Kyle Cranmer
NeurIPS workshop on Machine Learning and the Physical Sciences (ML4PS 2020) [ workshop paper ][ arXiv ]

Deep Learning and Statistics for Physics

Reframing Jet Physics with New Computational Methods
Kyle Cranmer, Matthew Drnevich, Sebastian Macaluso*, Duccio Pappadopulo (authors in alphabetical order)
25th International Conference on Computing in High-Energy and Nuclear Physics [ conference paper ] [ arXiv ]

The Machine Learning Landscape of Top Taggers
Gregor Kasieczka, Tilman Plehn, S. Macaluso et al.
SciPost Phys. 7 (2019) 014 [ journal paper ] [ arXiv ])

Pulling Out All the Tops with Computer Vision and Deep Learning
S. Macaluso and D. Shih
Journal of High Energy Physics volume 2018, Article number: 121 (2018) [ journal paper ] [ arXiv ]

Particle Physics Theory

(Authors in alphabetical order)

Cornering Natural SUSY at LHC Run II and Beyond
M. R. Buckley, D. Feld, S. Macaluso, A. Monteux, and D. Shih
Journal of High Energy Physics volume 2017, Article number: 115 (2017) [ journal paper ] [ arXiv ]

Dark Matter and the Higgs in Natural SUSY A. Basirnia, S. Macaluso, and D. Shih Journal of High Energy Physics volume 2017, Article number: 73 (2017) [ journal paper ] [ arXiv ]

Revealing Compressed Stops Using High-Momentum Recoils
S. Macaluso, M. Park, D. Shih, and B. Tweedie
Journal of High Energy Physics volume 2016, Article number: 151 (2016) [ journal paper ] [ arXiv ]

Deep inelastic scattering structure functions of holographic spin-1 hadrons with N_f≥ 1
Ezequiel Koile, Sebastian Macaluso, Martin Schvellinger
Journal of High Energy Physics volume 2014, Article number: 166 (2014) [ journal paper ] [ arXiv ]

Deep inelastic scattering from holographic spin-one hadrons
Ezequiel Koile, Sebastian Macaluso and Martin Schvellinger
Journal of High Energy Physics volume 2012, Article number: 103 (2012) [ journal paper ] [ arXiv ]