Daniel Whiteson

23 papers A* 2Journal 19Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Efficient estimation of unfactorizable systematic uncertainties. Mach. Learn. Sci. Technol. Alexis Romero, Kyle Cranmer, Daniel Whiteson
2025	J	`jnl`	Towards AI-assisted Neutrino Flavor Theory Design. CoRR Jason Benjamin Baretz, Max Fieg, Vijay Ganesh, Aishik Ghosh, V. Knapp-Perez, Jake Rudolph, Daniel Whiteson
2024	J	`jnl`	Artificial Intelligence for the Electron Ion Collider (AI4EIC). Comput. Softw. Big Sci. C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, Marco Battaglieri, J. C. Bernauer, M. Bondì, N. Branson, T. Britton, Anja Butter, I. Chahrour, P. Chatagnon, Evaristo Cisbani, E. W. Cline, S. Dash, C. T. Dean, W. Deconinck, A. Deshpande, Markus Diefenthaler, R. Ent, Cristiano Fanelli, M. Finger, E. Fol, S. Furletov, Y. Gao, James Giroux, N. C. Gunawardhana Waduge, Omar Hassan, P. L. Hegde, Roger José Hernández-Pinto, Astrid N. Hiller Blin, Tanja Horn, J. Huang, A. Jalotra, D. Jayakodige, B. Joo, M. Junaid, N. Kalantarians, Piyush Karande, B. Kriesten, R. Kunnawalkam Elayavalli, Y. Li, Meifeng Lin, Frank Liu, S. Liuti, G. Matousek, Matthew McEneaney, Diana McSpadden, T. Menzo, T. Miceli, Vinicius Mikuni, R. Montgomery, Benjamin Nachman, R. R. Nair, J. Niestroy, Salvador A. Ochoa-Oregon, J. Oleniacz, J. D. Osborn, Churamani Paudel, Connor Pecar, C. Peng, Gabriel N. Perdue, W. Phelps, Martin L. Purschke, H. Rajendran, Kishansingh Rajput, Y. Ren, David Francisco Rentería-Estrada, D. Richford, B. J. Roy, D. Roy, A. Saini, Nobuo Sato, T. Satogata, German Sborlini, Malachi Schram, David Shih, J. Singh, R. Singh, Andrzej Siódmok, J. Stevens, P. Stone, L. Suarez, K. Suresh, Abdel Nasser Tawfik, Fernando Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, Richard Tyson, S. Volkova, Anselm Vossen, Eric Walter, Daniel Whiteson, Michael Williams, S. Wu, N. Zachariou, P. Zurita
2024	J	`jnl`	Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion. CoRR Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson
2024	J	`jnl`	Learning Broken Symmetries with Approximate Invariance. CoRR Seth Nabat, Aishik Ghosh, Edmund Witkowski, Gregor Kasieczka, Daniel Whiteson
2024	J	`jnl`	Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks. CoRR Haoyang Li, Marko Stamenkovic, Alexander Shmakov, Michael Fenton, Darius Shih-Chieh Chao, Kaitlyn Maiya White, Caden Mikkelsen, Jovan Mitic, Cristina Mantilla Suarez, Melissa Quinnan, Greg Landsberg, Harvey B. Newman, Pierre Baldi, Daniel Whiteson, Javier M. Duarte
2024	J	`jnl`	The Landscape of Unfolding with Machine Learning. CoRR Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael James Fenton, Kevin Greif, Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn
2023	J	`jnl`	Artificial Intelligence for the Electron Ion Collider (AI4EIC). CoRR C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, Marco Battaglieri, J. C. Bernauer, M. Bondì, N. Branson, Thomas Britton, Anja Butter, I. Chahrour, P. Chatagnon, Evaristo Cisbani, E. W. Cline, S. Dash, C. T. Dean, W. Deconinck, A. Deshpande, Markus Diefenthaler, R. Ent, Cristiano Fanelli, M. Finger, M. Finger Jr., E. Fol, S. Furletov, Yuan Gao, James Giroux, N. C. Gunawardhana Waduge, R. Harish, Omar Hassan, P. L. Hegde, Roger José Hernández-Pinto, Astrid N. Hiller Blin, Tanja Horn, J. Huang, D. Jayakodige, B. Joo, M. Junaid, Piyush Karande, B. Kriesten, R. Kunnawalkam Elayavalli, M. Lin, Frank Liu, S. Liuti, G. Matousek, Matthew McEneaney, Diana McSpadden, T. Menzo, T. Miceli, Vinicius Mikuni, R. Montgomery, Benjamin Nachman, R. R. Nair, J. Niestroy, Salvador A. Ochoa-Oregon, J. Oleniacz, J. D. Osborn, Churamani Paudel, Connor Pecar, C. Peng, Gabriel N. Perdue, W. Phelps, Martin L. Purschke, Kishansingh Rajput, Y. Ren, David Francisco Rentería-Estrada, D. Richford, B. J. Roy, D. Roy, Nobuo Sato, T. Satogata, German Sborlini, Malachi Schram, David Shih, J. Singh, R. Singh, Andrzej Siódmok, P. Stone, J. Stevens, L. Suarez, K. Suresh, Abdel Nasser Tawfik, Fernando Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, Richard Tyson, S. Volkova, Anselm Vossen, Eric Walter, Daniel Whiteson, Michael Williams, S. Wu, N. Zachariou, P. Zurita
2023	A*	`conf`	End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics. NeurIPS Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson
2023	J	`jnl`	End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics. CoRR Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson
2023	J	`jnl`	Extended Symmetry Preserving Attention Networks for LHC Analysis. CoRR Michael James Fenton, Alexander Shmakov, Hideki Okawa, Yuji Li, Ko-Yang Hsiao, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi
2023	J	`jnl`	Generalizing to new calorimeter geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation. CoRR Junze Liu, Aishik Ghosh, Dylan Smith, Pierre Baldi, Daniel Whiteson
2022	J	`jnl`	Geometry-aware Autoregressive Models for Calorimeter Shower Simulations. CoRR Junze Liu, Aishik Ghosh, Dylan Smith, Pierre Baldi, Daniel Whiteson
2022	J	`jnl`	Machine-Learning Compression for Particle Physics Discoveries. CoRR Jack H. Collins, Yifeng Huang, Simon Knapen, Benjamin Nachman, Daniel Whiteson
2022	J	`jnl`	Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning. CoRR Phiala E. Shanahan, Kazuhiro Terao, Daniel Whiteson
2021	J	`jnl`	SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention. CoRR Alexander Shmakov, Michael James Fenton, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi
2020	J	`jnl`	Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks. CoRR Michael James Fenton, Alexander Shmakov, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi
2016	J	`jnl`	Parameterized Machine Learning for High-Energy Physics. CoRR Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter J. Sadowski, Daniel Whiteson
2014	—	`conf`	Deep Learning, Dark Knowledge, and Dark Matter. HEPML@NIPS Peter J. Sadowski, Julian Collado, Daniel Whiteson, Pierre Baldi
2014	J	`jnl`	Enhanced Higgs to $τ^+τ^-$ Searches with Deep Learning. CoRR Pierre Baldi, Peter J. Sadowski, Daniel Whiteson
2014	—	`conf`	Searching for Higgs Boson Decay Modes with Deep Learning. NIPS Peter J. Sadowski, Daniel Whiteson, Pierre Baldi
2009	J	`jnl`	Machine learning for event selection in high energy physics. Eng. Appl. Artif. Intell. Shimon Whiteson, Daniel Whiteson
2007	A*	`conf`	Stochastic Optimization for Collision Selection in High Energy Physics. AAAI Shimon Whiteson, Daniel Whiteson

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.