Sam Greydanus

13 papers Journal 12Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	The Cursive Transformer. CoRR Sam Greydanus, Zachary Wimpee
2023	J	`jnl`	Nature's Cost Function: Simulating Physics by Minimizing the Action. CoRR Tim Strang, Isabella Caruso, Sam Greydanus
2022	J	`jnl`	A Tutorial on Structural Optimization. CoRR Sam Greydanus
2022	J	`jnl`	Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately. CoRR Andrew Sosanya, Sam Greydanus
2021	J	`jnl`	Piecewise-constant Neural ODEs. CoRR Sam Greydanus, Stefan Lee, Alan Fern
2020	J	`jnl`	Lagrangian Neural Networks. CoRR Miles D. Cranmer, Sam Greydanus, Stephan Hoyer, Peter W. Battaglia, David N. Spergel, Shirley Ho
2020	J	`jnl`	Scaling down Deep Learning. CoRR Sam Greydanus
2019	J	`jnl`	Hamiltonian Neural Networks. CoRR Sam Greydanus, Misko Dzamba, Jason Yosinski
2019	—	`conf`	Learning Finite State Representations of Recurrent Policy Networks. ICLR (Poster) Anurag Koul, Alan Fern, Sam Greydanus
2019	J	`jnl`	Neural reparameterization improves structural optimization. CoRR Stephan Hoyer, Jascha Sohl-Dickstein, Sam Greydanus
2018	J	`jnl`	Learning Finite State Representations of Recurrent Policy Networks. CoRR Anurag Koul, Sam Greydanus, Alan Fern
2017	J	`jnl`	Learning the Enigma with Recurrent Neural Networks. CoRR Sam Greydanus
2017	J	`jnl`	Visualizing and Understanding Atari Agents. CoRR Sam Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern

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.

In 2025, he co-authored Assessing the Effectiveness of the Tigress Obfuscator Against MOPSA and BinaryNinja, published at CheckMATE. The paper evaluates the effectiveness of obfuscation techniques and their impact on source-level static analysis and binary-level decompilation.

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.