Dami Choi - RankMe

18 papers A* 6Journal 10Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Predictive Concept Decoders: Training Scalable End-to-End Interpretability Assistants. CoRR Vincent Huang, Dami Choi, Daniel D. Johnson, Sarah Schwettmann, Jacob Steinhardt
2024	A*	`conf`	Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. NeurIPS Johannes Treutlein, Dami Choi, Jan Betley, Samuel Marks, Cem Anil, Roger B. Grosse, Owain Evans
2024	J	`jnl`	Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. CoRR Johannes Treutlein, Dami Choi, Jan Betley, Cem Anil, Samuel Marks, Roger Baker Grosse, Owain Evans
2024	A*	`conf`	LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. NeurIPS James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Kristjanson Duvenaud
2024	J	`jnl`	LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. CoRR James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Duvenaud
2023	A*	`conf`	Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. NeurIPS Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani
2023	J	`jnl`	Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. CoRR Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani
2023	A*	`conf`	Tools for Verifying Neural Models' Training Data. NeurIPS Dami Choi, Yonadav Shavit, David Kristjanson Duvenaud
2023	J	`jnl`	Tools for Verifying Neural Models' Training Data. CoRR Dami Choi, Yonadav Shavit, David Duvenaud
2020	A*	`conf`	Gradient Estimation with Stochastic Softmax Tricks. NeurIPS Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison
2020	J	`jnl`	Gradient Estimation with Stochastic Softmax Tricks. CoRR Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison
2020	—	`conf`	Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. ICBINB@NeurIPS Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
2020	J	`jnl`	Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. CoRR Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
2019	J	`jnl`	Faster Neural Network Training with Data Echoing. CoRR Dami Choi, Alexandre Passos, Christopher J. Shallue, George E. Dahl
2019	A*	`conf`	Guided evolutionary strategies: augmenting random search with surrogate gradients. ICML Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein
2019	J	`jnl`	On Empirical Comparisons of Optimizers for Deep Learning. CoRR Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl
2018	—	`conf`	Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. ICLR (Poster) Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
2017	J	`jnl`	Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. CoRR Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud

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.