Samuel Lanthaler

32 papers A* 3Journal 29

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Probabilistic Framework for Solving High-Frequency Helmholtz Equations via Diffusion Models. CoRR Yicheng Zou, Samuel Lanthaler, Hossein Salahshoor
2026	J	`jnl`	Merging Memory and Space: A State Space Neural Operator. Trans. Mach. Learn. Res. Nodens F. Koren, Samuel Lanthaler
2025	J	`jnl`	Merging Memory and Space: A Spatiotemporal State Space Neural Operator. CoRR Nodens F. Koren, Samuel Lanthaler
2025	J	`jnl`	Scale-Consistent Learning for Partial Differential Equations. CoRR Zongyi Li, Samuel Lanthaler, Catherine Deng, Michael Chen, Yixuan Wang, Kamyar Azizzadenesheli, Anima Anandkumar
2025	J	`jnl`	Theory-to-Practice Gap for Neural Networks and Neural Operators. CoRR Philipp Grohs, Samuel Lanthaler, Margaret Trautner
2025	J	`jnl`	Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI. CoRR Bogdan Raonic, Siddhartha Mishra, Samuel Lanthaler
2025	J	`jnl`	Universality of physical neural networks with multivariate nonlinearity. CoRR Benjamin Savinson, David J. Norris, Siddhartha Mishra, Samuel Lanthaler
2024	J	`jnl`	Data Complexity Estimates for Operator Learning. CoRR Nikola B. Kovachki, Samuel Lanthaler, Hrushikesh Mhaskar
2024	J	`jnl`	Discretization Error of Fourier Neural Operators. CoRR Samuel Lanthaler, Andrew M. Stuart, Margaret Trautner
2024	J	`jnl`	Generative AI for fast and accurate Statistical Computation of Fluids. CoRR Roberto Molinaro, Samuel Lanthaler, Bogdan Raonic, Tobias Rohner, Victor Armegioiu, Zhong Yi Wan, Fei Sha, Siddhartha Mishra, Leonardo Zepeda-Núñez
2024	J	`jnl`	Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective. CoRR Samuel Lanthaler
2024	J	`jnl`	Operator Learning: Algorithms and Analysis. CoRR Nikola B. Kovachki, Samuel Lanthaler, Andrew M. Stuart
2023	A*	`conf`	Error Bounds for Learning with Vector-Valued Random Features. NeurIPS Samuel Lanthaler, Nicholas H. Nelsen
2023	J	`jnl`	Error Bounds for Learning with Vector-Valued Random Features. CoRR Samuel Lanthaler, Nicholas H. Nelsen
2023	A*	`conf`	Neural Oscillators are Universal. NeurIPS Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra
2023	J	`jnl`	Neural Oscillators are Universal. CoRR Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra
2023	A*	`conf`	Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities. ICLR Samuel Lanthaler, Roberto Molinaro, Patrik Hadorn, Siddhartha Mishra
2023	J	`jnl`	Operator learning with PCA-Net: upper and lower complexity bounds. CoRR Samuel Lanthaler
2023	J	`jnl`	Operator learning with PCA-Net: upper and lower complexity bounds. J. Mach. Learn. Res. Samuel Lanthaler
2023	J	`jnl`	The Nonlocal Neural Operator: Universal Approximation. CoRR Samuel Lanthaler, Zongyi Li, Andrew M. Stuart
2023	J	`jnl`	The curse of dimensionality in operator learning. CoRR Samuel Lanthaler, Andrew M. Stuart
2022	J	`jnl`	Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities. CoRR Samuel Lanthaler, Roberto Molinaro, Patrik Hadorn, Siddhartha Mishra
2021	J	`jnl`	Error estimates for DeepOnets: A deep learning framework in infinite dimensions. CoRR Samuel Lanthaler, Siddhartha Mishra, George Em Karniadakis
2021	J	`jnl`	On Universal Approximation and Error Bounds for Fourier Neural Operators. J. Mach. Learn. Res. Nikola B. Kovachki, Samuel Lanthaler, Siddhartha Mishra
2021	J	`jnl`	On the approximation of functions by tanh neural networks. CoRR Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra
2021	J	`jnl`	On the approximation of functions by tanh neural networks. Neural Networks Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra
2021	J	`jnl`	On the well-posedness of Bayesian inversion for PDEs with ill-posed forward problems. CoRR Samuel Lanthaler, Siddhartha Mishra, Franziska Weber
2021	J	`jnl`	On universal approximation and error bounds for Fourier Neural Operators. CoRR Nikola B. Kovachki, Samuel Lanthaler, Siddhartha Mishra
2020	J	`jnl`	On concentration in vortex sheets. CoRR Samuel Lanthaler
2020	J	`jnl`	On the Convergence of the Spectral Viscosity Method for the Two-Dimensional Incompressible Euler Equations with Rough Initial Data. Found. Comput. Math. Samuel Lanthaler, Siddhartha Mishra
2020	J	`jnl`	On the conservation of energy in two-dimensional incompressible flows. CoRR Samuel Lanthaler, Siddhartha Mishra, Carlos Parés-Pulido
2019	J	`jnl`	Statistical solutions of the incompressible Euler equations. CoRR Samuel Lanthaler, Siddhartha Mishra, Carlos Parés-Pulido

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.