Lars Grasedyck

45 papers B 1Journal 43

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Reconstructing Disease Histories in Huge Discrete State Spaces. Künstliche Intell. Rudolf Schill, Maren Klever, Kevin Rupp, Y. Linda Hu, Andreas Lösch, Peter Georg, Simon Pfahler, Stefan Vocht, Stefan Hansch, Tilo Wettig, Lars Grasedyck, Rainer Spang
2024	J	`jnl`	Correcting for Observation Bias in Cancer Progression Modeling. J. Comput. Biol. Rudolf Schill, Maren Klever, Andreas Lösch, Y. Linda Hu, Stefan Vocht, Kevin Rupp, Lars Grasedyck, Rainer Spang, Niko Beerenwinkel
2024	J	`jnl`	Differentiated uniformization: a new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models. Comput. Stat. Kevin Rupp, Rudolf Schill, Jonas Süskind, Peter Georg, Maren Klever, Andreas Lösch, Lars Grasedyck, Tilo Wettig, Rainer Spang
2024	J	`jnl`	Modeling metastatic progression from cross-sectional cancer genomics data. Bioinform. Kevin Rupp, Andreas Lösch, Y. Linda Hu, Chenxi Nie, Rudolf Schill, Maren Klever, Simon Pfahler, Lars Grasedyck, Tilo Wettig, Niko Beerenwinkel, Rainer Spang
2024	B	`conf`	Overcoming Observation Bias for Cancer Progression Modeling. RECOMB Rudolf Schill, Maren Klever, Andreas Lösch, Y. Linda Hu, Stefan Vocht, Kevin Rupp, Lars Grasedyck, Rainer Spang, Niko Beerenwinkel
2024	J	`jnl`	Taming numerical imprecision by adapting the KL divergence to negative probabilities. Stat. Comput. Simon Pfahler, Peter Georg, Rudolf Schill, Maren Klever, Lars Grasedyck, Rainer Spang, Tilo Wettig
2022	J	`jnl`	Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format. SIAM/ASA J. Uncertain. Quantification Paul B. Rohrbach, Sergey Dolgov, Lars Grasedyck, Robert Scheichl
2021	J	`jnl`	Differentiated uniformization: A new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models. CoRR Kevin Rupp, Rudolf Schill, Jonas Süskind, Peter Georg, Maren Klever, Andreas Lösch, Lars Grasedyck, Tilo Wettig, Rainer Spang
2020	J	`jnl`	A parameter-dependent smoother for the multigrid method. CoRR Lars Grasedyck, Maren Klever, Christian Löbbert, Tim A. Werthmann
2020	J	`jnl`	Bayesian inversion for electromyography using low-rank tensor formats. CoRR Anna Rörich, Tim A. Werthmann, Dominik Göddeke, Lars Grasedyck
2020	J	`jnl`	Low-rank tensor methods for Markov chains with applications to tumor progression models. CoRR Peter Georg, Lars Grasedyck, Maren Klever, Rudolf Schill, Rainer Spang, Tilo Wettig
2020	J	`jnl`	Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format. CoRR Paul B. Rohrbach, Sergey Dolgov, Lars Grasedyck, Robert Scheichl
2019	J	`jnl`	Finding entries of maximum absolute value in low-rank tensors. CoRR Lars Grasedyck, Lukas Juschka, Christian Löbbert
2019	J	`jnl`	Stable ALS approximation in the TT-format for rank-adaptive tensor completion. Numerische Mathematik Lars Grasedyck, Sebastian Krämer
2019	J	`jnl`	The multipole approach for EEG forward modeling using the finite element method. NeuroImage Johannes Vorwerk, Anne Hanrath, Carsten H. Wolters, Lars Grasedyck
2018	J	`jnl`	7th Workshop on Matrix Equations and Tensor Techniques. Numer. Linear Algebra Appl. Peter Benner, Heike Faßbender, Lars Grasedyck, Daniel Kressner, Beatrice Meini, Valeria Simoncini
2018	J	`jnl`	Distributed hierarchical SVD in the Hierarchical Tucker format. Numer. Linear Algebra Appl. Lars Grasedyck, Christian Löbbert
2016	J	`jnl`	A nearly optimal multigrid method for general unstructured grids. Numerische Mathematik Lars Grasedyck, Lu Wang, Jinchao Xu
2016	—	`ch.`	Space and Time Parallel Multigrid for Optimization and Uncertainty Quantification in PDE Simulations. Software for Exascale Computing Lars Grasedyck, Christian Löbbert, Gabriel Wittum, Arne Nägel, Volker Schulz, Martin Siebenborn, Rolf Krause, Pietro Benedusi, Uwe Küster, Björn Dick
2016	J	`jnl`	Tensor-Sparsity of Solutions to High-Dimensional Elliptic Partial Differential Equations. Found. Comput. Math. Wolfgang Dahmen, Ronald A. DeVore, Lars Grasedyck, Endre Süli
2015	J	`jnl`	Hierarchical Tensor Approximation of Output Quantities of Parameter-Dependent PDEs. SIAM/ASA J. Uncertain. Quantification Jonas Ballani, Lars Grasedyck
2015	J	`jnl`	Parallel tensor sampling in the hierarchical Tucker format. Comput. Vis. Sci. Lars Grasedyck, Ronald Kriemann, Christian Löbbert, Arne Nägel, Gabriel Wittum, Konstantinos Xylouris
2015	J	`jnl`	Variants of Alternating Least Squares Tensor Completion in the Tensor Train Format. SIAM J. Sci. Comput. Lars Grasedyck, Melanie Kluge, Sebastian Krämer
2014	J	`jnl`	Tree Adaptive Approximation in the Hierarchical Tensor Format. SIAM J. Sci. Comput. Jonas Ballani, Lars Grasedyck
2013	J	`jnl`	A projection method to solve linear systems in tensor format. Numer. Linear Algebra Appl. Jonas Ballani, Lars Grasedyck
2013	J	`jnl`	Solving an elliptic PDE eigenvalue problem via automated multi-level substructuring and hierarchical matrices. Comput. Vis. Sci. Peter Gerds, Lars Grasedyck
2012	J	`jnl`	The AL Basis for the Solution of Elliptic Problems in Heterogeneous Media. Multiscale Model. Simul. Lars Grasedyck, Isabelle Greff, Stefan A. Sauter
2011	J	`jnl`	An Introduction to Hierarchical (H-) Rank and TT-Rank of Tensors with Examples. Comput. Methods Appl. Math. Lars Grasedyck, Wolfgang Hackbusch
2010	J	`jnl`	Hierarchical Singular Value Decomposition of Tensors. SIAM J. Matrix Anal. Appl. Lars Grasedyck
2009	J	`jnl`	A full subtraction approach for finite element method based source analysis using constrained Delaunay tetrahedralisation. NeuroImage Florian Drechsler, Carsten H. Wolters, Thomas Dierkes, Hang Si, Lars Grasedyck
2009	J	`jnl`	Domain decomposition based ℋ-LU preconditioning. Numerische Mathematik Lars Grasedyck, Ronald Kriemann, Sabine Le Borne
2008	J	`jnl`	Nonlinear multigrid for the solution of large-scale Riccati equations in low-rank and ℋ-matrix format. Numer. Linear Algebra Appl. Lars Grasedyck
2007	J	`jnl`	A Multigrid Method to Solve Large Scale Sylvester Equations. SIAM J. Matrix Anal. Appl. Lars Grasedyck, Wolfgang Hackbusch
2007	J	`jnl`	A structured low-rank wavelet solver for the Ornstein-Zernike integral equation. Computing Maxim V. Fedorov, Heinz-Jürgen Flad, Gennady N. Chuev, Lars Grasedyck, Boris N. Khoromskij
2007	J	`jnl`	Numerical Mathematics of the Subtraction Method for the Modeling of a Current Dipole in EEG Source Reconstruction Using Finite Element Head Models. SIAM J. Sci. Comput. Carsten H. Wolters, Harald Köstler, Christian Möller, Jochen Härdtlein, Lars Grasedyck, Wolfgang Hackbusch
2007	J	`jnl`	Optimal Panel-Clustering in the Presence of Anisotropic Mesh Refinement. SIAM J. Numer. Anal. Ivan G. Graham, Lars Grasedyck, Wolfgang Hackbusch, Stefan A. Sauter
2006	J	`jnl`	H-matrix Preconditioners in Convection-Dominated Problems. SIAM J. Matrix Anal. Appl. Sabine Le Borne, Lars Grasedyck
2005	J	`jnl`	Adaptive Recompression of Computing Lars Grasedyck
2005	J	`jnl`	Hybrid cross approximation of integral operators. Numerische Mathematik Steffen Börm, Lars Grasedyck
2004	J	`jnl`	Adaptive Geometrically Balanced Clustering of H-Matrices. Computing Lars Grasedyck, Wolfgang Hackbusch, Sabine Le Borne
2004	J	`jnl`	Existence and Computation of Low Kronecker-Rank Approximations for Large Linear Systems of Tensor Product Structure. Computing Lars Grasedyck
2004	J	`jnl`	Existence of a low rank or ℋ-matrix approximant to the solution of a Sylvester equation. Numer. Linear Algebra Appl. Lars Grasedyck
2004	J	`jnl`	Low-Rank Approximation of Integral Operators by Interpolation. Computing Steffen Börm, Lars Grasedyck
2003	J	`jnl`	Construction and Arithmetics of H-Matrices. Computing Lars Grasedyck, Wolfgang Hackbusch
2003	J	`jnl`	Solution of Large Scale Algebraic Matrix Riccati Equations by Use of Hierarchical Matrices. Computing Lars Grasedyck, Wolfgang Hackbusch, Boris N. Khoromskij

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.