Lars Struen Imsland

23 papers Journal 16Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes. Appl. Soft Comput. Bjarne A. Grimstad, Kristian Løvland, Lars Struen Imsland, Vidar Gunnerud
2025	—	`conf`	Advanced Real-Time Iterations and Short-Horizon Predictor for Fast Nonlinear Model Predictive Control. CCTA Trym Arve L. Gabrielsen, Lars Struen Imsland, Sébastien Gros
2025	—	`conf`	Entropic Risk-Based Predictive Control for Wind Integrated Energy Systems Under Market Participation. CCTA Kang Qiu, Sébastien Gros, Lars Struen Imsland
2025	—	`conf`	On a Closed-Loop Identification Challenge in Feedback Optimization. CCTA Kristian Løvland, Lars Struen Imsland, Bjarne A. Grimstad
2025	J	`jnl`	On a closed-loop identification challenge in feedback optimization. CoRR Kristian Løvland, Lars Struen Imsland, Bjarne A. Grimstad
2025	—	`conf`	Persistently Exciting Online Feedback Optimization Controller with Minimal Perturbations. CCTA Tore Gude, Marta A. Zagorowska, Lars Struen Imsland
2025	J	`jnl`	Persistently Exciting Online Feedback Optimization Controller with Minimal Perturbations. CoRR Tore Gude, Marta A. Zagorowska, Lars Struen Imsland
2025	—	`conf`	Probabilistic Forecasting for Multi-Stage Nonlinear Model Predictive Control. CCTA Kiet Tuan Hoang, Christian Ankerstjerne Thilker, Brage Rugstad Knudsen, Lars Struen Imsland
2024	J	`jnl`	A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes. CoRR Bjarne A. Grimstad, Kristian Løvland, Lars Struen Imsland, Vidar Gunnerud
2024	—	`conf`	An updated look on the convergence and consistency of data-driven dynamical models. CDC Kristian Løvland, Bjarne A. Grimstad, Lars Struen Imsland
2024	J	`jnl`	An updated look on the convergence and consistency of data-driven dynamical models. CoRR Kristian Løvland, Bjarne A. Grimstad, Lars Struen Imsland
2024	J	`jnl`	Complementarity-constrained predictive control for efficient gas-balanced hybrid power systems. CoRR Kiet Tuan Hoang, Brage Rugstad Knudsen, Lars Struen Imsland
2024	J	`jnl`	Neural networks informed by physics for modeling mass flow rate in a production wellbore. Eng. Appl. Artif. Intell. Luis Fernando Nazari, Eduardo Camponogara, Lars Struen Imsland, Laio Oriel Seman
2024	J	`jnl`	Physics-Informed Neural Networks with Skip Connections for Modeling and Control of Gas-Lifted Oil Wells. CoRR Jonas Ekeland Kittelsen, Eric Aislan Antonelo, Eduardo Camponogara, Lars Struen Imsland
2024	J	`jnl`	Physics-Informed Neural Networks with skip connections for modeling and control of gas-lifted oil wells. Appl. Soft Comput. Jonas Ekeland Kittelsen, Eric Aislan Antonelo, Eduardo Camponogara, Lars Struen Imsland
2023	J	`jnl`	A Survey on the Application of Machine Learning and Metaheuristic Algorithms for Intelligent Proxy Modeling in Reservoir Simulation. Comput. Chem. Eng. Cuthbert Shang Wui Ng, Menad Nait Amar, Ashkan Jahanbani Ghahfarokhi, Lars Struen Imsland
2023	J	`jnl`	Multi-unit soft sensing permits few-shot learning. CoRR Bjarne A. Grimstad, Kristian Løvland, Lars Struen Imsland
2022	J	`jnl`	Adjustment formulas for learning causal steady-state models from closed-loop operational data. CoRR Kristian Løvland, Bjarne A. Grimstad, Lars Struen Imsland
2022	J	`jnl`	Framework for produced water discharge management with flow-weighted mean concentration based economic model predictive control. Comput. Chem. Eng. Otávio Fonseca Ivo, Lars Struen Imsland
2022	J	`jnl`	Passive learning to address nonstationarity in virtual flow metering applications. Expert Syst. Appl. Mathilde Hotvedt, Bjarne A. Grimstad, Lars Struen Imsland
2021	J	`jnl`	Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study. CoRR Bjarne A. Grimstad, Mathilde Hotvedt, Anders T. Sandnes, Odd Kolbjørnsen, Lars Struen Imsland
2021	J	`jnl`	Bayesian neural networks for virtual flow metering: An empirical study. Appl. Soft Comput. Bjarne A. Grimstad, Mathilde Hotvedt, Anders T. Sandnes, Odd Kolbjørnsen, Lars Struen Imsland
2014	—	`conf`	Full algorithmic differentiation of a Rosenbrock-type method for direct single shooting. ECC Ralf Hannemann-Tamás, Lars Struen Imsland

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.