Samuel Yanes Luis

24 papers B 2Journal 18Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner. CoRR Alejandro Mendoza Barrionuevo, Dame Seck Diop, Alejandro Casado Pérez, Daniel Gutiérrez-Reina, Sergio L. Toral Marín, Samuel Yanes Luis
2026	J	`jnl`	Beyond Task Performance: A Metric-Based Analysis of Sequential Cooperation in Heterogeneous Multi-Agent Destructive Foraging. CoRR Alejandro Mendoza Barrionuevo, Samuel Yanes Luis, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2026	J	`jnl`	From Project-Based Learning to Service Learning: A Practical Approach. Rev. Iberoam. de Tecnol. del Aprendiz. Manuel A. Perales-Esteve, Samuel Yanes Luis, Alfredo Pérez Vega-Leal
2025	J	`jnl`	Assessment of CNN-based methods for discrimination of olive planting systems with Sentinel-2 images. Comput. Electron. Agric. Cristina Martínez-Ruedas, Samuel Yanes Luis, Rafael Linares-Burgos, Daniel Gutiérrez-Reina, Isabel Luisa Castillejo-González
2025	J	`jnl`	Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles With Deep Reinforcement Learning. IEEE Robotics Autom. Lett. Alejandro Mendoza Barrionuevo, Samuel Yanes Luis, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2025	—	`conf`	VAE-Informed Patrolling for Online Environmental Monitoring with Heterogeneous ASV-UAV Teams. ECMR Alex Bassot, Michele Antonazzi, Edoardo Rodiani, Samuel Yanes Luis, Matteo Luperto, Nicola Basilico
2025	J	`jnl`	Variational model-based Deep Reinforcement Learning for Non-Homogeneous Patrolling aquatic environments with multiple unmanned surface vehicles. Expert Syst. Appl. Samuel Yanes Luis, Nicola Basilico, Michele Antonazzi, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2024	J	`jnl`	Decoupling Patrolling Tasks for Water Quality Monitoring: A Multi-Agent Deep Reinforcement Learning Approach. IEEE Access Dame Seck Diop, Samuel Yanes Luis, Manuel A. Perales-Esteve, Sergio L. Toral Marín, Daniel Gutiérrez-Reina
2024	J	`jnl`	Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring. CoRR Samuel Yanes Luis, Dmitriy Shutin, Juan Marchal Gómez, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2024	J	`jnl`	Deep Reinforcement Multiagent Learning Framework for Information Gathering with Local Gaussian Processes for Water Monitoring. Adv. Intell. Syst. Samuel Yanes Luis, Dmitriy Shutin, Juan Marchal Gómez, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2024	—	`conf`	Deep Variational Auto-Encoder for Model-Based Water Quality Patrolling with Intelligent Surface Vehicles. CAEPIA Samuel Yanes Luis, Nicola Basilico, Michele Antonazzi, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2024	J	`jnl`	Informative Deep Reinforcement Path Planning for Heterogeneous Autonomous Surface Vehicles in Large Water Resources. IEEE Access Alejandro Mendoza Barrionuevo, Samuel Yanes Luis, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2024	J	`jnl`	Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning. CoRR Alejandro Mendoza Barrionuevo, Samuel Yanes Luis, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2024	J	`jnl`	Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring. CoRR Luis Miguel Díaz, Samuel Yanes Luis, Alejandro Mendoza Barrionuevo, Dame Seck Diop, Manuel A. Perales-Esteve, Alejandro Casado, Sergio Toral, Daniel Gutiérrez-Reina
2023	J	`jnl`	Censored deep reinforcement patrolling with information criterion for monitoring large water resources using Autonomous Surface Vehicles. Appl. Soft Comput. Samuel Yanes Luis, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2023	—	`ch.`	Deep Reinforcement Learning Applied to Multi-agent Informative Path Planning in Environmental Missions. Mobile Robot: Motion Control and Path Planning Samuel Yanes Luis, Manuel A. Perales-Esteve, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2022	J	`jnl`	An evolutionary multi-objective path planning of a fleet of ASVs for patrolling water resources. Eng. Appl. Artif. Intell. Samuel Yanes Luis, Federico Peralta, Alejandro Tapia Córdoba, Álvaro Rodríguez del Nozal, Sergio L. Toral Marín, Daniel Gutiérrez-Reina
2022	J	`jnl`	Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources using Autonomous Surface Vehicles. CoRR Samuel Yanes Luis, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2022	—	`conf`	Maximum Information Coverage and Monitoring Path Planning with Unmanned Surface Vehicles Using Deep Reinforcement Learning. OLA Samuel Yanes Luis, Daniel Gutiérrez-Reina, Sergio Toral
2021	J	`jnl`	A Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensors. Sensors Samuel Yanes Luis, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2021	J	`jnl`	A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case. IEEE Access Samuel Yanes Luis, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2021	B	`conf`	A Sample-Efficiency Comparison Between Evolutionary Algorithms and Deep Reinforcement Learning for Path Planning in an Environmental Patrolling Mission. CEC Samuel Yanes Luis, Federico Peralta Samaniego, Daniel Gutiérrez-Reina, Sergio L. Toral Marín
2021	B	`conf`	Monitoring Water Resources through a Bayesian Optimization-based Approach using Multiple Surface Vehicles: The Ypacarai Lake Case Study. CEC Federico Peralta, Samuel Yanes Luis, Daniel Gutiérrez-Reina, Sergio Toral
2020	J	`jnl`	A Deep Reinforcement Learning Approach for the Patrolling Problem of Water Resources Through Autonomous Surface Vehicles: The Ypacarai Lake Case. IEEE Access Samuel Yanes Luis, Daniel Gutiérrez-Reina, Sergio L. Toral Marín

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.