Daniel Vasata - RankMe

21 papers B 2Misc 1Journal 11Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Advanced similarity metrics for IP flow data analytics. Comput. Networks Ivo Petr, Jitka Hrabáková, Magda Friedjungová, Daniel Vasata, Karel Hynek
2025	J	`jnl`	Drift-Based Dataset Stability Benchmark. CoRR Dominik Soukup, Richard Plný, Daniel Vasata, Tomás Cejka
2025	—	`conf`	Towards Real-Time Machine Learning Approximations of Ac Optimal Power Flow. CAI Marek Miltner, Daniel Vasata, Magda Friedjungová, Nishant Gadde, Ondrej Stogl, Oldrich Starý, Ram Rajagopal
2024	—	`conf`	Investigation into the Training Dynamics of Learned Optimizers. ICAART (2) Jan Sobotka, Petr Simánek, Daniel Vasata
2024	—	`conf`	Predicting Blood Glucose Levels with LMU Recurrent Neural Networks: A Novel Computational Model. AIME (1) Ladislav Floris, Daniel Vasata
2024	J	`jnl`	Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas. CoRR Marek Miltner, Jakub Zíka, Daniel Vasata, Artem Bryksa, Magda Friedjungová, Ondrej Stogl, Ram Rajagopal, Oldrich Starý
2023	J	`jnl`	Investigation into the Training Dynamics of Learned Optimizers. CoRR Jan Sobotka, Petr Simánek, Daniel Vasata
2023	Misc	`conf`	Machine Learning Metrics for Network Datasets Evaluation. SEC Dominik Soukup, Daniel Uhrícek, Daniel Vasata, Tomás Cejka
2022	J	`jnl`	Evacuation trials from a double-deck electric train unit: Experimental data and sensitivity analysis. CoRR Hana Najmanová, Veronika Pesková, Lukás Kuklík, Marek Bukácek, Pavel Hrabák, Daniel Vasata
2022	B	`conf`	Learning to Optimize with Dynamic Mode Decomposition. IJCNN Petr Simánek, Daniel Vasata, Pavel Kordík
2022	J	`jnl`	Learning to Optimize with Dynamic Mode Decomposition. CoRR Petr Simánek, Daniel Vasata, Pavel Kordík
2021	—	`conf`	Image Inpainting Using Wasserstein Generative Adversarial Imputation Network. ICANN (2) Daniel Vasata, Tomás Halama, Magda Friedjungová
2021	J	`jnl`	Image Inpainting Using Wasserstein Generative Adversarial Imputation Network. CoRR Daniel Vasata, Tomás Halama, Magda Friedjungová
2021	—	`conf`	Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network. MetaDL@AAAI Tomás Chobola, Daniel Vasata, Pavel Kordík
2021	J	`jnl`	Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network. CoRR Tomás Chobola, Daniel Vasata, Pavel Kordík
2020	—	`conf`	Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network. ICCS (4) Magda Friedjungová, Daniel Vasata, Maksym Balatsko, Marcel Jirina
2020	J	`jnl`	Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network. CoRR Magda Friedjungová, Daniel Vasata, Maksym Balatsko, Marcel Jirina
2020	B	`conf`	Unsupervised Latent Space Translation Network. ESANN Magda Friedjungová, Daniel Vasata, Tomás Chobola, Marcel Jirina
2020	J	`jnl`	Unsupervised Latent Space Translation Network. CoRR Magda Friedjungová, Daniel Vasata, Tomás Chobola, Marcel Jirina
2019	—	`conf`	Missing Features Reconstruction and Its Impact on Classification Accuracy. ICCS (3) Magda Friedjungová, Marcel Jirina, Daniel Vasata
2019	J	`jnl`	Missing Features Reconstruction and Its Impact on Classification Accuracy. CoRR Magda Friedjungová, Daniel Vasata, Marcel Jirina

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.