Daniel Edward Pagendam

16 papers A* 1Journal 15

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Advancing integrated terrestrial nutrient budget modeling: Challenges and opportunities for improvement across space, time, and end-users. Ecol. Informatics Chiara Pasut, David I. Forrester, Thomas F. A. Bishop, Jacqueline R. England, Donald Gaydon, Peter Grace, Ian Harman, Sreekanth Janardhanan, Bennet Macdonald, Federico Maggi, Brendan P. Malone, Daniel Edward Pagendam, David W. Rassam, Dan F. Rosauer, Stephen H. Roxburgh, Ross D. Searle, Fiona H. M. Tang, Sabastine Ugbaje, Ying-Ping Wang, Jinyan Yang, Yingying Yu, Senani Karunaratne
2025	A*	`conf`	Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional, Black-box Systems. ICLR Daniel MacKinlay, Russell Tsuchida, Daniel Edward Pagendam, Petra Kuhnert
2024	J	`jnl`	Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional Systems. CoRR Daniel MacKinlay, Russell Tsuchida, Daniel Edward Pagendam, Petra Kuhnert
2023	J	`jnl`	Exploiting Field Dependencies for Learning on Categorical Data. CoRR Zhibin Li, Piotr Koniusz, Lu Zhang, Daniel Edward Pagendam, Peyman Moghadam
2023	J	`jnl`	Exploiting Field Dependencies for Learning on Categorical Data. IEEE Trans. Pattern Anal. Mach. Intell. Zhibin Li, Piotr Koniusz, Lu Zhang, Daniel Edward Pagendam, Peyman Moghadam
2023	J	`jnl`	The effect of biologically mediated decay rates on modelling soil carbon sequestration in agricultural settings. Environ. Model. Softw. Mohammad-Javad Davoudabadi, Daniel Edward Pagendam, Christopher C. Drovandi, Jeff Baldock, Gentry White
2022	J	`jnl`	Bayesian Neural Network Inference via Implicit Models and the Posterior Predictive Distribution. CoRR Joel Janek Dabrowski, Daniel Edward Pagendam
2022	J	`jnl`	Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires. CoRR Joel Janek Dabrowski, Daniel Edward Pagendam, James Hilton, Conrad Sanderson, Daniel MacKinlay, Carolyn Huston, Andrew Bolt, Petra Kuhnert
2021	J	`jnl`	A Semiautomatic Method for History Matching Using Sequential Monte Carlo. SIAM/ASA J. Uncertain. Quantification Christopher C. Drovandi, David J. Nott, Daniel Edward Pagendam
2021	J	`jnl`	Advanced Bayesian approaches for state-space models with a case study on soil carbon sequestration. Environ. Model. Softw. Mohammad-Javad Davoudabadi, Daniel Edward Pagendam, Christopher C. Drovandi, Jeff Baldock, Gentry White
2021	J	`jnl`	Gaussian process machine learning and Kriging for groundwater salinity interpolation. Environ. Model. Softw. Tao Cui, Daniel Edward Pagendam, Mat Gilfedder
2021	J	`jnl`	Opportunistic Emulation of Computationally Expensive Simulations via Deep Learning. CoRR Conrad Sanderson, Daniel Edward Pagendam, Brendan Power, Frederick Bennett, Ross Darnell
2020	J	`jnl`	Enforcing mean reversion in state space models for prawn pond water quality forecasting. Comput. Electron. Agric. Joel Janek Dabrowski, Ashfaqur Rahman, Daniel Edward Pagendam, Andrew George
2018	J	`jnl`	Determining the initial spatial extent of an environmental impact assessment with a probabilistic screening methodology. Environ. Model. Softw. Luk Peeters, Daniel Edward Pagendam, Russell S. Crosbie, Praveen K. Rachakonda, Warrick R. Dawes, Lei Gao, Steve P. Marvanek, Yongqiang Zhang, Tim R. McVicar
2015	J	`jnl`	Streamflow rating uncertainty: Characterisation and impacts on model calibration and performance. Environ. Model. Softw. Jorge L. Peña-Arancibia, Yongqiang Zhang, Daniel Edward Pagendam, Neil R. Viney, Julien Lerat, Albert I. J. M. van Dijk, Jai Vaze, Andrew J. Frost
2008	J	`jnl`	Conceptualisation and application of models for groundwater-surface water interactions and nitrate attenuation potential in riparian zones. Environ. Model. Softw. David W. Rassam, Daniel Edward Pagendam, H. M. Hunter

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.