Gareth John Conduit

17 papers Journal 17

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Comparing massively-multitask regression algorithms for drug discovery. J. Comput. Aided Mol. Des. Eric J. Martin, Xiang-wei Zhu, Patrick Riley, Steven Kearnes, Ekaterina A. Sosnina, Li Tian, Chi-Ming Che, Zijian Wang, Ying Wei, Thomas M. Whitehead, Gareth John Conduit, Matthew D. Segall
2026	J	`jnl`	Talk Freely, Execute Strictly: Schema-Gated Agentic AI for Flexible and Reproducible Scientific Workflows. CoRR Joel Strickland, Arjun Vijeta, Chris Moores, Oliwia Bodek, Bogdan Nenchev, Thomas M. Whitehead, Charles Phillips, Karl Tassenberg, Gareth John Conduit, Ben Pellegrini
2024	J	`jnl`	A compact yet flexible design space for large-scale nonperiodic 3D woven composites based on a weighted game for generating candidate tow architectures. Comput. Aided Des. Zhenpei Wang, Brian N. Cox, Shemuel Joash Kuehsamy, Mark Hyunpong Jhon, Olivier Sudre, N. Sridhar, Gareth John Conduit
2024	J	`jnl`	Artificial intelligence (AI) futures: India-UK collaborations emerging from the 4th Royal Society Yusuf Hamied workshop. Int. J. Inf. Manag. Yogesh K. Dwivedi, Laurie Hughes, Harshad K. D. H. Bhadeshia, Sophia Ananiadou, Anthony G. Cohn, Jacqueline M. Cole, Gareth John Conduit, Maunendra Sankar Desarkar, Xinwei Wang
2024	J	`jnl`	Modelling the nicotine pharmacokinetic profile for e-cigarettes using real time monitoring of consumers' physiological measurements and mouth level exposure. BioData Min. Krishna Prasad, Allen Griffiths, Kavya Agrawal, Michael McEwan, Flavio Macci, Marco Ghisoni, Matthew Stopher, Matthew Napleton, Joel Strickland, David Keating, Thomas M. Whitehead, Gareth John Conduit, Stacey Murray, Lauren Edward
2024	J	`jnl`	Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling. J. Chem. Inf. Model. Thomas M. Whitehead, Joel Strickland, Gareth John Conduit, Alexandre Borrel, Daniel Mucs, Irene Baskerville-Abraham
2023	J	`jnl`	Probabilistic selection and design of concrete using machine learning. CoRR Jessica C. Forsdyke, Bahdan Zviazhynski, Janet M. Lees, Gareth John Conduit
2023	J	`jnl`	Unveil the unseen: Exploit information hidden in noise. Appl. Intell. Bahdan Zviazhynski, Gareth John Conduit
2022	J	`jnl`	Unveil the unseen: Exploit information hidden in noise. CoRR Bahdan Zviazhynski, Gareth John Conduit
2021	J	`jnl`	Imputation of sensory properties using deep learning. J. Comput. Aided Mol. Des. Samar Mahmoud, Benedict W. J. Irwin, Dmitriy Chekmarev, Shyam Vyas, Jeff Kattas, Thomas M. Whitehead, Tamsin Mansley, Jack Bikker, Gareth John Conduit, Matthew D. Segall
2020	J	`jnl`	Machine learning to predict mesenchymal stem cell efficacy for cartilage repair. PLoS Comput. Biol. Yu Yang Fredrik Liu, Yin Lu, Steve Oh, Gareth John Conduit
2020	J	`jnl`	Practical Applications of Deep Learning To Impute Heterogeneous Drug Discovery Data. J. Chem. Inf. Model. Benedict W. J. Irwin, Julian R. Levell, Thomas M. Whitehead, Matthew D. Segall, Gareth John Conduit
2020	J	`jnl`	Predicting the state of charge and health of batteries using data-driven machine learning. Nat. Mach. Intell. Man-Fai Ng, Jin Zhao, Qingyu Yan, Gareth John Conduit, Zhi Wei Seh
2019	J	`jnl`	Fragment Graphical Variational AutoEncoding for Screening Molecules with Small Data. CoRR John Armitage, Leszek J. Spalek, Malgorzata Nguyen, Mark Nikolka, Ian E. Jacobs, Lorena Marañón, Iyad Nasrallah, Guillaume Schweicher, Ivan Dimov, Dimitrios Simatos, Iain McCulloch, Christian B. Nielsen, Gareth John Conduit, Henning Sirringhaus
2019	J	`jnl`	Imputation of Assay Bioactivity Data Using Deep Learning. J. Chem. Inf. Model. Thomas M. Whitehead, Benedict W. J. Irwin, Peter A. Hunt, Matthew D. Segall, Gareth John Conduit
2018	J	`jnl`	Design of a nickel-base superalloy using a neural network. CoRR Bryce D. Conduit, Nick G. Jones, Howard J. Stone, Gareth John Conduit
2018	J	`jnl`	Probabilistic design of a molybdenum-base alloy using a neural network. CoRR Bryce D. Conduit, Nick G. Jones, Howard J. Stone, Gareth John Conduit

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.