Gavneet Singh Chadha

20 papers C 5Journal 9Unranked 5

Year	Rank	Type	Title / Venue / Authors
2024	—	`—`	Deep Neural Network Architectures for Prognostics and Health Management of Industrial Systems Gavneet Singh Chadha
2023	J	`jnl`	Gradient Monitored Reinforcement Learning. IEEE Trans. Neural Networks Learn. Syst. Mohammed Sharafath Abdul Hameed, Gavneet Singh Chadha, Andreas Schwung, Steven X. Ding
2022	J	`jnl`	Contrastive learning based self-supervised time-series analysis. Appl. Soft Comput. Johannes Pöppelbaum, Gavneet Singh Chadha, Andreas Schwung
2022	J	`jnl`	Shared Temporal Attention Transformer for Remaining Useful Lifetime Estimation. IEEE Access Gavneet Singh Chadha, Sayed Rafay Bin Shah, Andreas Schwung, Steven X. Ding
2021	J	`jnl`	A Sequence-to-Sequence Approach for Remaining Useful Lifetime Estimation Using Attention-augmented Bidirectional LSTM. Intell. Syst. Appl. Sayed Rafay Bin Shah, Gavneet Singh Chadha, Andreas Schwung, Steven X. Ding
2021	J	`jnl`	Deep Convolutional Clustering-Based Time Series Anomaly Detection. Sensors Gavneet Singh Chadha, Intekhab Islam, Andreas Schwung, Steven X. Ding
2021	C	`conf`	Generalized Dilation Structures in Convolutional Neural Networks. ICPRAM Gavneet Singh Chadha, Jan Niclas Reimann, Andreas Schwung
2021	J	`jnl`	Generalized dilation convolutional neural networks for remaining useful lifetime estimation. Neurocomputing Gavneet Singh Chadha, Utkarsh Panara, Andreas Schwung, Steven X. Ding
2020	J	`jnl`	Gradient Monitored Reinforcement Learning. CoRR Mohammed Sharafath Abdul Hameed, Gavneet Singh Chadha, Andreas Schwung, Steven X. Ding
2020	—	`conf`	Permutation Learning in Convolutional Neural Networks for Time-Series Analysis. ICANN (1) Gavneet Singh Chadha, Jinwoo Kim, Andreas Schwung, Steven X. Ding
2020	C	`conf`	Remaining Useful Lifetime Estimation with Sobolev Training. ETFA Gavneet Singh Chadha, S. M. Nazmus Sakeib, Andreas Schwung
2019	C	`conf`	Comparison of Semi-supervised Deep Neural Networks for Anomaly Detection in Industrial Processes. INDIN Gavneet Singh Chadha, Arfyan Rabbani, Andreas Schwung
2019	J	`jnl`	Generalized Dilation Neural Networks. CoRR Gavneet Singh Chadha, Jan Niclas Reimann, Andreas Schwung
2019	J	`jnl`	Learning the Non-linearity in Convolutional Neural Networks. CoRR Gavneet Singh Chadha, Andreas Schwung
2019	—	`conf`	Regularizing Neural Networks with Gradient Monitoring. INNSBDDL Gavneet Singh Chadha, Elnaz Meydani, Andreas Schwung
2019	C	`conf`	Time Series based Fault Detection in Industrial Processes using Convolutional Neural Networks. IECON Gavneet Singh Chadha, Monica Krishnamoorthy, Andreas Schwung
2019	—	`conf`	Traffic Sign Detection Using R-CNN. INNSBDDL Philipp Rehlaender, Maik Schroeer, Gavneet Singh Chadha, Andreas Schwung
2018	—	`conf`	Integrated IPC for data-driven fault detection. ICPS Fabian Westbrink, Gavneet Singh Chadha, Andreas Schwung
2018	—	`conf`	Optimal dosing of bulk material using mass-flow estimation and DEM simulation. ICIT Gavneet Singh Chadha, Fabian Westbrink, Thomas Schütte, Andreas Schwung
2017	C	`conf`	Comparison of deep neural network architectures for fault detection in Tennessee Eastman process. ETFA Gavneet Singh Chadha, Andreas Schwung

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.