Rajeev K. Jaiman

24 papers Journal 24

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A reward-directed diffusion framework for generative design. Eng. Appl. Artif. Intell. Hadi Keramati, Patrick Kirchen, Mohammed Hannan, Rajeev K. Jaiman
2025	J	`jnl`	A Physics-Guided Probabilistic Surrogate Modeling Framework for Digital Twins of Underwater Radiated Noise. CoRR Indu Kant Deo, Akash Venkateshwaran, Rajeev K. Jaiman
2025	J	`jnl`	A Reward-Directed Diffusion Framework for Generative Design Optimization. CoRR Hadi Keramati, Patrick Kirchen, Mohammed Hannan, Rajeev K. Jaiman
2025	J	`jnl`	Data-driven modeling of fluid flow around rotating structures with graph neural networks. CoRR Rui Gao, Zhi Cheng, Rajeev K. Jaiman
2025	J	`jnl`	HeatGen: A Guided Diffusion Framework for Multiphysics Heat Sink Design Optimization. CoRR Hadi Keramati, Morteza Sadeghi, Rajeev K. Jaiman
2025	J	`jnl`	Predicting Wave Dynamics using Deep Learning with Multistep Integration Inspired Attention and Physics-Based Loss Decomposition. CoRR Indu Kant Deo, Rajeev K. Jaiman
2024	J	`jnl`	A finite element-inspired hypergraph neural network: Application to fluid dynamics simulations. J. Comput. Phys. Rui Gao, Indu Kant Deo, Rajeev K. Jaiman
2024	J	`jnl`	Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net. CoRR Indu Kant Deo, Akash Venkateshwaran, Rajeev K. Jaiman
2024	J	`jnl`	H-SIREN: Improving implicit neural representations with hyperbolic periodic functions. CoRR Rui Gao, Rajeev K. Jaiman
2023	J	`jnl`	An interface preserving and residual-based adaptivity for phase-field modeling of fully Eulerian fluid-structure interaction. J. Comput. Phys. Biswajeet Rath, Xiaoyu Mao, Rajeev K. Jaiman
2022	J	`jnl`	Convolutional recurrent autoencoder network for learning underwater ocean acoustics. CoRR Wrik Mallik, Rajeev K. Jaiman, Jasmin Jelovica
2022	J	`jnl`	Deep convolutional neural network for shape optimization using level-set approach. CoRR Wrik Mallik, Neil Farvolden, Jasmin Jelovica, Rajeev K. Jaiman
2022	J	`jnl`	Node-Element Hypergraph Message Passing for Fluid Dynamics Simulations. CoRR Rui Gao, Indu Kant Deo, Rajeev K. Jaiman
2021	J	`jnl`	A robust and accurate finite element framework for cavitating flows with moving fluid-structure interfaces. Comput. Math. Appl. Suraj R. Kashyap, Rajeev K. Jaiman
2021	J	`jnl`	A variational interface-preserving and conservative phase-field method for the surface tension effect in two-phase flows. J. Comput. Phys. Xiaoyu Mao, Vaibhav Joshi, Rajeev K. Jaiman
2021	J	`jnl`	Kinematically consistent recurrent neural networks for learning inverse problems in wave propagation. CoRR Wrik Mallik, Rajeev K. Jaiman, Jasmin Jelovica
2020	J	`jnl`	A variational flexible multibody formulation for partitioned fluid-structure interaction: Application to bat-inspired drones and unmanned air-vehicles. Comput. Math. Appl. Vaibhav Joshi, Rajeev K. Jaiman, Carl Ollivier-Gooch
2018	J	`jnl`	A 3D common-refinement method for non-matching meshes in partitioned variational fluid-structure analysis. J. Comput. Phys. Yulong Li, Yun Zhi Law, Vaibhav Joshi, Rajeev K. Jaiman
2018	J	`jnl`	A positivity preserving and conservative variational scheme for phase-field modeling of two-phase flows. J. Comput. Phys. Vaibhav Joshi, Rajeev K. Jaiman
2018	J	`jnl`	An adaptive variational procedure for the conservative and positivity preserving Allen-Cahn phase-field model. J. Comput. Phys. Vaibhav Joshi, Rajeev K. Jaiman
2017	J	`jnl`	A positivity preserving variational method for multi-dimensional convection-diffusion-reaction equation. J. Comput. Phys. Vaibhav Joshi, Rajeev K. Jaiman
2014	J	`jnl`	A stable second-order scheme for fluid-structure interaction with strong added-mass effects. J. Comput. Phys. Jie Liu, Rajeev K. Jaiman, Pardha S. Gurugubelli
2007	J	`jnl`	Erratum to "Conservative load transfer along curved fluid-solid interface with non-matching meshes" [J. Comput. Phys. 218(1) (2006) 372-397]. J. Comput. Phys. Rajeev K. Jaiman, Xiangmin Jiao, Philippe H. Geubelle, Eric Loth
2006	J	`jnl`	Conservative load transfer along curved fluid-solid interface with non-matching meshes. J. Comput. Phys. Rajeev K. Jaiman, Xiangmin Jiao, Philippe H. Geubelle, Eric Loth

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.