Abhinav Moudgil

21 papers A* 5A 1Journal 14Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents. CoRR Alisia Maria Lupidi, Bhavul Gauri, Thomas Foster, Bassel Al Omari, Despoina Magka, Alberto Pepe, Alexis Audran-Reiss, Muna Aghamelu, Nicolas Mario Baldwin, Lucia Cipolina-Kun, Jean-Christophe Gagnon-Audet, Chee Hau Leow, Sandra Lefdal, Hossam Mossalam, Abhinav Moudgil, Saba Nazir, Emanuel Tewolde, Isabel Urrego, Jordi Armengol-Estapé, Amar Budhiraja, Gaurav Chaurasia, Abhishek Charnalia, Derek Dunfield, Karen Hambardzumyan, Daniel Izcovich, Martin Josifoski, Ishita Mediratta, Kelvin Niu, Parth Pathak, Michael Shvartsman, Edan Toledo, Anton Protopopov, Roberta Raileanu, Alexander H. Miller, Tatiana Shavrina, Jakob N. Foerster, Yoram Bachrach
2026	J	`jnl`	Celo2: Towards Learned Optimization Free Lunch. CoRR Abhinav Moudgil, Boris Knyazev, Eugene Belilovsky
2025	A*	`conf`	Accelerating Training with Neuron Interaction and Nowcasting Networks. ICLR Boris Knyazev, Abhinav Moudgil, Guillaume Lajoie, Eugene Belilovsky, Simon Lacoste-Julien
2025	J	`jnl`	Celo: Training Versatile Learned Optimizers on a Compute Diet. Trans. Mach. Learn. Res. Abhinav Moudgil, Boris Knyazev, Guillaume Lajoie, Eugene Belilovsky
2025	J	`jnl`	Learning Versatile Optimizers on a Compute Diet. CoRR Abhinav Moudgil, Boris Knyazev, Guillaume Lajoie, Eugene Belilovsky
2025	J	`jnl`	Meta-learning Optimizers for Communication-Efficient Learning. Trans. Mach. Learn. Res. Charles-Étienne Joseph, Benjamin Thérien, Abhinav Moudgil, Boris Knyazev, Eugene Belilovsky
2025	J	`jnl`	PyLO: Towards Accessible Learned Optimizers in PyTorch. CoRR Paul Janson, Benjamin Thérien, Quentin Anthony, Xiaolong Huang, Abhinav Moudgil, Eugene Belilovsky
2025	A*	`conf`	Understanding Input Selectivity in Mamba: Impact on Approximation Power, Memorization, and Associative Recall Capacity. ICML Ningyuan Teresa Huang, Miguel Sarabia, Abhinav Moudgil, Pau Rodríguez, Luca Zappella, Federico Danieli
2025	J	`jnl`	Understanding Input Selectivity in Mamba: Impact on Approximation Power, Memorization, and Associative Recall Capacity. CoRR Ningyuan Huang, Miguel Sarabia, Abhinav Moudgil, Pau Rodríguez, Luca Zappella, Federico Danieli
2024	J	`jnl`	Accelerating Training with Neuron Interaction and Nowcasting Networks. CoRR Boris Knyazev, Abhinav Moudgil, Guillaume Lajoie, Eugene Belilovsky, Simon Lacoste-Julien
2023	J	`jnl`	Can We Learn Communication-Efficient Optimizers? CoRR Charles-Étienne Joseph, Benjamin Thérien, Abhinav Moudgil, Boris Knyazev, Eugene Belilovsky
2022	A*	`conf`	Towards Scaling Difference Target Propagation by Learning Backprop Targets. ICML Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake A. Richards, Yoshua Bengio
2022	J	`jnl`	Towards Scaling Difference Target Propagation by Learning Backprop Targets. CoRR Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake A. Richards, Yoshua Bengio
2021	A*	`conf`	Contrast and Classify: Training Robust VQA Models. ICCV Yash Kant, Abhinav Moudgil, Dhruv Batra, Devi Parikh, Harsh Agrawal
2021	A*	`conf`	SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation. NeurIPS Abhinav Moudgil, Arjun Majumdar, Harsh Agrawal, Stefan Lee, Dhruv Batra
2021	J	`jnl`	SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation. CoRR Abhinav Moudgil, Arjun Majumdar, Harsh Agrawal, Stefan Lee, Dhruv Batra
2020	J	`jnl`	Contrast and Classify: Alternate Training for Robust VQA. CoRR Yash Kant, Abhinav Moudgil, Dhruv Batra, Devi Parikh, Harsh Agrawal
2020	A	`conf`	Exploring 3 R's of Long-term Tracking: Re-detection, Recovery and Reliability. WACV Shyamgopal Karthik, Abhinav Moudgil, Vineet Gandhi
2019	J	`jnl`	Exploring 3 R's of Long-term Tracking: Re-detection, Recovery and Reliability. CoRR Shyamgopal Karthik, Abhinav Moudgil, Vineet Gandhi
2018	—	`conf`	Long-Term Visual Object Tracking Benchmark. ACCV (2) Abhinav Moudgil, Vineet Gandhi
2017	J	`jnl`	Long-Term Visual Object Tracking Benchmark. CoRR Abhinav Moudgil, Vineet Gandhi

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.