Abdul Hameed Azeemi

17 papers A 2B 1Journal 9Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	Language Model-Driven Data Pruning Enables Efficient Active Learning. EACL (Findings) Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
2025	J	`jnl`	A Survey on Data Selection for Efficient Speech Processing. IEEE Access Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
2025	J	`jnl`	Scaling Truth: The Confidence Paradox in AI Fact-Checking. CoRR Ihsan Ayyub Qazi, Zohaib Khan, Abdullah Ghani, Agha Ali Raza, Zafar Ayyub Qazi, Wassay Sajjad, Ayesha Ali, Asher Javaid, Muhammad Abdullah Sohail, Abdul Hameed Azeemi
2025	B	`conf`	To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation. COLING Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
2024	—	`conf`	Deepfake Defense: Constructing and Evaluating a Specialized Urdu Deepfake Audio Dataset. ACL (Findings) Sheza Munir, Wassay Sajjad, Mukeet Raza, Emaan Abbas, Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
2024	—	`conf`	Generalists vs. Specialists: Evaluating Large Language Models for Urdu. EMNLP (Findings) Samee Arif, Abdul Hameed Azeemi, Agha Ali Raza, Awais Athar
2024	J	`jnl`	Generalists vs. Specialists: Evaluating Large Language Models for Urdu. CoRR Samee Arif, Abdul Hameed Azeemi, Agha Ali Raza, Awais Athar
2024	J	`jnl`	Language Model-Driven Data Pruning Enables Efficient Active Learning. CoRR Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
2024	J	`jnl`	The Fellowship of the LLMs: Multi-Agent Workflows for Synthetic Preference Optimization Dataset Generation. CoRR Samee Arif, Sualeha Farid, Abdul Hameed Azeemi, Awais Athar, Agha Ali Raza
2024	J	`jnl`	To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation. CoRR Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
2023	—	`conf`	Data Pruning for Efficient Model Pruning in Neural Machine Translation. EMNLP (Findings) Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
2023	A	`conf`	Self-Supervised Dataset Pruning for Efficient Training in Audio Anti-spoofing. INTERSPEECH Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
2022	A	`conf`	Dataset Pruning for Resource-constrained Spoofed Audio Detection. INTERSPEECH Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
2022	J	`jnl`	Towards Representative Subset Selection for Self-Supervised Speech Recognition. CoRR Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
2021	J	`jnl`	COVID-19 Tweets Analysis through Transformer Language Models. CoRR Abdul Hameed Azeemi, Adeel Waheed
2021	—	`conf`	RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds. AALTD@ECML/PKDD Abdul Hameed Azeemi, Muhammad Hamza Sohail, Talha Zubair, Muaz Maqbool, Irfan Younas, Omair Shafiq
2021	J	`jnl`	RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds. CoRR Abdul Hameed Azeemi, Muhammad Hamza Sohail, Talha Zubair, Muaz Maqbool, Irfan Younas, Omair Shafiq

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.