Farzad Eskandanian

15 papers A 4Misc 1Journal 7Unranked 3

Year	Rank	Type	Title / Venue / Authors
2024	—	`conf`	Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation. UMAP (Adjunct Publication) Kun Lin, Masoud Mansoury, Farzad Eskandanian, Milad Sabouri, Bamshad Mobasher
2024	J	`jnl`	Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation. CoRR Kun Lin, Masoud Mansoury, Farzad Eskandanian, Milad Sabouri, Bamshad Mobasher
2020	J	`jnl`	"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation. CoRR Nasim Sonboli, Robin Burke, Nicholas Mattei, Farzad Eskandanian, Tian Gao
2020	A	`conf`	Opportunistic Multi-aspect Fairness through Personalized Re-ranking. UMAP Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, Bamshad Mobasher
2020	J	`jnl`	Opportunistic Multi-aspect Fairness through Personalized Re-ranking. CoRR Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, Bamshad Mobasher
2020	A	`conf`	Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation. UMAP Farzad Eskandanian, Bamshad Mobasher
2020	J	`jnl`	Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation. CoRR Farzad Eskandanian, Bamshad Mobasher
2019	Misc	`conf`	Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models. FLAIRS Farzad Eskandanian, Bamshad Mobasher
2019	J	`jnl`	Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models. CoRR Farzad Eskandanian, Bamshad Mobasher
2019	A	`conf`	Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems. UMAP Farzad Eskandanian, Nasim Sonboli, Bamshad Mobasher
2019	J	`jnl`	Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems. CoRR Farzad Eskandanian, Nasim Sonboli, Bamshad Mobasher
2018	J	`jnl`	Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks. CoRR Farzad Eskandanian, Bamshad Mobasher
2017	A	`conf`	A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems. UMAP Farzad Eskandanian, Bamshad Mobasher, Robin Burke
2016	—	`conf`	Collaborative Recommendation of Informal Learning Experiences. WI Workshops Robin D. Burke, Farzad Eskandanian
2016	—	`conf`	User Segmentation for Controlling Recommendation Diversity. RecSys Posters Farzad Eskandanian, Bamshad Mobasher, Robin D. Burke

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.

In 2025, he co-authored Assessing the Effectiveness of the Tigress Obfuscator Against MOPSA and BinaryNinja, published at CheckMATE. The paper evaluates the effectiveness of obfuscation techniques and their impact on source-level static analysis and binary-level decompilation.

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.