Saikat Majumdar

12 papers Misc 1Journal 8Unranked 3

Year	Rank	Type	Title / Venue / Authors
2024	—	`conf`	Voltage Noise-Based Adversarial Attacks on Machine Learning Inference in Multi-Tenant FPGA Accelerators. HOST Saikat Majumdar, Radu Teodorescu
2023	J	`jnl`	A new switch-diode cell-based single-phase cascaded multilevel inverter. Soft Comput. Bidyut Mahato, Saikat Majumdar, Kartick Chandra Jana, Ebrahim Babaei, Dusmanta Kumar Mohanta
2022	—	`conf`	Characterizing Side-Channel Leakage of DNN Classifiers though Performance Counters. HOST Saikat Majumdar, Mohammad Hossein Samavatian, Radu Teodorescu
2022	J	`jnl`	DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning. CoRR Mohammad Hossein Samavatian, Saikat Majumdar, Kristin Barber, Radu Teodorescu
2021	J	`jnl`	HASI: Hardware-Accelerated Stochastic Inference, A Defense Against Adversarial Machine Learning Attacks. CoRR Mohammad Hossein Samavatian, Saikat Majumdar, Kristin Barber, Radu Teodorescu
2021	J	`jnl`	Using Undervolting as an On-Device Defense Against Adversarial Machine Learning Attacks. CoRR Saikat Majumdar, Mohammad Hossein Samavatian, Kristin Barber, Radu Teodorescu
2021	—	`conf`	Using Undervolting as an on-Device Defense Against Adversarial Machine Learning Attacks. HOST Saikat Majumdar, Mohammad Hossein Samavatian, Kristin Barber, Radu Teodorescu
2020	J	`jnl`	A New and Generalized MLI with Overall Lesser Power Electronic Devices. J. Circuits Syst. Comput. Bidyut Mahato, Saikat Majumdar, Kartick Chandra Jana
2020	J	`jnl`	Implementation of an Optimum Reduced Components Multicell Multilevel Inverter (MC-MLI) for Lower Standing Voltage. IEEE Trans. Ind. Electron. Saikat Majumdar, Bidyut Mahato, Kartick Chandra Jana
2020	J	`jnl`	Reduction of Power Electronic Devices in a Single-Phase Generalized Multilevel Inverter. J. Circuits Syst. Comput. Bidyut Mahato, Saikat Majumdar, Kartick Chandra Jana
2019	Misc	`conf`	Adaptive parallel execution of deep neural networks on heterogeneous edge devices. SEC Li Zhou, Mohammad Hossein Samavatian, Anys Bacha, Saikat Majumdar, Radu Teodorescu
2019	J	`jnl`	Optimum Structure-Based Multi-level Inverter with Doubling Circuit Configuration. J. Circuits Syst. Comput. Saikat Majumdar, Bidyut Mahato, Kartick Chandra Jana

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.