Omobayode Fagbohungbe

18 papers B 2C 1Journal 15

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Analog Foundation Models. CoRR Julian Büchel, Iason Chalas, Giovanni Acampa, An Chen, Omobayode Fagbohungbe, Sidney Tsai, Kaoutar El Maghraoui, Manuel Le Gallo, Abbas Rahimi, Abu Sebastian
2025	J	`jnl`	Analog In-memory Training on General Non-ideal Resistive Elements: The Impact of Response Functions. CoRR Zhaoxian Wu, Quan Xiao, Tayfun Gokmen, Omobayode Fagbohungbe, Tianyi Chen
2025	C	`conf`	Assessing the Performance of Analog Training for Transfer Learning. ISCAS Omobayode Fagbohungbe, Corey Lammie, Malte J. Rasch, Takashi Ando, Tayfun Gokmen, Vijay Narayanan
2025	J	`jnl`	Assessing the Performance of Analog Training for Transfer Learning. CoRR Omobayode Fagbohungbe, Corey Lammie, Malte J. Rasch, Takashi Ando, Tayfun Gokmen, Vijay Narayanan
2025	J	`jnl`	Assessing the Performance of Analog Training for Transfer Learning. IEEE Trans. Circuits Syst. II Express Briefs Omobayode Fagbohungbe, Corey Lammie, Malte J. Rasch, Takashi Ando, Tayfun Gokmen, Vijay Narayanan
2023	J	`jnl`	Fast offset corrected in-memory training. CoRR Malte J. Rasch, Fabio Carta, Omobayode Fagbohungbe, Tayfun Gokmen
2023	J	`jnl`	Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference. CoRR Manuel Le Gallo, Corey Lammie, Julian Büchel, Fabio Carta, Omobayode Fagbohungbe, Charles Mackin, Hsinyu Tsai, Vijay Narayanan, Abu Sebastian, Kaoutar El Maghraoui, Malte J. Rasch
2022	J	`jnl`	A Joint Energy and Latency Framework for Transfer Learning Over 5G Industrial Edge Networks. IEEE Trans. Ind. Informatics Bo Yang, Omobayode Fagbohungbe, Xuelin Cao, Chau Yuen, Lijun Qian, Dusit Niyato, Yan Zhang
2022	J	`jnl`	Effect of Batch Normalization on Noise Resistant Property of Deep Learning Models. CoRR Omobayode Fagbohungbe, Lijun Qian
2022	J	`jnl`	Efficient Privacy Preserving Edge Intelligent Computing Framework for Image Classification in IoT. IEEE Trans. Emerg. Top. Comput. Intell. Omobayode Fagbohungbe, Sheikh Rufsan Reza, Xishuang Dong, Lijun Qian
2022	B	`conf`	Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models. IJCNN Omobayode Fagbohungbe, Lijun Qian
2022	J	`jnl`	Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models. CoRR Omobayode Fagbohungbe, Lijun Qian
2022	J	`jnl`	Impact of Learning Rate on Noise Resistant Property of Deep Learning Models. CoRR Omobayode Fagbohungbe, Lijun Qian
2022	J	`jnl`	The Effect of Batch Normalization on Noise Resistant Property of Deep Learning Models. IEEE Access Omobayode Fagbohungbe, Lijun Qian
2021	J	`jnl`	A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks. CoRR Bo Yang, Omobayode Fagbohungbe, Xuelin Cao, Chau Yuen, Lijun Qian, Dusit Niyato, Yan Zhang
2021	B	`conf`	Benchmarking Inference Performance of Deep Learning Models on Analog Devices. IJCNN Omobayode Fagbohungbe, Lijun Qian
2020	J	`jnl`	Benchmarking Inference Performance of Deep Learning Models on Analog Devices. CoRR Omobayode Fagbohungbe, Lijun Qian
2020	J	`jnl`	Efficient Privacy Preserving Edge Computing Framework for Image Classification. CoRR Omobayode Fagbohungbe, Sheikh Rufsan Reza, Xishuang Dong, Lijun Qian

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.