Emad Mahmodi

I am a software engineer and applied-ML researcher with 10+ years spanning software engineering, data science, high-performance computing, and security. My work turns hard, structured data — binaries, graphs, and network traffic — into working machine learning and systems, and I have three peer-reviewed ML publications in anomaly detection and recommender systems. My applied background runs deep in automotive embedded systems and reverse engineering (firmware analysis on TriCore and PowerPC, binary de-obfuscation, and LLM/RAG-assisted diagnostics).

My research interests span AI in Cybersecurity (leveraging LLMs and RAG for automated binary analysis), Binary Reverse Engineering of automotive ECUs, Data Science & Anomaly Detection for high-frequency data streams, and Systems Security including anti-debugging and hardware-level vulnerability research, and Recommendation system .

At Negar-Khodro, I architect ECU programming platforms, lead reverse engineering efforts for TriCore and PowerPC architectures, and develop tools like Negaremap — a native Persian remapping platform with automated table identification and checksum correction. I have also contributed to high-performance computing projects, porting AI inference models to edge devices, and conducted vulnerability research at NIUniverse.

I hold an M.Sc. in Computer Science (Software Engineering) from Ferdowsi University of Mashhad, where my thesis focused on adaptive phishing detection against URL obfuscation. My work has been published in Expert Systems with Applications (Elsevier), Electronics (MDPI), and the International Journal of Engineering, with further papers currently under review at Future Generation Computer Systems (Elsevier).