Ayah Mohsen
Software Engineer · Edge AI for Structural Health Monitoring
Bridging civil engineering and AI to build infrastructure that protects lives.
📍 Cairo, Egypt
Software Engineer · Edge AI for Structural Health Monitoring
Bridging civil engineering and AI to build infrastructure that protects lives.
📍 Cairo, Egypt
I'm a Software Engineer at Sheriax Solutions and a civil engineering graduate of Ain Shams University (B.Sc. + Postgraduate Diploma in Structural Engineering). I build production mobile applications in Flutter and Firebase — currently contributing to the TNTJ Blood Donors app (10,000+ users on Google Play) and the Kizu AI financial wellness platform.
My research direction combines both worlds: edge-based AI for Structural Health Monitoring — developing lightweight anomaly-detection systems that can run on IoT sensors to watch over bridges, pipelines, and storage tanks in real time.
I'm currently applying to the University of Ulsan for a Master's through the Global Korea Scholarship, and studying Korean alongside it.
I grew up admiring my father, a civil engineer who built his career through ambition and generosity. I followed his path into Ain Shams University's Faculty of Engineering — ranked 267th globally in Engineering and Technology (QS 2025). During my first year, my father passed away in a car accident, and my family relocated from Saudi Arabia to Egypt. My undergraduate grades reflect that period more than they reflect my capacity. I finished the degree, then pursued a Postgraduate Diploma in Structural Engineering to prove what I could do under stable conditions — GPA 3.19 overall, 4.0 on the graduation project.
During that project, my professor introduced me to Building Information Modeling (BIM). It was my first glimpse of how software could enhance structural safety — and it set the direction for everything that followed.
In 2024 I was accepted into the Digital Egypt Pioneers Initiative (DEPI) for mobile development. Six months later I joined Sheriax Solutions as a Software Engineer, where I've completed 850 hours of distinction-grade certifications (91, 93.5, and 94.5 out of 100) in Mobile Development, Applied Software Engineering, and Research & Technical Documentation. I now ship features to real users on production systems.
The bridge between my two careers came from an unexpected place — a YouTube video on the 1994 Seongsu Bridge collapse in Seoul. Researching how Korea rebuilt its infrastructure culture led me to Structural Health Monitoring (SHM): using IoT sensors, edge computing, and machine learning to watch structures continuously and catch failure before it happens. That is what I want to study at University of Ulsan, and what I'm building toward with every commit, every paper I read, and every Korean character I memorise.
Edge AI for Structural Health Monitoring
Current focus. I'm working toward graduate research in edge-based anomaly detection for civil infrastructure. The idea: instead of streaming raw vibration and acoustic-emission data from hundreds of sensors to the cloud, run lightweight AI directly on the sensor — detect abnormal patterns locally, only surface the anomalies. This is bandwidth-efficient, privacy-preserving, and fast enough to warn before failure.
Why it matters. The 1994 Seongsu Bridge collapse and the 1995 Sampoong Department Store disaster reshaped Korea's infrastructure culture. I want to be part of the next chapter: AI systems that make those lessons permanent.
Fourier, wavelets, envelope analysis for vibration and AE signals.
Statistical (Mahalanobis, T²), classical ML (Isolation Forest, OC-SVM), deep (autoencoders, Deep SVDD).
Quantization, pruning, TensorFlow Lite, ONNX.
Vibration + acoustic emission for earlier fault detection.
What I'm reading. Papers from Prof. Kim Jong Myon (University of Ulsan, AI & Computer Engineering) on intelligent fault diagnosis and lightweight deep learning for industrial signals — especially his recent work on acoustic-emission-based bearing diagnostics.
edge-shm-anomaly-detection — open source, MIT. Walks through signal processing → feature extraction → anomaly detection → edge deployment on a public bearing-fault dataset.
Technologies and tools I work with daily
Flutter3 apps
Firebaselive
Python · SHMresearchSoftware Developer
Selected work I'm proud of
AI Financial Recovery Platform · Built at Sheriax Solutions
Kizu (meaning “wound” in Japanese) is an AI-powered platform that helps users heal their financial health. It analyzes spending patterns to detect hidden money leaks — forgotten subscriptions, sneaky fees, inefficient spending habits — and generates personalized “treatment plans” to recover lost funds.
Lightweight AI for Structural Health Monitoring
Lightweight AI pipeline for detecting structural anomalies in vibration and acoustic-emission signals, optimised for deployment on resource-constrained edge devices. Walks through signal processing, feature extraction, and multi-model anomaly detection (statistical, classical ML, deep learning) on public SHM benchmark datasets.
Blood Donation App · Built for TNTJ Medical Wing
A humanitarian blood donation app that connects donors with patients across Tamil Nadu and beyond. Built for the TNTJ Medical Wing, the app serves as a critical bridge between people in need of blood and registered donors, operating under the principle “If anyone saved a life, it would be as if he saved the life of the whole humanity.”
Degrees and continuing education
Ain Shams University
GPA 3.19 · Project GPA 4.0
Ain Shams University
Khaled International Schools, Riyadh
GPA 4.0 · 98.72%
Professional achievements and validated skills
Sheriax Solutions
Sheriax Solutions
Sheriax Solutions
DEPI / MCIT, Egypt
Native
Professional proficiency · IELTS 8.0
Elementary · TOPIK Level 1 (studying toward Level 2)
Open to opportunities, collaborations, and conversations.