Machine Learning Engineer & Published AI Researcher (IEEE)
I work at the intersection of rigorous research and production ML — from Graph Neural Networks for drug discovery to multimodal sentiment systems. Published at IEEE ASET 2025. Co-founder of AI-Talents (33 mentees). Technical Program Chair, IndabaX Egypt 2026.
Research-first, impact-driven, cross-cultural.
I am a Machine Learning Engineer and published AI researcher whose work is driven by one idea: bridge rigorous research with real-world impact. My trajectory moved from Android and freelancing, through a Master's at the University of Ottawa, into production ML and IEEE-level research.
I hold an M.Eng in Data Science & AI from the University of Ottawa (A+, full DEBI scholarship) and a B.Sc. in Computer Science & Math from Helwan University (2nd in class). Over 2+ years I have specialized in deep learning, NLP, multimodal AI and generative systems, with production deployment experience.
My research on Predicting Drug-Drug Interactions using Graph Neural Networks was accepted and presented at IEEE ASET 2025. I also completed the Deep Dive into Blockchain Summer School at UZH, expanding my perspective on secure, decentralized systems.
I am actively seeking a PhD position where I can contribute to healthcare AI, multimodal learning, GNNs, or trustworthy ML.
From concept to peer-reviewed IEEE publication — proven end-to-end research capability.
Preprocessing to deployment and monitoring. Spoken Language ID system with 90%+ accuracy in production.
Co-founded AI-Talents (33 mentees). Technical Program Chair at IndabaX Egypt 2026.
Egypt, Canada, Germany, Switzerland, UAE. Adaptable, open-minded, detail-oriented.
Not a projects gallery. These are the systems I stand behind — research-grade or production-ready, each with a clear reason to exist.
A GNN trained on DrugBank that flags risky drug pairings before clinicians prescribe them — published paper, peer-reviewed, demo-backed.
Three-stream fusion (text · audio · video) that reads sentiment and emotion across English and Arabic video input — in real time.
A CNN that triages MRI slices for tumor presence with clinician-readable confidence — deployed as a Gradio space for non-technical validation.
A five-stage dense+sparse retrieval pipeline that turns 2M unstructured logs into grounded incident reports.
Below the flagships: five applied systems that round out the engineering breadth — agentic AI, distributed systems, AI speech, biometrics, security.
Enterprise agentic workflow with CRM integration, automated support, and operational dashboards.
Three-phase AI framework for fragmentation (Random Forest 97.88%), replication (Q-Learning), and anomaly detection (LSTM autoencoder).
Production-deployed language-ID pipeline at AIM Technologies — 90%+ accuracy across the full ML lifecycle.
Fused face + voice + fingerprint signals for robust identity verification under noise and spoofing.
GAN-based generative modeling for anomaly-resistant intrusion detection on imbalanced attack distributions.
Peer-reviewed research.
Proceedings of the 7th HCT ASET Conference · Abu Dhabi, UAE · 2025 · IEEE Xplore
My contribution: GNN architecture design, DrugBank data pipeline, benchmarking vs. baselines, co-authored writing.
Where research meets impact.
Lead the technical program for Egypt's largest AI/ML community conference. Curate the workshop track, recruit speakers and mentors, and shape the scientific direction.
Co-founded a mindset-first EdTech startup training the next generation of AI engineers across MENA. Architected the full curriculum. 33 active mentees.
Shipped production Spoken Language Identification with 90%+ accuracy — full ML lifecycle from preprocessing through deployment and monitoring.
Researched GANs for synthetic data generation. Contributed to ML pipeline design alongside senior researchers.
Where I want to go deeper in a PhD.
ML for drug discovery, clinical decision support, and Arabic medical NLP. Published IEEE work in drug-drug interactions.
Molecular property prediction, relational reasoning, dynamic graphs, scalability of GNNs on real-world datasets.
Fusion of text, audio, and visual signals. Cross-lingual Arabic / English sentiment and emotion recognition.
Hybrid dense-sparse retrieval, knowledge-grounded generation, evaluation of RAG under distribution shift.
Robustness, anomaly detection, interpretability — so ML systems can be deployed in healthcare and critical settings with confidence.
Research, PhD, or applied AI — I'd be glad to connect.
Whether you want to discuss a PhD opportunity, propose a research collaboration, or scope an applied AI engagement — I'd be glad to connect. For consulting, research discussion, or PhD opportunities, email me directly or book a short call.
Research collaboration, PhD discussion, consulting — same person, different tracks.
I work with both research teams and applied clients. My work sits between rigorous experimentation and production-ready AI systems — so I can contribute to research-oriented collaboration, consulting engagements, or full ML system development.
Literature reviews, experiment design, baseline development, benchmarking, and model prototyping across multimodal AI, GNNs, RAG, and trustworthy ML.
Open to PhD opportunities, research assistantships, and interdisciplinary collaborations where machine learning can contribute to meaningful scientific or societal impact.
Hands-on curriculum design, technical workshops, and mentoring for students, teams, and early-career AI practitioners.
Production-minded ML systems — from preprocessing and model training to deployment, monitoring, and iteration.
Retrieval-augmented generation, tool-calling agents, and LLM-powered workflow automation for support, analytics, and internal knowledge systems.
Exploratory analysis, modeling, reporting, and research-to-product translation for organizations that need both technical depth and practical delivery.
Email me directly or book a short call — choose Research / PhD or Consulting as the meeting type.
Growing the AI ecosystem in MENA and beyond.
Technical Program Chair
Published Author & Presenter
Attendee
Research to production.
Pick a meeting type, then choose a time on the calendar below.