Engineering AI for
I build end-to-end AI systems that transform complex scientific data into scalable, production-ready solutions. Combining artificial intelligence, machine learning, software engineering, and computational biology, I develop intelligent technologies for healthcare, agriculture, and environmental science—turning scientific challenges into practical tools that drive discovery and real-world impact.
Scientific Identity & Core Competencies
Building complete, production-ready AI systems by unifying machine learning algorithms, scalable software engineering, robust data infrastructure, and computational intelligence.
I develop scalable AI solutions from the ground up—spanning data acquisition, feature engineering, and model architecture, all the way to backend services, containerized deployment, and high-performance user interfaces.
Applied AI, Generative AI & Deep Learning
Generative AI & Diffusion Models • Graph Neural Networks • Representation Learning
Full-Stack Software & Distributed Backend Systems
High-Throughput APIs • Clean Architecture • Modern Web Interfaces
Data Engineering, Cloud Infrastructure & MLOps
Automated Ingestion • Containerized Orchestration • Model Serving
Domain Intelligence: Computational Biophysics & Bio-AI
Structural Biology & Molecular Dynamics • Peptide Toxicity • 3D Conformer Modeling
Trajectory & Career Milestones
Applied research roles, software engineering initiatives, and computational leadership milestones.
Artificial Intelligence Committee Member
- Develop AI systems using Machine Learning, Deep Learning, and Generative AI for real-world engineering applications.
- Build production-oriented software through collaborative engineering projects and structured technical training.
- Apply modern software engineering practices across the complete AI development lifecycle, from experimentation to deployment.
- Collaborate with multidisciplinary teams to transform research concepts into scalable AI solutions.
Research Cohort Member
- Selected through a competitive national research program focused on developing future interdisciplinary researchers.
- Built practical expertise in research methodology, academic writing, literature review, and evidence-based analysis.
- Designed research questions, evaluated scientific literature, and translated ideas into structured study designs.
- Prepared for future peer-reviewed publications through mentorship and collaborative research training.
Research & Community Engagement Facilitator
- Conducted participatory community research to investigate digital safety challenges affecting young people.
- Contributed to developing an evidence-based policy paper by transforming research findings into actionable recommendations.
- Designed and independently delivered a critical-thinking workshop for 25 school teachers to strengthen digital resilience.
- Collaborated with multidisciplinary teams to convert research outcomes into practical community interventions.
Google Gemini Student Ambassador
- Promoted practical adoption of Generative AI across Egyptian universities through structured outreach and technical guidance.
- Evaluated AI-generated content and designed Arabic prompting strategies for educational use cases.
- Supported more than 800 university students in effectively using Google Gemini Advanced.
- Recognized among the program’s top-performing ambassadors for outreach impact and community engagement.
Validated Competence & Certifications
Formally accredited specializations in Deep Learning, Bioinformatics, Cheminformatics, and Data Engineering.
NVIDIA DLI : Generative AI (Beginner Level)
Statistics for Genomic Data Science
Introduction to Genomic Technologies
Python for Genomic Data Science
Data Science: R Basics
Python Basics for Data Science
Data Analytics Basics
Google Gemini Student Ambassador
Galactic Problem Solver
Future M.Ds+ in STEM Scholar
GreenAura Ambassadors
Featured Research & Applied Engineering
A curated collection of my research applications, tools, and open-source contributions in computational biology, machine learning, and full-stack development.
AgroGraph-Tox AI: Multi-Task Molecular & Pollinator Toxicity Screening
Flood Intelligence AI: Sentinel Satellite & Geospatial Disaster HUD
EchoGuard: Air-Gapped Voice AI & Embedded Edge IoT Platform
BioPhys Refinement Lab
NeuroScan AI: Explainable Tumor Classification
Open-Source Portfolio Template
Open Science & Data-Centric Infrastructure
Flawed datasets produce models that look proficient on paper but fail fundamentally in substance. The widening chasm between raw experimental data and deep learning architectures demands rigorous, transparent, data-centric curation.
AgroBench-3D: Multi-Task Molecular & 3D Conformer Benchmark
Peer-Reviewed Science & Preprint Archive
Rigorous scientific investigation, multi-task representation benchmarks, and peer-reviewed preprint manuscripts currently in active synthesis.
“Great scientific breakthroughs cannot be rushed. The most enduring discoveries demand deep mathematical rigor, empirical validation, and patient craftsmanship.”
Currently synthesizing foundational preprints across Multi-Task Graph Neural Networks, Ecotoxicological Modeling, and Molecular Biophysics. Comprehensive manuscripts, reproducible open codebases, and Zenodo repositories will be unlocked here upon empirical validation.
Get in Touch & Collaborate
Direct channel for research collaborations, foundational AI system engineering, and high-impact scientific initiatives.
Location:Alexandria, Egypt (UTC+2)
Response SLA:< 24 Hours Response Time
Status:Available for Deep Tech & AI Collaborations