Experience

Experience

Building machine learning systems for complex, high-stakes environments

2025 – Present · Full-time

Current

Postdoctoral Fellow

McMaster University · Faculty of Engineering

This is where the threads of my PhD come together. I lead research on predictive intelligence for risk and resilience in complex systems, combining machine learning with network analysis, optimization, and digital twins to anticipate how failures spread — from wildfire and flood hazards to cascading risks across interdependent infrastructure. Increasingly, that work extends to language models that can reason over these risk networks.

2022 – Present · Part-time

Current

Research Engineer

McMaster University · DeGroote School of Business, Smart Freight Centre

Alongside my PhD, I took the same risk-prediction thinking into the air. At the Smart Freight Centre, I build real-time models that predict accident risk for drones carrying hazardous materials — work that now supports larger research programs in AI-driven logistics and Advanced Air Mobility.

2023 – 2025 · Part-time

Deputy Project Manager

WSP Canada Inc. · NSERC CREATE Placement

An industry placement let me see risk from the other side of the desk. Leading infrastructure project teams at WSP showed me how data-driven risk management changes outcomes on real budgets and real schedules — a perspective that still shapes the systems I build.

2020 – 2025 · Full-time

Doctoral Researcher

McMaster University · Faculty of Engineering

This is where the core idea took shape: risks in complex systems rarely act alone — they interact and cascade. I built machine learning and graph-based models to predict those interactions, moved them into production pipelines on Google Cloud, and shared the results through peer-reviewed research.

2019 – 2020 · Full-time

Researcher & Teaching Assistant

The German University in Cairo (GUC)

After graduating, I stayed at GUC to teach — and started asking whether machine learning could change how we manage infrastructure. That question became the foundation of my PhD.

2018 · Full-time

Bachelor Thesis Researcher

University of Stuttgart · ILEK Institute, Germany

A six-month research placement in Stuttgart was my first serious encounter with computational modeling. Building a C#/.NET simulation tool to analyze how complex systems respond shifted the direction of everything that followed.

Technical Deep Dive

Tools and methods across these roles

01

Machine Learning & Deep Learning

Designing and training predictive models

Frameworks

Python PyTorch TensorFlow / Keras PyTorch Lightning Scikit-learn XGBoost LightGBM CatBoost

Architectures

CNN LSTM ConvLSTM Transformers
02

Generative AI & Agents

Grounded assistants and multi-agent systems

Frameworks

OpenAI SDK CrewAI Gradio

Retrieval

RAG FAISS BM25 HyDE Reciprocal Rank Fusion GraphRAG
03

MLOps & Deployment

Taking models to production — and keeping them there

Pipelines

TFX Kubeflow Apache Airflow MLflow

Serving & Monitoring

FastAPI TF Serving Docker Kubernetes Docker Compose Minikube Evidently AI

CI/CD

Git GitHub Actions GitLab CI pytest
04

Cloud, Data & Analytics

Cloud data workflows and decision-ready insight

Cloud

GCP Vertex AI BigQuery

Data

SQL pandas R

Visualization

Tableau Power BI Plotly Seaborn
05

Complex Systems & Optimization

Modeling interdependence and optimizing decisions

Networks

Graph Theory Complex Network Analytics (CNA) NetworkX Digital Twins

Optimization

Particle Swarm Genetic Algorithms NSGA-II Pattern Search Simulated Annealing Tabu Search Differential Evolution
06

Geospatial & Risk Analytics

Where the models meet real-world hazard

Geospatial

ArcGIS QGIS Shapely Fiona OpenCV Satellite Imagery

Risk

Systemic Risk Modeling Hazard Forecasting Predictive Risk Modeling

Results & Metrics

What these roles delivered

Engineering Impact

Model Performance

~38%

Better Wildfire Prediction

Improvement over baseline models in predicting wildfire hazard from geospatial time-series data.

MLOps

~40%

Faster Deployment

Through reproducible, production-grade TFX pipelines on Google Cloud.

Optimization

~90%

Training Efficiency Gain

Through optimization-based hyperparameter tuning and parallelized search strategies.

Research & Community Impact

Funding

$14M

CAD in Research Proposals

Contributed to funding proposals for AI-driven logistics and freight research.

Publications

7+

Peer-Reviewed Journal Papers

Plus 2 conference papers, spanning machine learning, network analysis, and infrastructure risk.

Open Source

4,000+

Package Downloads

Open-source ML tools adopted by practitioners worldwide.

Education

Academic background

2025 · 12/12 (A+) GPA

Engineering Ph.D., Machine Learning for Systemic Risk Management of Complex Systems

Vanier Scholar · McMaster University, Canada

2019

Engineering B.Sc., Class Valedictorian, Highest Honors

The German University in Cairo (GUC), Egypt

2018

Engineering Bachelor Thesis

University of Stuttgart, Germany

Get in Touch

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