AI/ML Engineer · Research Scientist
Dr. Ahmed Moussa
Machine Learning for Risk and Resilience in Complex, Interdependent Systems
Sharpened by Optimization and Graph Theory. Built to run in Production!
Proven Across
Climate · Infrastructure · Logistics · Supply Chains · Project & Asset Management · Safety in Project Delivery
What I Build
Production AI — from research to deployment
Every system here was built under one constraint: it has to work when it matters.
Applied ML Systems
Early warning that converts into action
Deep learning on geospatial time series to forecast flood and wildfire hazard, and to predict accident risk in drone-based hazmat logistics deployed on the cloud infrastructure.
NSE 89% flood forecasting · 91% wildfire accuracy · days of lead time before impact
Agentic AI Systems
Multi-step work completed autonomously
Autonomous research agents, hierarchical coordination pipelines, and retrieval-grounded assistants. These finish tasks end to end rather than answering one question at a time.
7 coordinated agents · multi-round research · hours of desk work compressed into one run
Production ML & MLOps
Models that keep working after launch
Drift detection that triggers automatic retraining, CI/CD that ships to containers, and orchestrated pipelines on Vertex AI. This is the production part of ML systems.
40% faster deployment cycles · automated retraining · fewer engineering hours on maintenance
Research & Open-Source Tooling
Methods and code that survive peer review
Peer-reviewed work on graph-theoretic risk modelling and optimization-augmented ML, plus three open-source Python packages in active use by practitioners worldwide.
5+ peer-reviewed publications · 2,000+ package downloads · methods validated externally
Featured Work
Flood hazard forecasting from rainfall alone — fast enough to act on
One build, in full — the operational gap, the architecture that closed it, and what it delivers in production.
Urban Flood Forecasting
Conventional flood models need soil moisture readings, calibrated river gauges, and physics-based solvers — inputs that take hours to assemble and often aren’t available when a storm is arriving. This system predicts flood extent and depth across Calgary’s Bow and Elbow watersheds from rainfall data alone. A CNN encodes spatial rainfall patterns against topography, an LSTM models temporal accumulation, and a custom synchronization layer resolves the lag between storm and inundation — the decision that drives the accuracy. The whole system runs as an automated TFX pipeline on GCP Vertex AI, retraining on new data and promoting a model only when it beats the current champion.
Why It Matters
Calgary’s 2013 flood displaced more than 100,000 residents and caused close to $5 billion in damages. Removing the data burden makes prediction possible the moment a rainfall forecast lands — and running the same model against climate projections to 2100 turns it into an instrument for capital planning, not just emergency response.
By The Numbers
What the work has produced
Measured against baselines, delivery cycles, and funded outcomes — not against activity.
Let's Work Together
Let's build something that matters!
I take on applied AI and machine learning engagements — from framing the problem and building the model, through deploying it as a system your team can run without me. If you have a decision that depends on predicting risk in a complex system, I’d like to hear about it.