Research
7+
Peer-Reviewed
Publications
~20
Journals
Reviewed
~40
Papers
Reviewed
4
Top-Tier
Venues
Vanier
Scholar
NSERC Canada
Graduate Scholarship
Gold
Medal
Governor General
of Canada · 2025
Research Themes
One core method. Two edges. Three frontiers.
The Core Method
01 · Core
Machine Learning for Risk and Resilience in Complex Systems
Proven in Infrastructure · Project & Asset Management
Predicting how risk emerges and interacts in systems whose components depend on each other — using multi-output supervised learning built for high-dimensional, non-linear interactions, and forecasting infrastructure project performance with R² of 0.97.
Two Sharpening Edges
02 · Edge
Graph-Theoretic Risk Modeling
Networks · Systemic Risk
Modeling how local disruptions escalate into system-wide failure using network analysis of interdependent risks — pinpointing where intervention does the most to contain a cascade.
03 · Edge
Optimization-Augmented Machine Learning
Metaheuristics · Decision Support
Replacing exhaustive search with metaheuristics — improving training efficiency by ~90%, carrying predictions through to multi-objective decisions, and releasing the approach as an open-source package with 4,000+ downloads.
Three Applied Frontiers
04 · Frontier
Hazard Forecasting
Climate
Forecasting flood and wildfire hazard from geospatial time series using ConvLSTM and CNN+LSTM architectures — reaching NSE of 89% on urban flood depth and ~38% better wildfire prediction than baseline models.
05 · Frontier
Freight & Air Mobility Risk
Logistics & Supply Chains · Advanced Air Mobility
Predicting accident likelihood and impact for drone-based hazmat delivery using a four-model neural-network suite on geospatial data — reaching R² of 0.95 and informing $14M CAD in proposed freight research.
06 · Frontier
EmergingLanguage Models over Risk Networks
AI Systems
Testing how reliably language models reason over cascading-failure networks — comparing chain-of-thought, structured, and GraphRAG-style prompting on multi-hop dependency graphs, and extending toward small models that call network-analysis tools, running locally.
Published Research
Peer-Reviewed Publications
Full publication list available on Google Scholar · Web of Science · ORCID
Real-Time Machine Learning Risk Prediction for Autonomous Drone Operations
A closed-loop ML architecture continuously processes real-time flight, shipment, and environmental data to estimate accident likelihood and initiate mitigation responses — with parallel ANN models predicting multi-stakeholder consequence impacts on the public, businesses, and customers, closing the loop from prediction to resilience.
Supervised ML for Multi-KPI Prediction Under Interacting Systemic Risks
ML models trained on historical project data to simultaneously predict five interdependent performance indicators under the combined effect of interacting and systemic risks — treating multi-output prediction as a first-class modeling problem rather than an afterthought.
Ensemble Machine Learning for Real-Time Dynamic Risk Prediction
Ensemble algorithms trained on injury precursors to generate proactive, real-time safety risk predictions across work zones — moving risk management from reactive lagging indicators to predictive leading indicators.
Graph-Theoretic Machine Learning Pipeline for Cascading Risk Quantification
A three-stage pipeline where complex network theory quantifies team interdependencies as graph features, ML models predict project performance, and network-based and optimization-based mitigation strategies reduce systemic exposure and build resilience into project delivery.
Complex Network Theory and Metaheuristic Optimization for Systemic Failure Modeling
The origin of the network-theory edge: contractor interdependence quantified via weighted degree centrality on dynamic and static project networks — correlated with KPI degradation and mitigated through metaheuristic schedule reorganization. The network-theoretic and optimization foundation that later fuses with machine learning in the work above.
GA-Tuned ML and Multi-objective Optimization for Performance Prediction
Genetic algorithm hyperparameter tuning outperforms grid search across all ML models — predictions then feed NSGA-II multi-objective optimization to generate optimized schedules and risk registers that improve KPIs and build resilience under real-world constraints.
Research Philosophy
How I think about research
Pillar 01
Rigor Without Compromise
Research that cannot be reproduced is not research — it is speculation. Every model, pipeline, and result is held to the standard of reproducibility, statistical validity, and honest evaluation against strong baselines. Rigor is not a phase of the process; it is the process.
Pillar 02
Methods That Travel
A method that only works on the dataset it was built for has limited value. The goal is frameworks that generalize — where the core approach transfers across domains, scales to new data, and survives contact with real-world conditions that were not anticipated at design time.
Pillar 03
From Prediction to Resilience
Predictive accuracy is a means, not an end. Research that stops at a performance metric misses the point. The full value of a model is realized only when its outputs inform decisions and engineer resilience — which requires interpretability, uncertainty quantification, and integration with the constraints of operational environments.
Pillar 04
Reliability as a Design Constraint
Trustworthy systems are not built by accident. Reliability, resilience to distributional shift, and graceful failure under unexpected inputs must be designed in from the start — not patched in after deployment reveals the gaps.
Methods & Tools
Full Technical Stack
Machine Learning & Deep Learning
Designing and training predictive models
Frameworks
Architectures
Generative AI & Agents
Grounded assistants and multi-agent systems
Frameworks
Retrieval
MLOps & Deployment
Taking models to production — and keeping them there
Pipelines
Serving & Monitoring
CI/CD
Cloud, Data & Analytics
Cloud data workflows and decision-ready insight
Cloud
Data
Visualization
Complex Systems & Optimization
Modeling interdependence and optimizing decisions
Networks
Optimization
Geospatial & Risk Analytics
Where the models meet real-world hazard
Geospatial
Risk
Research Impact
Where the research is going
94
Total
Citations
5
H-Index
7+
Peer-Reviewed
Publications
~40
Papers
Reviewed
The next frontier of this research is systems that don’t just predict accurately — but predict reliably, explain themselves, and remain trustworthy as the world they model continues to change. From graph-based risk intelligence to language models that reason over risk networks, the through-line is the same: building AI that holds up under pressure, at scale, in production.