Fahd Alrawaili

Highly motivated researcher with a robust foundation in mathematics, machine learning, deep learning, and causal inference, focused on the development of data-driven, explainable, and intelligent systems for urban environments. Experienced in applying computational models, structural causal frameworks, and neural network architectures to optimize traffic systems, energy management, environmental monitoring, and urban decision-making. Committed to advancing evidence-based, scalable, and sustainable solutions in the domain of AI for smart cities, bridging theoretical research with practical applications.

• Fahd F. Alrawaili, Marwan Bikdash. Bayesian Network for Causal Analysis Based on Domain Knowledge Experts.
• Fahd F. Alrawaili, Marwan Bikdash. Consensus Measuring between Feature Ranking Methods.

• MATLAB, Python, R.

• Urban Mobility & Traffic,
• Causal AI for Smarter Urban Traffic Decision-Making, SCM & DAGs | Traffic Policy Simulation | Accuracy: 92%, Graph
• Neural Networks Transforming Urban Mobility, Graph Neural Networks | Urban Road Networks | Congestion Reduction: 15%,
• Deep Learning–Powered Urban Traffic Intelligence, LSTM & DNN | Time-Series Prediction | RMSE Improvement: 25%,
• Energy & Resource Optimization,
• AI-Driven Energy Forecasting for Smart City Systems, Gradient Boosting | Forecast Error ↓ 30%,
• Hybrid AI & Optimization Models for Renewable Energy Planning, Mathematical Optimization + AI | Efficiency Improved,
• Big Data Analytics for Smart Resource Management, Machine Learning | Urban Water & Waste | Real-Time Decision Support,
• Predictive Analytics for Water & Waste Management in Cities, Deep Learning | Anomaly Detection | Precision: 90%,
• Environment & Air Quality, Deep Learning Models for Intelligent Air Quality Monitoring, LSTM & CNN | Spatio-Temporal Prediction | RMSE Improvement: 25%,
• Decision Support & Governance,
• Reasoning-Driven AI & Mathematical Models for Urban Planning, Hybrid AI + Mathematical Modeling | Policy Optimization,
• Explainable Causal AI for Smart City Decision Support, SCM & DAGs | Interpretable Predictions | Increased Trust,
• AI-Powered Causal Frameworks for Data-Driven Policy Making, Counterfactual Analysis | Big Data | Policy Outcome Evaluation,
• Public Safety & Emergency Response,
• AI-Enhanced Public Safety & Emergency Analytics, Predictive Analytics | Sensor Data Integration | Faster Emergency Response.

• Marwan Bikdash, bikdash@ncat.edu.

• Dukka Kc, dukka.kc@wichita.edu

• Kaushik Roy, kroy@ncat.edu.