Urban Intelligence & IoT Lab (UI²) Lab
The IRC-SML Urban Intelligence and IoT (UI2) Lab is a research and development laboratory at KFUPM, focused on safe prototyping, deployment, and scaling of intelligence, sensing, and safety assurance layer of urban mobility, transportation, logistics, and infrastructure systems, with particular emphasis on how AI-enabled systems are verified, validated, and safely deployed in operation at scale.
UI2 develops the methods that turn city-scale sensing and data into decisions that can be trusted in the field: IoT and connected infrastructure sensing, urban data fusion, digital twins and high-fidelity simulation, learning-based prediction and optimization, and decision-making under uncertainty, all considering the technology deployed closely alongside human users.
To that end, the UI2 Lab develops the verification and validation frameworks for AIs in general, including safety and performance specification, rare-event and long-tail evaluation, uncertainty quantification, risk and resilience analysis, runtime monitoring, and the assurance evidence required for certification and regulatory acceptance. The emphasis is on system-level intelligence and on demonstrable safety and reliability, while closely collaborating with other labs at KFUPM working on the design or fabrication of the cyber-physical platforms themselves.
A central goal of UI2 is to close the gap between AI development and safe operational deployment by producing the methods, testbeds, benchmarks, and evidence that allow operators, regulators, and industry partners to adopt AI-enabled systems with quantified confidence that are acceptable for urban-scale deployment.
Vision:
To become a leading regional and internationally recognized hub for scalable and trustworthy urban intelligence, providing the methods, tools, and evidence that allow AI and IoT systems to be deployed safely, reliably, and accountably across mobility, logistics, and critical infrastructure.
Objectives:
1. Develop urban intelligence and IoT solutions for priority applications in Saudi Arabia
Address real operational needs in traffic and transit management, freight and last-mile logistics, ports and terminals, utilities, and smart city and infrastructure monitoring.
2. Build a national capability for AI testing, assurance, and certification readiness
Establish the simulation environments, digital twins, instrumented testbeds, benchmarks, and evaluation protocols needed to assess AI systems before and during deployment, aligned with emerging standards such as ISO/PAS 8800, UL 4600, and the NIST AI Risk Management Framework.
3. Quantify and manage risk in AI-enabled urban and logistics systems
Model and contain safety, security, and resilience risks, including rare failures, cascading effects across connected systems, and performance degradation under distribution shift.
4. Advance interdisciplinary high-impact research in AI verification, validation, and safety
Develop internationally competitive methods for rare-event evaluation, uncertainty quantification, formal and statistical assurance, safe decision-making under uncertainty, and runtime monitoring of learning-enabled systems.
5. Build strategic R&D partnerships with academia, industry, and government
Co-develop assurance and evaluation solutions with transportation, logistics, energy, infrastructure, and technology partners, and support regulators and operators with independent, evidence-based assessment of AI systems before adoption.
6. Develop talent in trustworthy AI and connected systems
Train students and researchers across the full lifecycle, from sensing, data, and modeling through validation, certification evidence, deployment, and operational monitoring.