Industrial AI

Digital Transformation requires Industrial AI research to extend established Industrial Analytics knowledge.

Automated analytical applications that take full benefit of greatly increased data collection capabilities

Scalable data-driven development that requires much less engineering effort than traditional mission critical applications engineering

Scalable computing performance to enable Industrial AI applications deployment at massive scale

Digital Twins - living models that evolve over time to be current as new data becomes available

Industrial AI faculty and their students work in all these areas

Industrial AI Initiative helps industry to navigate analytical technologies for Digital Transformation.

Past research of the Stanford I-AI faculty is broadly used in leading edge mission critical applications and systems in many industrial domains such as aerospace, energy and power, telecom, data centers, supply chains, process industries, operations and support, and financial systems.

I-AI requires rigorous analytical technologies that are sometimes referred as ‘Rocket Science’.

I-AI research extends established analytical technologies that are core areas of expertise for the I-AI Initiative faculty

Control Systems technology developed to support mission critical analytics interacting with physical system. It includes rigorous analytical methods as well as verification and validation (V&V) processes

Operations Research (OR) is focused on analytics for business processes

Signal Processing and Information Theory have major applications in telecommunications and include rigorous evaluation of system performance

Machine Learning (ML) tools can be used for identification of models from data

Mathematical Optimization is a foundational technology that underlies much of the list above. Optimization-based ML is used for model training. Embedded optimization is used for execution of control and decision support strategies.