In precision agriculture, where data is used to automate and optimize agricultural tasks for maximum yield, large-scale ...

Standard AI VP Aniruddha Maru explains why real-world machine learning failures are usually systems issues, not model flaws.

Data analysis is enabling teams to make better decisions, and systems engineering is no different. Digital solutions have emerged to help engineers gather and analyze data from modeling, simulation, ...

The model predicted that if B cells were nearly eliminated, there would be little impact on the vaccine response, and experiments showed that prediction was correct. This modeling approach could be ...

This course introduces the basic concepts of functional verification and model checking, highlighting their importance in modern system designs. It explains different modeling formalisms for ...

Theory, computation, and simulation are foundational to modern energy research. Theoretical understanding reveals why materials and systems behave as they do, predicting performance before experiments ...

Complexity in hardware design is spilling over to other disciplines, including software, manufacturing, and new materials, creating issues for how to model more data at multiple abstraction levels.