Intelligent systems pushes the limits of artificial intelligence and machine learning performance by engineering the cutting-edge systems in which the intelligence is efficiently embedded. We collaborate with colleagues across disciplines to build customized intellects for engineering applications that understand and react to events in the human body, changes in our environment, the dynamics of molecules, and various sensor signals.See faculty in intelligent systems
Engineering at the cutting edge
Artificial intelligence for signal processing
Develop efficient machine learning and deep learning technology for real-world signal processing in low-power devices. This includes robust pattern recognition, perceptual quality improvement, signal compression, and analysis of multi-modal/multi-source observations from speech, music, radio frequency, and various other biomedical and environmental sensor signals.
Large-scale graph machine learning
Research layers of data analytics covering parallel algorithms, high-performance software and libraries, and applications from biology, social science, and scientific computing to close the gap between expected and actual performance of graph-based applications.
Machine learning for enhancing simulations
Integrate machine learning with simulations to enhance the performance of the simulation and improve its usability for research and education. Generate predictions that comport with the results from specific simulations to enhance the performance gains of parallel computing using machine learning.
Visualization for artificial intelligence
Develop open-source tools for the creation of data visualizations using algorithms to create images from data so humans can understand and respond to data more effectively. Artificial intelligence development is the quest for algorithms that can “understand” and respond to data the same ways as a human can — or better.