Intelligent Systems

Subareas

Artificial intelligence for signal processing

Efficient machine learning and deep learning technology for real-world signal processing in low-power devices. 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 all 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.

Associated centers, groups, and labs

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