This project envisions using technology to make music education, specifically learning how to play an instrument, more accessible, enjoyable, and comprehensive than current techniques. While there is no substitute for human-based instruction, especially regarding creative and expressive subjects like music, there is room for productive use of technology to augment existing techniques, particularly in terms of filling the time gaps between interactions with a human instructor and addressing knowledge gaps about particular elements and genres of musical expression.

Neuro-symbolic AI is often envisioned as the next generation, ‘third wave’ of AI that will offer unique advantages both in design-time (e.g., engineering effort and verifiability of results) and at run-time (e.g., performance and safety). While these claims are expected to be true for engineered systems where development processes are built on the model-based (symbolic) tradition, they have not been substantiated in convincing, scaled examples.

During the past two decades, the Federal Highway Administration (FHWA) has invested heavily in researching, piloting, and demonstrating that Integrated Corridor Management (ICM) strategies and systems are a viable alternative to mitigating congestion when lane expansion is not possible. The vision of ICM is that transportation networks will realize significant improvements in the efficient movement of people and goods through institutional collaboration and aggressive, proactive integration of existing infrastructure along major corridors.

This project will improve the ability to build artificial intelligence algorithms for Cyber-Physical Systems (CPS) that incorporate communications technologies by developing methods of learning from simulation environments. The specific application area is connected and automated vehicles (CAV) that drive strategically to reduce stop-and-go traffic. 

The CIRCLES Website https://circles-consortium.github.io contains more detailed information on this project. 

This project aims to develop a new Science of Design for societal-scale Cyber- Physical Systems (CPS).

Widespread use of automation in many sectors of society has yielded a large amount of data regarding historical behaviors for a variety of dynamical systems, such as unmanned aerial, marine, and ground vehicles, biological systems, and weather systems. This project aims to develop novel algorithms to discover governing rules that explain the observed behaviors (i.e., trajectories) of dynamical systems. Discovery of underlying models, while useful for analysis and control, can be computationally challenging.