The most exciting new direction in software-integrated systems is the use of AI/ML-based techniques and components, both in design flows, as well as at run-time. These novel approaches offer new opportunities, but generate novel challenges as well. Our projects cover a broad spectrum covering all aspects from design to implementation to operation.

  • AI in design flows: system design, synthesis, and discovery using machine learning
  • Assured autonomous systems: design and assurance at design-time and run-time
  • Learning-enabled systems: architecture, verification, and operation of systems with learning-enabled components
  • Learning-enabled, distributed decision making under uncertainty: AI/ML in support of decision making

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AI in engineered systems
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Assured Neuro Symbolic Components and Systems (ANSCS)

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.

Decision Support System for Integrated Corridor Management using Artificial Intelligence

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.

CPS: TTP Option: Medium: Collaborative Research: Smoothing Traffic via Energy-efficient Autonomous Driving (STEAD)

Studies show five of the top 10 most-gridlocked cities in the world are in the United States. Traffic congestion puts undue burden on transportation systems across the United States, raising transportation costs and the energy footprint. Vehicle automation creates an opportunity to reduce traffic and improve efficiency of the transportation infrastructure.

Collaborative Research: CPS: TTP Option: Medium: Coordinating Actors via Learning for Lagrangian Systems (CALLS)

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. 

SHF: Small: Collaborative Research: Fuzzing Cyber-Physical System Development Tool Chains with Deep Learning (DeepFuzz-CPS)

Developing a modern technical product such as a car, plane, or a complex medical device includes designing the complex interplay between sensors (which measure physical product and environment state) and actuators (such as small electric motors that control the product).

Collaborative Research: Operator theoretic methods for identification and verification of dynamical systems

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.

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