Participatory science has opened opportunities for many to participate in data collection for science experiments about the environment, local transportation, disaster response, and public safety where people live. The nature of the collection by non-scientists on a large scale carries inherent risks of sufficient coverage, accuracy and reliability of measurements. This project is motivated by the challenges in data and predictive analytics and in control for participatory science data collection and curation in cyber-physical systems (CPS) experiments.

Animal agriculture has intensified over the past several decades, and animals are managed increasingly as large groups. As animals are often located remotely on large expanses of pasture, continuous monitoring of animal health and well-being is labor-intensive and challenging. This project aims to develop a solar sensor-based smart farm Internet-of-Things network, which is versatile, reliable, and robust to cyberattacks for smart animal monitoring and to demonstrate its operation and practicality on real farms.

Human blood clots kill an estimated 100,000 to 300,000 Americans each year. Current treatments rely on medications that break down clots, which can be combined with a surgical procedure that mechanically alters the clot. However, clot-busting medications and surgery are both linked to unintended adverse events. This project designs and studies miniature magnetic swimmers as a minimally invasive alternative to these treatments. These devices are millimeter-scale objects that have a helical shape and contain a small permanent magnet.

Innovations driven by recent progress in artificial intelligence (AI) have demonstrated human-competitive performance. However, as research expands to safety-critical applications, such as autonomous vehicles and healthcare treatment, the question of their safety becomes a bottleneck for the transition from theories to practice. Safety-critical autonomy must go through a rigorous evaluation before massive deployment. They are unique in the sense that failures may cause serious consequences, thus requiring an extremely low failure rate.

This research will create and validate new approaches for optimally managing mobile observational networks consisting of a renewably powered ?host? agent and ?satellite? agents that are deployed from and recharged by the host. Such networks can enable autonomous, long-term measurements for meteorological, climate change, reconnaissance, and surveillance applications, which are of significant national interest.

This Cyber-Physical Systems (CPS) project will make foundational methodological advances that enable safe and robust reinforcement learning (RL)-based control algorithmic solutions that are driven by problems in smart traffic signal control systems. Recent advances in computation, communication, storage, and sensing have led to a demand for data-driven learning-based decision-making and control in modern cyber-physical systems (CPSs), such as smart transportation systems.

Self-driving cars and home assistants provide just a small glimpse of the future cost-costrainted complex cyber-physical-human systems (CPHS) that will integrate engineering systems with the natural word and humans. This project will devise new mathematical tools and methods to systematically describe CPHS and optimize their operation. The application focus is on wireless body area networks, a natural CPHS representative with humans in the loop, heavily resource-constrained operation, and heterogeneous components that are intertwined with and altered by human behavior.

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. Employing communication between vehicles can improve the efficiency of vehicle control systems to manage traffic compared to vehicles without communication.

This NSF project aims to develop a novel computational framework for informed contextual multi-armed bandits (iCMABs) that will be capable of robustly operating in complex, time-varying environments. The project will bring transformative change to the way that intelligent decision-making agents are designed for CPS, specifically those that utilize variants of multi-armed bandits.

This project will provide funding for students from U.S. institutions of higher learning to attend the 2023 Cyber-Physical Systems and Internet-of-Things Week (CPS-IoT Week 2023) in San Antonio, Texas during May 9-12, 2023. This is the first in-person CPS IoTT week since the pandemic in 2020.