Funding

Our research is only possible with the funding provided by a broad range of grants and industry collaborations. We are deeply thankful to all the entities listed below.

Active Grants

• "NSF Center for Pandemic Insight (NSF CPI)" (more information here) support by NSF, 09/01/2024 - 08/31/2031
• "NCS-FO: Understanding the computations the brain performs during choice," support by NSF, 09/01/2023 - 08/31/2026
• "Metrics and Models for Real-Time Inference and Prediction of Trust in Human-autonomy Teaming," supported by AFOSR, 11/25/2022 - 11/24/2025
• "Unmanned Aerial Vehicle Swarms for Large-Scale, Real-Time, and Intelligent Disaster Responses," supported by Sony Corporation, 06/17/2021 - 09/30/2025
• "NCS-FO: ​Identification and Control of Neural Cognitive Systems," supported by NSF, 10/01/2020 - 09/30/2024
• "AI Institute: Next Generation Food Systems" (more information here), supported by USDA/NSF, 09/01/2020 - 08/31/2025

Current Projects

Causal Explainable Reinforcement Learning

Trust is an essential prerequisite for adopting AI-based systems. Explainability has been identified as one key component in increasing users’ trust and acceptance of AI-based systems. Furthermore, explainability justifies an AI’s decisions and enables the AI to be fair and ethical. Reinforcement learning (RL) will be vital in solving control and decision-making problems in healthcare, agriculture, transportation, social networking, e-commerce, etc. This project aims to advance both the theoretical and algorithmic foundations of the explainable RL (XRL) framework. We will adopt a human-centric methodology by considering existing knowledge of how humans define, generate, select, present, and evaluate explanations. We will first focus on devising methods for generating causal (e.g., counterfactual) explanations. We are collaborating on this project with Dr. Xin Liu (CS, UCD) and Dr. Xin Chen (ISyE, Gatech). USDA and NSF currently support this project.

Real-Time Inference and Prediction of Trust in Human-Autonomy Teaming

Humans will interact with ever-more autonomous technical systems in future civilian and military settings. Due to such systems' safety- and mission-critical nature, human mistrust, distrust, overreliance, or skepticism of autonomy may lead to ineffective team performances and even disastrous outcomes. A key component to ensure high-performing human-autonomy teams (HAT) is properly calibrated trust. Trust pertaining to many HAT tasks is dynamic and ever-changing in response to repeated human-autonomy interactions. Therefore, to ensure seamless HAT, there is an urgent need to account for this dynamic nature of trust and devise objective, unobtrusive means of inferring and predicting trust. This project addresses this need by developing a range of embedded and physiological metrics and models capable of inferring and predicting single-trial trust dynamics in complex HAT settings and real time. We are collaborating on this project with Dr. Allie Anderson and Dr. Torin Clark at CU Boulder​. This project was supported by CITRIS under the grant "Trust Aware Human-Machine Teaming Using Real-Time Neurophysiological Data" (01/01/2023 - 12/31/2023) as well as NASA under the grant "Habitats Optimized for Missions of Exploration (HOME)" (09/01/2019 - 08/31/2024).and is currently supported by AFOSR.​

System Identification and Control of Neural Cognitive Systems

 

Current brain-machine interface (BMI) technology is focused on controlling motor prostheses. However, we will see more devices directly interacting with cognitive systems in the future. The project aims to develop tools for system identification and concomitant control of neural cognitive systems by combining expertise in system identification and control of technical systems and neural mechanisms underlying cognitive function. We will implant nonhuman primates that have been trained on different cognitive tasks with recording and stimulation electrode arrays in frontal cortical areas to obtain the required high-channel-count data for system identification, for testing the suitability of dynamical models and learning algorithms, and for testing the effectiveness of control algorithms and stimulation strategies to modify the current state of the system. We are collaborating on this project with Dr. Jochen Ditterich in the Center for Neuroscience. This project is currently supported by NSF.

Understanding Computations Brain Performs During Choice

This project aims to develop a causal framework to investigate the neural computations underlying decision-making by taking advantage of a neuroeconomic decision-making task, the Gambling Task. Decision-making is fundamental to our actions as humans, and poor decision-making, as a consequence of cognitive impairment, affects patients, caregivers, and their families, contributing to the more than $2 trillion cost expected in the US alone by 2030. One challenging problem is that we do not have suitable methods to understand how ongoing neural dynamics across brain regions encode decision-making processes. This prevents an understanding of the computations the brain performs to make decisions. To overcome this limitation, we will combine new feature engineering and machine learning methods with engineering systems identification and control theory to develop a framework capable of understanding the brain's computations during decision-making.  We are collaborating on this project with Dr. Karen Moxon (CS, UCD), Dr. Xin Liu (CS, UCD), and Dr. Ignacio Saez (Mount Sinai). NSF currently supports this project.

Unmanned Aerial Vehicle Swarms for Large-Scale, Real-Time, and Intelligent Disaster Responses

For environmental health scientists to study the impacts of wildfires on human health, gaseous pollutant concentration resulting from these burning events and quantitative information on the characteristics of aerosol distribution are needed. Ground sensors, manned airplanes, and satellites are widely used. However, the spatial and temporal resolution of data collected by these platforms is relatively low and inadequate for local and regional exposure assessments. Moreover, manned airplanes are prohibitively expensive and can be used only for sparse and sporadic measurements. The goal of this project is to explore the possibility of using a swarm of coordinated small (lightweight) UAVs equipped with low-cost air-quality sensors to provide three-dimensional (spatially and temporally resolved), real-time information on aerosol and gaseous pollutant concentrations over a large area affected by nearby wild/prescribed fires. This data should facilitate the local and regional human exposure assessments. We are collaborating with Dr. Ajith Kaduwela and Dr. Anthony Wexler in the Air Quality Research Center on this project. This project was supported by CITRIS under the grant "Uncrewed Aerial Vehicle Swarms for Large-Scale and Real-Time Air Toxic Measurement near Wildland-Urban-Interface Fires" (01/01/2022 - 12/31/2022) and is currently supported by Sony.

Completed Projects

XCPS: Explainable Cyber-Physical Systems (09/2019-08/2024)

 

Greater complexity in (semi-)autonomous systems is a double-edged sword. More sophisticated automation may increase, rather than decrease, the difficulty for a human operator to comprehend the system’s behaviors. The problem may be exacerbated when the operator is denied a direct observation of the internal functioning of the system, thus having to surmise what may be occurring from sensor data. This can be daunting, mainly when some sensors are susceptible to failure. Many such systems, e.g., driverless cars, nuclear power plants, and spacecraft, are safety-critical: a small error may lead to unintended, highly regrettable consequences. This project aimed to enable safety-critical (semi-)autonomous systems to inspect their observations and consequently explain themselves in a language understandable by humans. This project was supported by NASA under the grant "Habitats Optimized for Missions of Exploration (HOME)" (09/01/2019 - 08/31/2024).

 Safety-Assured Imitation Learning for Vision-Based Control of UAS in Complex, GPS-Denied Environments (03/20-08/22)

Many future military (e.g., scouting and patrolling) and civilian (e.g., search-and-rescue) missions require unmanned aerial systems (UAS) that can fly inside complex and possibly GPS-denied environments (e.g., a forest) safely and effectively. This project aimed to tackle one technical challenge hampering the future development and deployment of such UAS: how to guarantee the safety of the UAS, e.g., avoiding collision with nearby obstacles, while maintaining its performance. A natural starting point is imitation learning, which allows an agent to learn what actions to take in an environment from the demonstrations of a human expert. However, even though all expert demonstrations are technically safe in a traditional imitation learning setting, the agent may generate an unsafe control policy. This project aimed to integrate imitation learning and formal methods at the fundamental level to develop safety-assured learning strategies. ONR supported this project under the grant "Safety-Assured Apprenticeship Learning for Vision-Based Control of UAS in Riverine Environments" (03/01/2020 - 08/31/2022).

Network-based Neurophysiological and Psychophysiological Metrics of Human Trust Dynamics When Teamed with Autonomy (04/21-03/22)

The objective of this collaborative project was to develop neurophysiological and psychophysiological measurement-based trust metrics with a particular focus on dynamic assessment of trust in human-autonomy teaming tasks. Specifically, this project integrated the fields of cognitive science, network science, and human factors to (1) develop an experimental methodology to study human trust dynamics in human-autonomy teaming scenarios and (2) investigate if a series of network-based metrics that are derived from multi-modal neurophysiological and psychophysiological measurements, e.g., EEG and fNIRS, can be used to infer human trust dynamically. Our collaborators were Dr. Allie Anderson and Dr. Torin Clark at CU Boulder. This project was supported by AFOSR under the grant "Network-based Neurophysiological and Psychophysiological Metrics of Human Trust Dynamics When Teamed with Autonomy" (04/01/2021 - 09/30/2022)

The left figure shows our human-autonomy teaming setup, while the right figure shows the performances of our classifiers evaluated against test data.

Reducing Pesticide Risk by Using Drones to Enhance Performance of Biological Control (07/18-12/21)

Our world is plagued with large-scale ecological disasters. Tragic examples include the Fukushima I Nuclear Power Plant calamity and the Deepwater Horizon oil spill. Today, these disasters may be partially mitigated by teams of humans working in hazardous and labor-intensive environments. To combat these destructive environmental processes, we envision developing a network of UAVs to combat these disasters while keeping humans at a safe distance. This project was part of our effort to achieve this long-term goal. In this project, we developed a UAS platform, including a UAV and a predatory mite dispensing device, that can precisely and autonomously disperse predatory mites onto infested plants, even in challenging situations like strong winds! Our main collaborator was Dr. Christian Nansen in the Department of Entomology and Nematology. Our project has been featured by two local news stations, ABC10 and CBS13, and ASME. The California DPR supported this project under the grant "Reducing Pesticide Risk by Using Drones to Enhance Performance of Biological Control" (07/01/18-12/31/21).

Smart Energy Management for Uncrewed Aerial System Operation in Complex Military Missions  (07/18-06/20)

​In recent years, uncrewed aerial systems (UAS) and other uncrewed systems (UxS) have found their way onto the battleground to extend the reach of military forces, providing essential intelligence, surveillance, and reconnaissance (ISR) as well as payload delivery and recovery. The increasing capability of such systems, tiny and lightweight UxS swarms, has offered the Navy, the Marine Corps, and other military branches a significant opportunity to potentially reduce cost, increase system resiliency, and enhance operation effectiveness. However, energy efficiency is one fundamental challenge impeding the broad deployment of small-scale UxS and UxS swarms, particularly in complex military missions. This project aimed to tackle this challenge by investigating the fundamental physics governing the UAS energy performance and improving the mission KPIs through energy-oriented planning and control. This was a collaborative work with Dr. Xinfan Lin. The project was funded by the NEPTUNE 1.0 Program of the ONR.

Data-Driven Personalized Training of Next-Generation Workforce (07/16-06/18)

Humans, in the role of either customers, designers, program managers, or workers, will always play a significant part in future manufacturing. However, the systems that humans interact with are becoming increasingly complicated, especially with the rise of large-scale industrial cyber-physical systems (CPSs), also called the Industrial Internet of Things (IIoT). This project aimed at developing a data-driven method of modeling and analyzing humans' manual expertise while interacting with CPSs and traditional machines. The insights gained from this project can help us build machines and CPSs that can collaborate with humans more efficiently and effectively and also allow us to facilitate knowledge transfer among generations. This was a collaborative work with Dr. Barbara Linke.

The video on the left shows the gaze data of an expert performing a manual grinding task, while the video on the right shows the gaze data of a novice performing the same task.