Autonomous and Intelligent Human-AI-Machine Systems and Urban Environments
The integration of the cyber world with its networks and computing nodes and the physical world with sensors and machines together with people results in new challenges and opportunities for interdisciplinary research driven by computing and artificial intelligence. Our priorities include developing robust and trustworthy autonomous systems as well as designing intelligent and autonomous systems that can work with people in a versatile and natural way.
Areas of interest include:
Autonomous and Intelligent Systems
Autonomy refers to a system’s ability to accomplish goals independently, or with minimal supervision from human operators in environments that are complex and unpredictable. Autonomous systems are increasingly critical to several current and future application domains. We are particularly interested in expanding our efforts in innovative computing technologies that integrate machine learning, control theory and robotics to enable the development of safe and dependable autonomous and intelligent systems.
Trustworthy AI/Machine Learning
Artificial intelligence and machine learning have a huge impact on society and our daily lives. However, such technologies introduce novel risks that can have significant consequences. We are interested in computing solutions to address these challenges by developing the principles, methods, and tools for engineering trustworthy systems with AI/ML. We are interested in safe and secure AI, interpretability and explainability as well as ethics guidelines for trustworthiness and fairness in AI.
Cognitive Science and Human Factors for Human-AI-Machine Systems
The integration of cognitive science, human factors, and AI offers a deep understanding of human cognition and communication that is required to develop the tenets and science for humans interacting with learning-based machines. Important questions include how humans develop internal models for machines that change as they learn and if it is possible to provide evolving AI systems with models of human cognition and decision-making.
Artificial Intelligence in Education and Learning Analytics
AI has emerged as a foundational technology that is profoundly reshaping society. Bringing together the accelerating advances in natural language processing, computer vision, machine learning, simulation and game environments, and multimodal sensing and analysis technologies, AI holds significant transformative potential for improving human learning. We are interested in strengthening and complementing our demonstrated expertise in the learning and cognitive sciences, educational technologies, and multimodal learning analytics to advance learning experiences and outcomes through innovations for teaching, training and learning through the integration of new AI-rich digital platforms.
Computing and Connectivity for Driving City Innovations
In this era of a worldwide pandemic and international calls for social justice, now is the time to provide data-driven and computing solutions to the challenges that cities face. We aim to build on our demonstrated expertise in bringing together scientists and engineers across campuses to collaborate on innovative solutions that improve the quality of life in cities.
- Assured autonomy of learning-enabled cyber-physical systems (Institute for Software Integrated Systems)
- Medical assistive robotics and rehabilitation engineering (Center for Rehabilitation Engineering and Assistive Technologies)
- Inclusion Engineering SM: Empowering individuals with physical and neurological differences through engineering invention, research, and development
- Learning Incubator: a Vanderbilt Initiative (LIVE)
- NSF Institute for an AI-Engaged Future of Learning (ENGAGE)
- City Innovations through the Vanderbilt initiative on Infrastructure Connectivity (CIVIC)
Our priorities include developing robust, trustworthy AI systems and designing intelligent, autonomous systems that can work with people in a versatile and natural way.