Research

Foundations encompass the core principles, frameworks, and methodologies of data science and AI. This includes work on the ethics and education of data science and AI, and foundational methods in assurance and autonomy.

Interactive and Embodied AI focuses on the application of data science and AI in the physical world and in human interaction. It includes the development of AI systems that can navigate and interact with their environments, as well as systems designed to collaborate with people. Key focus areas include computer vision, language, speech, and robotics.

Health and Medicine considers the application of data science and AI expertise to understand human health and improve the practice of medicine—advancing diagnosis, guiding treatment, and improving care delivery. It also includes work to accelerate drug and device discovery, basics of disease modeling, and precision medicine.

Security and Safety applies data science and AI methods to advance the development and integration of safe, trustworthy AI systems across various fields. The goal is to prevent unintended consequences, foster public trust in AI-driven innovations, and ensure human safety and security.

Scientific Discovery integrates foundational data science and AI methods into research and discovery across the life sciences, physical sciences, social sciences and the humanities. The goal is to accelerate and deepen scientific understanding and drive advancements across disciplines.

Engineered Systems applies data science and AI methods to design, build, test, and deploy engineered systems across a range of fields, including materials design, security and defense, business, energy, and industrial design.

People, Policy, and Governance addresses fundamental challenges in how people interact with data science and AI systems. It also studies human populations using AI methods and examines how society seeks to encourage, guide, limit, and regulate these technologies.