The Bioinspired Artificial Intelligence Lab (DLab) explores how principles found in living systems can guide the development of new forms of artificial intelligence. We seek to bridge biological intelligence and computational design, using nature as a source of inspiration for building systems that are adaptive, robust, and capable of complex behavior.

Our research focuses on the mechanisms and architectures that support intelligence across multiple scales. To this end, we combine artificial intelligence, machine learning, neuroscience, complex systems, mathematics, and high-performance computing to study learning, adaptation, and neural computation, as well as coordination in distributed systems and the behavior of populations of agents interacting with dynamic environments and social systems.

Neuromorphic computing is one of the core research areas of the lab. In this area, we explore how principles derived from neural systems can inspire efficient and adaptive hardware and software for perception, learning, memory, and decision-making.

By connecting biological inspiration with computational innovation, DLab seeks to advance both the scientific understanding of intelligence and the design of more adaptive, resilient, efficient, and biologically grounded AI systems.