I am interested in understanding the principles that make our brain so much better than man-made machines and AI. We use naturalistic foraging tasks that combine uncertainty, spatial navigation, decision-making and episodic memory to understand inference in the brain. We explore how task-relevant latent variables and multi-sensory signals flow dynamically across brain areas to generate perception and cognition, how hierarchical causal inference is implemented in the brain, how beliefs propagate through the network, and how internal states modulate this information flow. We use time-dependent engineering approaches (e.g., Kalman filter and POMDP framework) to explore and understand neural dynamics and network coding of multi-modal information at multiple stages of processing under diverse naturalistic and perceptual contexts in macaques and rodents. We are interested in the neural implementation of canonical neural computations and how they go astray to result in sensory, motor, memory and cognitive deficits in neuropsychological disease. Our goal is to use this knowledge to understand computational principles of the brain in health and disease, to inspire artificial systems, to aid the development of prosthetics and other tools for understanding and treating deficits of sensory inference, spatial orientation, cognition and action.