We use large-scale neural recordings, combined with machine learning, to uncover the computational principles that guide decision-making. Our research focuses on internal brain states, such as those related to decisions and attention, and how these states shape behavior over time.

Rather than inferring brain states only from behavior or context, we integrate behavioral data with neural recordings to define these states more precisely and track how they change.

In the long term, our goal is to build a framework for real-time tracking and targeted manipulation of brain states under controlled conditions.