Johns Hopkins School of Medicine

Our central motivation is to understand animal and machine intelligences, and in particular how learning and memory arises in such systems. We believe that all intelligences operate under certain shared principles, much like all matter and energy are governed by certain principles. By determining these foundational principles, we can increase the rate at which we understand and improve intelligences.

To do so, we design, build, study, and apply statistical machine learning and big data science techniques. The tools we develop and utilize follow some basic design principles, including philosophical (validity, uncertainty), statistical (consistency, efficiency, robustness), and computational (scalability, tunability); see this blog post for details. While we don't fully understand how intelligence works, we do have some current working hypotheses, subject to revision given new data or theories.

See our website at https://neurodata.io/ for more information.