Our patent-pending dynamic phantom can provide, for the first time, a “ground truth” for the fMRI signal. This can be used to rigorously estimate and correct for scanner-induced distortion of time-series dynamics (“dynamic fidelity”). Applications include increasing SNR for clinical imaging, normalization across sites for multi-site studies, and improved integration of fMRI time-series with computational modeling.

Using biometric sensors, we are monitoring and analyzing data from the human body, in order to understand how the immune system functions in response to infection.

We are developing algorithms to ‘unmix’ the noise in stochastic signals, so as to clean the signal in situations where we have no known ground truth.

We developed a series of state - of - the - art algorithms to intelligently denoise resting state fMRI data.