My research utilizes multimodal neuroimaging methods like EEG, MEG, fMRI, and fNIRS to study working memory, attention, resting state, meditation, and consciousness.  I led multiple projects as a part of my PhD at Boston University including predictive modeling of cognition in healthy subjects and presurgical patients, predicting amyloid and tau levels in subjects with Alzheimer's Disease in collaboration with the MGHMAPP group at Harvard Medical, and mapping sensory-specific working memory and attention networks in the human brain. As a part of my Postdoctoral Fellowship at the Center of Brain Science at Harvard University, I'm working on understanding the function and organization of large-scale brain networks.

We introduced a new analysis for fMRI data called Temporal Synchronization Analysis, a model-free technique to find activations common across trials and subjects. We are working on follow-up studies to demonstrate the efficacy of the technique and release an easy-to-use toolbox for the community.

I am also trying to understand different brain states by multimodal analysis of resting-state fMRI and investigating the spatiotemporal dynamics of meditation. We introduced a new theory of consciousness, bridging concepts from Yogic philosophy and Neuroscience which allows modelling of internal and external states of the mind.

Sensory Specific Working Memory and Attention Networks

Temporal Synchronization Analysis


Spatiotemporal Dynamics of Meditation

Yogic Theory of Conciousness

Alzheimer's Prediction

Large Scale Brain Organization

Cellular & Molecular Architecture of Brain Networks