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rainbow brain recursive


Neuroscience | Statistics | Photography



My research focus has always been on deciphering the dynamical principles governing the brain and, as such, I have been captivated by the constraining role brain rhythms play in shaping our cognition. My work has already unearthed the significance of simple diffusion dynamics in determining fMRI functional connectivity, especially its perturbation following a stroke. Furthermore, through multiple modalities - MEG, EEG, and LFP - I've delved into the dual nature of oscillatory dynamics, which can both aid and disrupt beneficial synchronization across neural populations in the cortex and subcortex.

I now believe that sleep presents the most pristine lens through which we can understand these rhythms. Sleep is a behavioral state that naturally strips away many confounding factors, making it uniquely positioned to reveal how brain rhythms orchestrate human cognition. Detailed mechanistic work from Mircea Steriade and others has developed the field of rhythms in sleep such that, in combination with the unique data and statistical skills I can access, I can make significant progress as an independent investigator specializing in electrical rhythms during sleep.

My academic journey began under the aegis of Prof. Ramesh Srinivasan at the Human Neuroscience lab in 2019, a hub renowned for its rigorous methodologies, mechanistic cognitive neuroscience, and pioneering EEG theoretical endeavors. Concurrently, to build my intuitions about the inherent assumptions of neuroscientific data analysis, I undertook a Masters in Statistics. This enabled me to craft a generative model for M/EEG functional connectivity anchored by the brain’s anatomical connectivity. This model, which I had the privilege of presenting at UCI’s Workshop on Big Data in Brain Science in 2017, has since been showcased at multiple conferences and is now published at Network Neuroscience.

My postdoctoral journey was enriched by a generative model of rhythms, forming the bedrock for a real-time phase estimation framework, which has now been published at eLife and presented in talks at the New England Statistical Symposium and the 13th Annual Brain Informatics Conference. Alongside this, my collaboration with Dr. Catherine Chu has allowed me a deeper plunge into sleep neuroscience, specifically through thalamocortical recordings during sleep in epileptic patients. This venture has unraveled the intricate ties between cognitive dysfunction in epilepsy and thalamic sleep spindle disruptions: work that I presented at the 1st Annual MGH-MIT iBrain Seminars.

Now, firmly anchored in a postdoctoral role with Dr. Chu, I am focusing on joint analysis of thalamic, cortical, and hippocampal recordings. Marrying my statistical prowess, neural data analytics, and cognitive neuroscience expertise with the unparalleled knowledge of my mentors in epileptology (Dr. Chu) and computational neuroscience (Prof. Kramer), I seek to embark on investigating the triple-coupling of thalamic sleep spindles, hippocampal ripples, and pre-frontal cortex slow oscillations. This research is paramount for not only understanding the basic science of electrophysiological intricacies during sleep but also illuminating sleep-dependent memory consolidation pathways, paving the way for interventions to augment healthy cross-rhythm coupling.

With aspirations of joining a research institution as a faculty member, I want to advance our understanding of the dynamical constraints shaping brain function. My mission extends beyond basic understanding: I endeavor to unravel the network complexities of neurological disorders and develop interventions using real-time stimulation that ensures healthy brain dynamics.


August 2010 - December 2013

BITS Pilani, K.K. Birla Goa Campus

I studied computer science in a space filled with folks that looked outside the box, encouraging me to do so as well, to not see software engineering as a necessary endpoint. I studied psychology in my spare time while gaining the skills expected of engineers. For my thesis work, I did research on neurofeedback and brain computer interfaces. In a rush to get started on research, I managed to finish my degree half a year early.

September 2014 - July 2019

University of California, Irvine

At UCI, I learned to truly think with a scientist's mindset, not just in terms of the scientific method but also with an outlook of adding to the current scientific conversation. I got to spend time thinking about what I wanted to think about, finding that my interests lay in understanding the common dynamical principles in the brain..

September 2019 - March 2023

Boston University

In deciding on where to do my postdoc I was driven by a desire to learn more about rhythms in the brain by building models of them and so joined a group focused on studying coupling between rhythms. I worked on a new generative model of rhythms that we applied towards estimating an important characteristic of rhythms, the phase, in real-time. A contribution that will help us better understand the causal import of rhythms.

March 2023 - Ongoing

Massachusetts General Hospital/Harvard Medical School

To further my understanding of rhythms in the brain, I was convinced I needed to look at the intracranial recordings from patients. In particular, I had the great luck of getting access to patients with intracranial electrodes in the thalamus. My current work is on identifying how sleep rhythms support cognitive function using novel statistical methods and real-time phase-locked stimulation.

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