Human brain is the ultimate computing device, as yet unsurpassed in performance or complexity. Understanding its inner workings might facilitate the advent of strong artificial intelligence, and perhaps also yield the answer to one of the persistent mysteries taunting mankind; what is the nature of consciousness? 

An important aspect of brain function is the selective and prioritized processing of sensory information based on behavioral demands, a phenomena commonly known as attention. My research at UCSD as a graduate student and at NYU as a postdoctoral scholar involved investigating the neural mechanisms underlying selective attention and adaptation in humans using a combination of fMRI, psychophysics, and computational modeling. My Ph.D. thesis formulated an information-theoretic framework for attention, and postulates that attention fundamentally operates by changing the language of neural communication and information representation, the neural population codes, in the brain. 

I have segued into applied neuroscience and am currently a scientist at Columbia University, associated with the lab of Prof. Paul Sajda; liinc.bme.columbia.educnec.columbia.edu, and idse.columbia.edu. My current research involves creating next generation Brain Computer Interfaces for joint man-machine control of aircraft, in collaboration with Applied Physics lab (JHU) and DARPA. My long term interest lies in understanding human intelligence, artificial intelligence, and the interaction of the two (think movie 'The Matrix').

In my spare time, I indulge in biking, boxing, and sailing.

Email: ssaproo <dot> ucsd <at> gmail

Representative Publications

Neural mechanisms underlying catastrophic failure in human–machine interaction during aerial navigation, Journal of Neural Engineering, August 2016. 13 066005 (Supplement)

Cortically Coupled Computing: A New Paradigm for Synergistic Human–Machine Interaction, IEEE Computer, September 2016. 0018-9162/16

Attention Improves Transfer of Motion Information between V1 and MT, The Journal of Neuroscience, March 2014. 34(10):3586 –3596