Machines that Talk to the Brain and Think Like the Mind

Lecture / Panel
For NYU Community

Raghavendra Pothukuchi facing the camera


Raghavendra (Raghav) Pothukuchi
Yale University


"Machines that Talk to the Brain and Think Like the Mind"


Communicating with the brain enables us to advance our understanding of brain function, treat disorders, restore lost function, and when combined with artificial cognitive frameworks, can push the frontier of human capabilities. Key to realizing brain communication and replicating cognition are new computer architectures– systems that directly sense and stimulate the brain, and those capable of running complex cognitive frameworks. In this talk, I will present recent work on the first brain-computer interfacing platform, SCALO, that can sense, process and stimulate neural activity from multiple regions of the brain with millisecond latency and milliwatts of power. SCALO flexibly supports many neuroscientific applications, enabling for the first time, study of brain-wide behavior and diseases. I will also share how new quantum and classical platforms are needed to accelerate cognitive neuroscience modeling, and outline an end to end design that connects such platforms with brain interfaces.

About Speaker

Raghavendra (Raghav) Pothukuchi is an Associate Research Scientist at Yale University. He is an NSF/CRA Computing Innovation Fellow with Profs. Abhishek Bhattacharjee and Jonathan D. Cohen (Princeton, neuroscience). He received his Ph. D. in Computer Science (CS) from the University of Illinois at Urbana-Champaign (UIUC) with Prof. Josep Torrellas. His research is on brain-computer interfaces, quantum and classical frameworks to accelerate cognitive models, and biologically inspired computer architectures. He also has interdisciplinary work on building intelligent and secure computer systems using control theory and machine learning. Raghav has been selected as a young researcher at the Heidelberg Laureate Forum, rising star in computer architecture, and his work has been recognized with a best paper award at ISCA, an IEEE Micro Top Picks selection, best paper nomination at PACT, and other honors.