Indian-origin brain expert from Harvard joins Sam Altman's OpenAI. Meet Akshay Jagadeesh

At OpenAI, Akshay Jagadeesh’s work will focus on safety research and the application of AI in health and medicine. (Image credit: Akshay Jagadeesh, AFP)

An Indian-origin neuroscientist trained at Stanford and Harvard has joined OpenAI as a research resident, where he will focus on advancing safety research and exploring how artificial intelligence can accelerate progress in health and medicine. Akshay Jagadeesh announced the move on social media, calling it “the next chapter” in his journey from studying the human brain to building artificial general intelligence (AGI).

“I’m thrilled to share that I’ve joined @OpenAI as a research resident, focusing on safety research and AI for health. After nearly 10 years studying the brain, I’m excited for this next chapter, building AGI to accelerate medical progress and scientific research!” he wrote on X.

At OpenAI, Jagadeesh’s work will focus on safety research and the application of AI in health and medicine—an area increasingly seen as a frontier for artificial intelligence.

From visual cortex to AGI: a decade of brain research

Jagadeesh’s academic journey spans nearly ten years of neuroscience research, with a focus on human visual perception. He is currently a postdoctoral research fellow at Harvard Medical School in the lab of Dr. Marge Livingstone, where his work centers on how the brain transforms sensory inputs into perceptual experiences that guide behavior.

His research has aimed to characterise the geometry of sensory representations in the human visual cortex and model the cortical computations involved in goal-driven behavior. A key theme in his work has been the role of attention in modulating sensory representations through task-dependent readouts.

Bridging neuroscience and AI

Jagadeesh’s interests lie at the intersection of neuroscience and artificial intelligence. He mentioned on his website that he has explored the similarities and differences between the human visual system and deep neural networks, asking why humans excel at tasks that challenge AI models, and vice versa. His research combines functional neuroimaging, electrophysiology, visual psychophysics, and computational modeling.

Stanford-trained, Berkeley-born academic

Jagadeesh completed his PhD in psychology and computational cognitive neuroscience at Stanford University in 2022. His doctoral research examined neural representations underlying object and texture recognition in humans and deep convolutional neural networks.

He holds dual bachelor’s degrees in computer science and cognitive science from the University of California, Berkeley. He also spent time as a visiting researcher at Humboldt Universität/BCCN Berlin.

Educator and mentor

Beyond research, Jagadeesh is passionate about teaching. He has designed and taught courses at UC Berkeley and Stanford on topics ranging from computer vision to cognitive neuroscience and the science of meditation. At Stanford, he served as a graduate student instructor for courses including Intro to Cognitive Neuroscience, Foundations of Cognition, Perception, and Cognitive Neuroscience Core. He also teaches AI to high school students through the Inspirit AI institute.