Surya Ganguli is a renowned researcher in theoretical neuroscience, machine learning, and applied physics. He is a professor of Applied Physics at Stanford University, where he leads the Neural Dynamics and Computation Lab, an Associate Director of Stanford's Human Centered AI Institute, and a Venture Partner at a16z. His research focuses on understanding the fundamental principles that underlie the brain’s ability to process information, learn, and adapt, using mathematical and computational approaches to bridge neuroscience and artificial intelligence.

Dr. Ganguli’s work spans several interdisciplinary areas, including the study of deep learning dynamics, the theory behind neural networks, and the mechanisms of learning and memory in biological systems. He is known for his theoretical insights into how complex behaviors emerge from interactions within neural circuits, and his research has applications in developing more robust and interpretable AI models.

He has been awarded numerous awards, including a Burroughs-Wellcome Career Award, two NeurIPS Outstanding Paper Awards, a Sloan fellowship, a James S. McDonnell Foundation scholar award in human cognition, a McKnight Scholar award in Neuroscience, a Simons Investigator Award in the mathematical modeling of living systems, an NSF CAREER award, and a Schmidt Science Polymath Award.

In addition to his academic work, Dr. Ganguli has collaborated with leading AI research labs and technology companies to help advance the field of machine learning. He is also a frequent speaker at conferences, where he shares his expertise in understanding both biological and artificial intelligence systems, contributing to discussions about the intersection of neuroscience, AI, and ethics.

Dr. Ganguli triple majored in physics, mathematics, and EECS at MIT, completed a PhD in string theory at Berkeley, and a postdoc in theoretical neuroscience at UCSF. He has also been a visiting researcher at both Google AI and Meta AI.

TED, Artificial Intelligence, Computer Science, Stanford University, Machine Learning Physics, Neuroscience, Professors, Academic,