Surya Ganguli

Surya Ganguli is an Associate Professor in the Department of Applied Physics at Stanford University, with courtesy appointments in Neurobiology and Electrical Engineering. He serves as Senior Fellow at the Stanford Institute for Human-Centered AI and is affiliated with the Stanford Neuroscience Institute, Bio-X, and Wu Tsai Neurosciences Institute. His research spans theoretical neuroscience, machine learning, and statistical mechanics.

• Ph.D., UC Berkeley, Theoretical Physics (2004)
• M.A., UC Berkeley, Physics (2000)
• M.A., UC Berkeley, Mathematics (2004)
• M.Eng., MIT, Electrical Engineering and Computer Science (1998)
• B.S., MIT, Physics (1998)
• B.S., MIT, Mathematics (1998)
• B.S., MIT, Electrical Engineering and Computer Science (1998)

His lab explores how higher-level cognitive phenomena emerge from neural network dynamics, focusing on perception, memory, attention, and decision-making. Research themes include statistical mechanics of learning, neural representational geometry, and biologically plausible learning rules. Current work examines nonlinear interactions in neural networks through the Schmidt Science Polymath Award (2023).

Key article trends reveal expertise in neural coding limits (2022 Nature), synaptic plasticity models (2022 Neural Computation), and deep learning theory (2022 NeurIPS publications). His 2019 Annual Review chapter on statistical mechanics of deep learning established foundational insights into network criticality.

• Scientific Awards:
• NSF Career Award (2019)
• Simons Foundation Investigator (2016)
• McKnight Scholar Award (2015)
• James S. McDonnell Foundation Scholar (2014)
• Sloan Research Fellow (2013)

As advisor, he mentors 10+ doctoral students across Applied Physics, Neurosciences, and Computer Science, including Vamshi Balanaga and Mason Kamb. His lab collaborates with experimental teams at Stanford and beyond, supported by grants from NSF, Simons Foundation, and Swartz Foundation. The Neural Dynamics & Computation Lab unites physicists, mathematicians, and neuroscientists to decode cognition through interdisciplinary methods.