Nalin Dhiman

Nalin works across physics, neuroscience, mathematics, and machine learning, guided by a simple question: how does the brain learn, think, and process information so efficiently while using so little energy?

To answer these questions, he uses whatever tools and ideas are needed, including nonequilibrium statistical mechanics, stochastic dynamics, information theory, connectomics, and mathematical biology. His toolkit also includes deep learning approaches such as encoder–decoder architectures, transformers, and diffusion models. He is happy to borrow ideas and methods from any field that helps him understand how the brain works.