Neel Nanda

Neel studied mathematics at the University of Cambridge, graduating in 2020. He has spent the past year working with a range of AI Alignment labs. He worked with Michael Cohen at the Future of Humanity Institute on theoretical Bayesian Reinforcement Learning, and with Jonathan Uesato at DeepMind on learning from noisy and biased labels. He hopes to better understand how neural networks work on the inside, and to use this understanding to help create networks that robustly do what we want them to. At CHAI, he works with Daniel Filan on neural network interpretability and clusterability.