Neel Nanda
Neel takes a pragmatic approach to interpretability: identify what stands between where we are now and where we want to be by AGI, and then focus on the subset of resulting research problems that can be tractably studied on today's models. This can look like diving deep into the internals of the model, or simpler black box methods like reading and carefully intervening on the chain of thought - whatever is the right tool for the job. This could look like studying how to detect deception, understanding why a model took a seemingly concerning action, or fixing weak points in other areas of safety, e.g. using interpretability to stop models realising they are being tested. You can learn more about Neel's approach in this podcast.
He has spent far too much time having MATS scholars, and has worked with ~60 so far - he’s excited to take on even more!
Stream overview
Mentors
Neel leads the mechanistic interpretability team at Google DeepMind, trying to use the internals of models to understand them better, and use this to make them safer - eg detecting deception, understanding concerning behaviours, and monitoring deployed systems for harmful behaviour.
Since mid 2024, Neel has become more pessimistic about ambitious mechanistic interpretability, and more optimistic that pragmatic approaches can add a lot of value. He's doing less work on basic science, and working more on model biology work, and work applying interpretability to real-world safety problems like monitoring.
He has spent far too much time having MATS scholars, and has about 50 alumni - he's excited to take on even more!
Mentorship style
Fellows we are looking for
Project selection
The Winter 2027 cohort offers a wide range of research streams led by experts across AI alignment, interpretability, governance, and safety. Each stream provides its own research agenda, methodology, and mentorship focus.
