We use mathematics to unify the study of intelligence in brains and machines.

In neuroscience, we still don’t know how large populations of neurons give rise to perception, memory, and learning. In AI/ML, we face significant challenges to understand systems of our own making, leaving them hard to control and trust.

Our lab's hypothesis is that we lack appropriate mathematics that reveals the principles of intelligence in brains and machines. Our research develops the missing framework, uses it to formalize a theory of intelligence across substrates, and builds it into novel artificial neural networks to deliver superior performance without necessarily scaling data, parameters, or compute.

Geometric Intelligence Research

Just as physicists used geometry to build unification theories, we show that brain and machine intelligences can be studied under a common mathematical framework: geometric intelligence.

Geometric Intelligence in Machines

AI

We study the properties of top-performing AI models and design mathematical approaches to improve them. Learn more.

Geometric Intelligence in Brains

NI

We study patterns of neural activity across diverse cognitive functions—from navigation and memory to vision. Learn more.

Building Brain Digital Twins

Brain

We use AI models to build digital twins of the brain, simulating its function in both health and disease. Learn more.

Latest News

Sophia Sanborn is on the TWIML AI Podcast with Sam Charrington

Sophia Sanborn, postdoctoral fellow in our Lab, is featured in the renowned TWIML AI Podcast with Sam Charrington.

Watch her discuss why deep networks and brains learn similar features here!

 

Read MoreSophia Sanborn is on the TWIML AI Podcast with Sam Charrington


Our survey of topological neural networks is the most popular arxiv link!

Our literature review "Architectures of Topological Deep Learning: A Survey of Topological Neural Networks" was the most popular Arxiv link on April 22, 2023!

Congratulations to the authors Mathilde Papillon, Sophia Sanborn and Nina Miolane from our lab, as well as to our collaborator Mustafa Hajij.

Read MoreOur survey of topological neural networks is the most popular arxiv link!


Nina Miolane Awarded a Faculty Research Grant by the Academic Senate

Nina Miolane, PI of the Geometric Intelligence Lab, is recognized by UCSB Academic Senate for the scholarly excellence of the Lab!

This grant, provided by the chancellor, will allow the team to develop novel methodology that can reveal brain anatomical changes across important life events, e.g. through aging, menopause, among many others.

Read MoreNina Miolane Awarded a Faculty Research Grant by the Academic Senate