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
We study the properties of top-performing AI models and design mathematical approaches to improve them. Learn more.
Geometric Intelligence in Brains
We study patterns of neural activity across diverse cognitive functions—from navigation and memory to vision. Learn more.
Building Brain Digital Twins
We use AI models to build digital twins of the brain, simulating its function in both health and disease. Learn more.
Latest News
Adele Myers is Interviewed by the Machine Learning Street Talks Podcast @ NeurIPS
Adele Myers, Ph.D. student in the Geometric Intelligence Lab, is featured in the renowned Machine Learning Street Talks Podcast Series at NeurIPS 2022 for her work: "Regression-Based Elastic Metric Learning on Shape Spaces of Elastic Curves" with Nina Miolane. Watch her interview here!
Read MoreAdele Myers is Interviewed by the Machine Learning Street Talks Podcast @ NeurIPS
Mathilde Papillon Receives the Best Paper Award at EAI ArtsIT
Mathilde Papillon, Ph.D. student in the Geometric Intelligence Lab, receives the prestigious Best Paper Award at the conference EAI ArtsIT for her work: "PirouNet: Creating Dance through Artist-Centric Deep Learning" with Mariel Pettee and Nina Miolane.
Read MoreMathilde Papillon Receives the Best Paper Award at EAI ArtsIT
Watch Nina Miolane's Keynote at the CVPR Workshop on Deep Learning for Geometric Computing
Nina Miolane, PI of the Geometric Intelligence Lab, presented the work of our group at the workshop "Deep Learning for Geometric Computing". Watch the video here!
Read MoreWatch Nina Miolane's Keynote at the CVPR Workshop on Deep Learning for Geometric Computing