We turn biological shapes into biomedical insights.

Modeling the healthy and pathological shapes of proteins, cells and organs is critical for research in biology and medicine --- ranging from our understanding of cancers to the diagnosis of neurodegenerative diseases. 

We work in collaboration with experts in molecular and cell biology, as well as with clinicians, to advance the frontiers of biomedical knowledge and AI-assisted medical practice. 

Our lab leverages tools from geometry, topology, computer vision, machine learning and deep learning to create computational representations of biological structures in the human body at different scales. We are dedicated to contributing to the transformation of biology and medicine into statistically-grounded and computationally-enabled disciplines.

BioShape Research

Geometry and Learning

geometric art

How can we represent biological shapes with their normal and pathological variabilities into a computer? What is the geometry of a deep learning model? Can we build a geometric model of the mind? Learn more.

Computational Biology

blood cells

Can we reconstruct molecular shapes at a sub-atomic resolution? How do protein shapes determine their function? What information do cancer cell shapes reveal about cancer types? Learn more.

Computational Medicine

brain mris

What does a brain shape tell us about the progression of Alzheimer's disease? What is the uncertainty associated with automatic diagnosis methods relying on organ shape analysis? How can AI best assist clinicians? Learn more.

Latest News


We win the 1st Prize in the C3.ai Covid-19 Grand Challenge

In the C3.ai COVID-19 Grand Challenge, developers, data scientists, students, and creative minds around the world developed meaningful data-driven insights to inform decision makers and change how the world is fighting this pandemic.

Our solution "Modeling Population Heterogeneity by Providing Personalized Covid-19 Diagnostics" with C. Donnat and F. Bunbury won the first prize of $100,000!

Continue ReadingWe win the 1st Prize in the C3.ai Covid-19 Grand Challenge

SCAI Geomstats Hackathon

The Sorbonne Center for Artificial Intelligence helped us organize Geomstats' hackathon, during the conference of Geometric Science of Information (GSI'2021).

It was a great opportunity for attendees to learn how to use existing implementations of differential geometry for their research, and implement new structures!

Geomstats GitHub

Continue ReadingSCAI Geomstats Hackathon