Clifford Algebras

We introduce a novel method to construct E(n)- and O(n)-equivariant neural networks using Clifford algebras. Published at NeurIPS 2023 (Oral).

We introduce Geometric Clifford Algebra Networks (GCANs) which parameterize combinations of learnable group actions. Published at ICML 2023.

We introduce neural network layers based on operations on composite objects of scalars, vectors, and higher order objects such as bivectors. Published at ICLR 2023.

My passion for Geometric Deep Learning can be unmistakenly traced back to my physics background. I have contributed to the fields of graph neural networks, equivariant architectures, and neural PDE solvers. Furthermore, I have lead efforts to introduce Lie Point Symmetries, and, most recently, Clifford (Geometric) Algebras into the Deep Learning community.