"Where there is matter data, there is geometry"
Kepler
I am a postdoctoral researcher at the Royal Institute of Technology (KTH) in Stockholm, Sweden.
My research focuses on geometric approaches to machine learning and high-dimensional statistics. More specifically, I am interested in algebro-geometric aspects of deep neural networks, manifold/representation learning, geometric density estimation, and topological data analysis.
Research
Below you can find a selection of my academic works, subdivided into topics. Clicking on an image redirects to the corresponding arXiv entry. For a complete list, please visit my Google Scholar profile.
Equivariant/Invariant Deep Learning: These works explore the interaction between symmetry and deep learning, ranging from the invariant theory of neural networks to equivariant representation learning, with applications to robotics.
Equivariant Representation Learning via Class-Pose Decomposition
AISTATS 2023
Equivariant Representation Learning in the Presence of Stabilizers
ECML-PKDD 2023
Learning Geometric Representations of Objects via Interaction
ECML-PKDD 2023
Back to the Manifold: Recovering from Out-of-Distribution States
IROS 2022
Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach
NeurReps Workshop 2023Computational Geometry: These works concern high-dimensional Voronoi tessellations and Delaunay triangulations, with applications to density estimation and active learning.
An Efficient and Continuous Voronoi Density Estimator
AISTATS 2023
Voronoi Density Estimator for High-Dimensional Data: Computation, Compactification and Convergence
UAI 2022
Active Nearest Neighbor Regression Through Delaunay Refinement
ICML 2022
Hyperbolic Delaunay Geometric Alignment
ECML-PKDD 2024
HyperSteiner: Computing Heuristic Hyperbolic Steiner Minimal Trees
ALENEX 2025Algebraic Geometry of Deep Learning: These works explore the (algebraic) geometry of function spaces defined by neural networks.
Geometry of Lightning Self-Attention: Identifiability and Dimension
Preprint
On the Geometry and Optimization of Polynomial Convolutional Networks
PreprintOther: These works concern various topics in pure mathematics, including category theory.
Hearts and Towers in Stable ∞-Categories
Journal of Homotopy 14Thesis: I obtained my doctoral degree from KTH in 2024 under the supervision of Prof. Danica Kragic. Below you can download the thesis and the accompanying slides.
On Symmetries and Metrics in Geometric Inference
KTH 2024Slides
Resume
Click here to download my academic resume.