Projects

Adaptive frame averaging for invariant and equivariant representations

Graphs
ML
GDL

Prof Bruno Ribeiro

Characterizing generalization and adversarial robustness for set networks

Graphs
ML
GDL

Prof Tolga Birdal

Contrastive learning

Graphs
ML
GDL

Dr Melanie Weber

DImplicit neural filters for steerable CNNs

Graphs
ML
GDL

Gabriele Cesa

Data reductions for graph attention variants

Graphs
ML
GDL

Kaustubh Dholé

Deep functional map

Graphs
ML
GDL

Dr Abhishek Sharma

Differential geometry for representation learning

Graphs
ML
GDL

Prof Georgios Arvanitidis

Distilling large GNNs for molecules

Graphs
ML
GDL

Johannes Gasteiger

Equivariant machine learning for vegetation dynamics

Graphs
ML
GDL

Prof Soledad Villar

Exploiting domain structure for music ML tasks

Graphs
ML
GDL

Dr Cătălina Cangea

Exploring network medicine principles encoded by graph neural networks

Graphs
ML
GDL

Kexin Huang

Generalized Laplacian positional encoding for graph learning

Graphs
ML
GDL

Dr Haggai Maron

Geometric tools for investigating loss landscapes of deep neural networks

Graphs
ML
GDL

Dr James Lucas

Graph-rewiring for GNNs from a geometric perspective

Graphs
ML
GDL

Dr Francesco di Giovanni

Helmhotlz-Hodge Laplacians: edge flows and simplicial learning

Graphs
ML
GDL

Prof Stefan Schonsheck

Latent graph learning for multivariate time series

Graphs
ML
GDL

Dr Xiang Zhang

Learning graph rewiring using RL

Graphs
ML
GDL

Dr Eli Meirom

Learning non-geodesic submanifolds

Graphs
ML
GDL

Prof Nina Miolane

Line bundle cohomology formulae on Calabi-Yau threefolds

Graphs
ML
GDL

Dr Andrei Constantin

Machine learning the fine interior

Graphs
ML
GDL

Prof Alexander Kasprzyk

PDE-inspired sheaf neural networks

Graphs
ML
GDL

Cristian Bodnar

Towards training GNNs using explanation feedbacks

Graphs
ML
GDL

Dr Chirag Agarwal
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