Luana Ruiz

Assistant Professor, Department of Applied Mathematics and Statistics, Johns Hopkins University

Office N452

Wyman Park Building

Baltimore, MD 21211

I am an assistant professor with the Department of Applied Mathematics and Statistics at Johns Hopkins University. Before that, I was a METEOR and FODSI postdoctoral fellow working with Prof. Stefanie Jegelka at MIT, and a Google Research Fellow at the Simons Institute for the Theory of Computing. I obtained my PhD from the Electrical and Systems Engineering Department at Penn, where I was very fortunate to be advised by Prof. Alejandro Ribeiro.

My primary research interests are in the areas of machine learning and signal processing over networks. My current work focuses on large-scale graph information processing and graph neural networks. Please check my CV here and a selected list of publications below.

news

Dec 9, 2023	Invited talk “Machine Learning on Large-Scale Graphs: Graphon NNs and Learning by Transference” at the Canadian Mathematical Society Winter Meeting.
Nov 16, 2023	Invited long talk “Large-Scale Graph Machine Learning: Tradeoffs, Guarantees and Dynamics” at DeepMath 2023.
Nov 10, 2023	I am serving as TC chair at ICASSP 2024 and as publicity chair at Graph Signal Processing Workshop (GSPW) 2024. Consider submitting an abstract to GSPW!
Oct 17, 2023	New preprint A Local Graph Limits Perspective on Sampling-Based GNNs with Yeganeh and Amin.
Oct 16, 2023	Talk “Large-Scale Graph Machine Learning: Tradeoffs, Guarantees and Dynamics” at INFORMS.
Sep 22, 2023	Talk “Large-Scale Graph Machine Learning: Tradeoffs, Guarantees and Dynamics” at the IFML Seminar at UT Austin.
Sep 14, 2023	Talk “Manifold Neural Networks for Large-Scale Geometric Information Processing” at the JHU AMS Department Seminar.
Aug 21, 2023	In Tokyo for ICIAM 2023 to speak about graphon neural tangent kernels at the “Geometric Methods in Machine Learning Minisymposium”, organized by Jeff Calder and Leon Bungert.
Aug 1, 2023	I have officially started my new position at JHU.
Jul 23, 2023	Paper “Graph Neural Tangent Kernel: Convergence on Large Graphs” at ICML 2023.

selected publications

2023

Learning by Transference: Training Graph Neural Networks on Growing Graphs

J. Cervino, L. Ruiz, and A. Ribeiro

IEEE Trans. Signal Process., 2023

@article{cervino2021increase,
  title = {Learning by Transference: Training Graph Neural Networks on Growing Graphs},
  author = {Cervino, J. and Ruiz, L. and Ribeiro, A.},
  journal = {IEEE Trans. Signal Process.},
  volume = {71},
  pages = {233--247},
  year = {2023},
  publisher = {IEEE},
}

Transferability Properties of Graph Neural Networks

L. Ruiz, L. F. O. Chamon, and A. Ribeiro

IEEE Trans. Signal Process., 2023

@article{ruiz2021transferability,
  title = {Transferability Properties of Graph Neural Networks},
  author = {Ruiz, L. and Chamon, L. F. O. and Ribeiro, A.},
  journal = {IEEE Trans. Signal Process.},
  year = {2023},
  publisher = {IEEE},
}

Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs

Z. Wang, L. Ruiz, and A. Ribeiro

arXiv [cs.LG]::2305.18467, 2023

@article{wang2023convergence,
  title = {Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs},
  author = {Wang, Z. and Ruiz, L. and Ribeiro, A.},
  journal = {arXiv [cs.LG]::2305.18467},
  year = {2023},
}

Graph Neural Tangent Kernel: Convergence on Large Graphs

S. Krishnagopal, and L. Ruiz

In 40th ICML, 23-29 jul. 2023

@inproceedings{krishnagopal23-gntk,
  title = {Graph Neural Tangent Kernel: Convergence on Large Graphs},
  author = {Krishnagopal, S. and Ruiz, L.},
  booktitle = {40th ICML},
  address = {Honolulu, HI},
  year = {2023},
  month = {23-29 Jul.},
}

Convolutional Filtering on Sampled Manifolds

Z. Wang, L Ruiz, and A. Ribeiro

In 48th ICASSP, 4-10 jun. 2023

@inproceedings{wang2023convolutional,
  title = {Convolutional Filtering on Sampled Manifolds},
  author = {Wang, Z. and Ruiz, L and Ribeiro, A.},
  booktitle = {48th ICASSP},
  address = {Rhodes, Greece},
  year = {2023},
  month = {4-10 Jun.},
}

Training Graph Neural Networks on Growing Stochastic Graphs

J/ Cerviño, L. Ruiz, and A. Ribeiro

In 48th ICASSP, 4-10 jun. 2023

@inproceedings{cervino2023training,
  title = {Training Graph Neural Networks on Growing Stochastic Graphs},
  author = {Cervi\~no, J/ and Ruiz, L. and Ribeiro, A.},
  booktitle = {48th ICASSP},
  address = {Rhodes, Greece},
  year = {2023},
  month = {4-10 Jun.},
}

A Local Graph Limits Perspective on Sampling-Based GNNs

Y. Alimohammadi, L. Ruiz, and A. Saberi

arXiv:2310.10953 [cs.LG], 4-10 jun. 2023

@article{alimohammadi2023local,
  title = {A Local Graph Limits Perspective on Sampling-Based GNNs},
  author = {Alimohammadi, Y. and Ruiz, L. and Saberi, A.},
  journal = {arXiv:2310.10953 [cs.LG]},
  year = {2023},
}

A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs

L. Ruiz, N. Huang, and S. Villar

2211.03231 [cs.SI]. Accepted at ICASSP 2024, 4-10 jun. 2023

@article{ruiz2024icassp,
  title = {A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs},
  author = {Ruiz, L. and Huang, N. and Villar, S.},
  journal = {2211.03231 [cs.SI]. Accepted at ICASSP 2024},
  year = {2023},
}

2021

Graph Neural Networks: Architectures, Stability and Transferability

L. Ruiz, F. Gama, and A. Ribeiro

Proc. IEEE, 4-10 jun. 2021

@article{ruiz2020gnns,
  title = {Graph Neural Networks: Architectures, Stability and Transferability},
  author = {Ruiz, L. and Gama, F. and Ribeiro, A.},
  journal = {Proc. IEEE},
  volume = {109},
  number = {5},
  pages = {660--682},
  year = {2021},
  publisher = {IEEE},
}

Graphon Signal Processing

L. Ruiz, L. F. O. Chamon, and A. Ribeiro

Trans. Signal Process., 4-10 jun. 2021

@article{ruiz2020graphonsp,
  title = {Graphon Signal Processing},
  author = {Ruiz, L. and Chamon, L. F. O. and Ribeiro, A.},
  journal = {Trans. Signal Process.},
  volume = {69},
  pages = {4961--4976},
  year = {2021},
  publisher = {IEEE},
}

Stability to Deformations of Manifold Filters and Manifold Neural Networks

Z. Wang, L. Ruiz, and A. Ribeiro

arXiv [cs.LG]:2106.03725, 4-10 jun. 2021

@article{wang2021stability,
  title = {Stability to Deformations of Manifold Filters and Manifold Neural Networks},
  author = {Wang, Z. and Ruiz, L. and Ribeiro, A.},
  journal = {arXiv [cs.LG]:2106.03725},
  year = {2021},
}

Stability of Neural Networks on Riemannian Manifolds (Best Paper Award)

Z. Wang, L. Ruiz, and A. Ribeiro

In 29th EUSIPCO, 23-27 aug. 2021

@inproceedings{wang2021manifold,
  title = {Stability of Neural Networks on {R}iemannian Manifolds (Best Paper Award)},
  author = {Wang, Z. and Ruiz, L. and Ribeiro, A.},
  booktitle = {29th EUSIPCO},
  address = {Dublin, Ireland (Virtual)},
  year = {2021},
  month = {23-27 Aug.},
  publisher = {IEEE},
}

Iterative Decoding for Compositional Generalization in Transformers

L. Ruiz, J. Ainslie, and S. Ontañón

arXiv:2110.04169 [cs.LG], 23-27 aug. 2021

@article{ruiz2021iterative,
  title = {Iterative Decoding for Compositional Generalization in Transformers},
  author = {Ruiz, L. and Ainslie, J. and Onta{\~n}{\'o}n, S.},
  journal = {arXiv:2110.04169 [cs.LG]},
  year = {2021},
}

2020

Invariance-Preserving Localized Activation Functions for Graph Neural Networks

L. Ruiz, F. Gama, A. G. Marques, and A. Ribeiro

Trans. Signal Process., 23-27 aug. 2020

@article{ruiz19-inv,
  author = {Ruiz, L. and Gama, F. and G. Marques, A. and Ribeiro, A.},
  title = {Invariance-Preserving Localized Activation Functions for Graph Neural Networks},
  journal = {Trans. Signal Process.},
  volume = {68},
  pages = {127--141},
  year = {2020},
  publisher = {IEEE},
}

Gated Graph Recurrent Neural Networks

L. Ruiz, F. Gama, and A. Ribeiro

Trans. Signal Process., 23-27 aug. 2020

@article{ruiz2020grnns,
  author = {Ruiz, L. and Gama, F. and Ribeiro, A.},
  journal = {Trans. Signal Process.},
  title = {Gated Graph Recurrent Neural Networks},
  volume = {68},
  pages = {6303--6318},
  year = {2020},
  publisher = {IEEE},
}

The Graphon Fourier Transform

L. Ruiz, L. F. O. Chamon, and A. Ribeiro

In 45th ICASSP, 4-8 may 2020

@inproceedings{ruiz2019graphon,
  title = {The {G}raphon {F}ourier {T}ransform},
  author = {Ruiz, L. and Chamon, L. F. O. and Ribeiro, A.},
  booktitle = {45th ICASSP},
  pages = {5660-5664},
  address = {Barcelona, Spain (Virtual)},
  year = {2020},
  month = {4-8 May},
  organization = {IEEE},
}

Graphon Neural Networks and the Transferability of Graph Neural Networks

L. Ruiz, L. F. O. Chamon, and A. Ribeiro

In 34th NeurIPS, 6-12 dec. 2020

@inproceedings{ruiz20-transf,
  title = {Graphon Neural Networks and the Transferability of Graph Neural Networks},
  author = {Ruiz, L. and Chamon, L. F. O. and Ribeiro, A.},
  booktitle = {34th NeurIPS},
  address = {Vancouver, BC (Virtual)},
  year = {2020},
  month = {6-12 Dec.},
  publisher = {NeurIPS Foundation},
}

2019

Gated Graph Convolutional Recurrent Neural Networks (Best Paper Award)

L. Ruiz, F. Gama, and A. Ribeiro

In 27th EUSIPCO, 2-6 sep. 2019

@inproceedings{ruiz18-grnns,
  title = {Gated Graph Convolutional Recurrent Neural Networks (Best Paper Award)},
  author = {Ruiz, L. and Gama, F. and Ribeiro, A.},
  booktitle = {27th EUSIPCO},
  pages = {1-5},
  address = {A Coruna, Spain},
  year = {2019},
  month = {2-6 Sep.},
  organization = {EURASIP},
}