Luana Ruiz

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

prof_pic.jpg

Wyman Park Building N452

Baltimore, MD 21211

lrubini1-at-jh-dot-edu

I am an assistant professor with the Department of Applied Mathematics and Statistics at Johns Hopkins University, the Mathematical Institute for Data Science (MINDS), and the Data Science and Artificial Intelligence Institute (DSAI). 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 research is at the intersection of machine learning, signal processing, and network science, with a focus on developing scalable algorithms for learning on non-Euclidean domains such as graphs and data manifolds. I work on large-scale graph information processing, graph neural networks (GNNs), and the theoretical limits of transferability and generalization in graph-based learning. I am also interested in physics-informed machine learning, manifold learning, and combinatorial optimization, especially as they relate to the structure and dynamics of complex systems.

You can find my CV here, and a selected list of publications is included below.

Selected publications

2025

  1. Local Distance-Preserving Node Embeddings and Their Performance on Random Graphs
    My Le, Luana Ruiz, and Souvik Dhara
    arXiv preprint arXiv:2504.08216, 2025
  2. Improved Image Classification with Manifold Neural Networks
    Caio F. Deberaldini Netto, Zhiyang Wang, and Luana Ruiz
    In 50th ICASSP, 2025
  3. A Generative Model for Controllable Feature Homophily in Graphs
    H. Wang, R. Ma, G. Mateos, and L. Ruiz
    arXiv preprint arXiv:2509.23230, 2025
    Submitted to ICASSP 2026
  4. Graph Semi-Supervised Learning for Point Classification on Data Manifolds
    Caio F. Deberaldini Netto, Zhiyang Wang, and Luana Ruiz
    arXiv preprint arXiv:2506.12197, 2025
    Submitted to IEEE TSP
  5. Subsampling Graphs with GNN Performance Guarantees
    M. Jain, S. Jegelka, I. Karmarkar, L.a Ruiz, and E. Vitercik
    arXiv preprint arXiv:2502.16703, 2025
  6. A Local Graph Limits Perspective on Sampling-Based Graph Neural Networks
    Yeganeh Alimohammadi, Luana Ruiz, and Amin Saberi
    In 2025 IEEE International Symposium on Information Theory (ISIT), 2025
  7. Dirichlet Meets Horvitz and Thompson: Estimating Homophily in Large Graphs via Sampling
    H. Ajorlou, L. Ruiz, and G. Mateos
    In 59th Asilomar Conf. on Sig. and Syst., 2025
    To appear
  8. Graph Sampling for Scalable and Expressive Graph Neural Networks on Homophilic Graphs
    Haolin Li, Haoyu Wang, and Luana Ruiz
    In 33rd European Signal Processing Conference (EUSIPCO), 2025

2024

  1. Stability to Deformations of Manifold Filters and Manifold Neural Networks
    Z. Wang, L. Ruiz, and A. Ribeiro
    IEEE Trans. Signal Process., 2024
  2. Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs
    Z. Wang, L. Ruiz, and A. Ribeiro
    IEEE Trans. Signal Process., 2024
  3. A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs (Spotlight)
    T. Le, L. Ruiz, and S. Jegelka
    In 12th ICLR, 7–11 may 2024
  4. A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs
    L. Ruiz, N. Huang, and S. Villar
    In 49th ICASSP, 14-19 apr. 2024

2023

  1. Learning by Transference: Training Graph Neural Networks on Growing Graphs
    J. Cervino, L. Ruiz, and A. Ribeiro
    IEEE Trans. Signal Process., 14-19 apr. 2023
  2. Transferability Properties of Graph Neural Networks
    L. Ruiz, L. F. O. Chamon, and A. Ribeiro
    IEEE Trans. Signal Process., 14-19 apr. 2023
  3. Graph Neural Tangent Kernel: Convergence on Large Graphs
    S. Krishnagopal, and L. Ruiz
    In 40th ICML, 23-29 jul. 2023

2021

  1. Graph Neural Networks: Architectures, Stability and Transferability
    L. Ruiz, F. Gama, and A. Ribeiro
    Proc. IEEE, 23-29 jul. 2021
  2. Graphon Signal Processing
    L. Ruiz, L. F. O. Chamon, and A. Ribeiro
    Trans. Signal Process., 23-29 jul. 2021
  3. Stability of Neural Networks on Riemannian Manifolds (Best Paper Award)
    Z. Wang, L. Ruiz, and A. Ribeiro
    In 29th EUSIPCO, 23-27 aug. 2021
  4. Iterative Decoding for Compositional Generalization in Transformers
    L. Ruiz, J. Ainslie, and S. Ontañón
    arXiv:2110.04169 [cs.LG], 23-27 aug. 2021

2020

  1. Gated Graph Recurrent Neural Networks
    L. Ruiz, F. Gama, and A. Ribeiro
    Trans. Signal Process., 23-27 aug. 2020
  2. The Graphon Fourier Transform
    L. Ruiz, L. F. O. Chamon, and A. Ribeiro
    In 45th ICASSP, 4-8 may 2020
  3. 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

2019

  1. Gated Graph Convolutional Recurrent Neural Networks (Best Paper Award)
    L. Ruiz, F. Gama, and A. Ribeiro
    In 27th EUSIPCO, 2-6 sep. 2019