Research

My main research interests are on unsupervised (or self-supervised) machine learning, particularly with geometrically-inspired representations. I investigate how the geometry of representations learned by different models can be either enforced or understood through the formalism of Riemannian manifolds.

During my PhD, I worked on one of the first temporal graph neural networks for recommender systems. In generative modeling, I investigated how to improve the generation of data via interpolations in the base space of normalising flows.

Prior to my PhD research, my research interests focused on data visualization, with an emphasis on making high-dimensional data understandable through interactive 2D scatterplots.

Teaching

Most of my teaching experience is at graduate (MSc. level) courses, but I have worked as a teaching assistant for BSc. courses.

When opportunity allows it, I also try to come up with alternative ways to help students understand some concepts. Particularly for a Deep Learning course, I have designed a visualization tool for convolutions. I would be happy to hear if you found it useful or have feedback.

Publications

Towards General Geometries for Embedding Knowledge Graphs
- S. G. Fadel, T. Paulsen, S. Mair
- ICML/ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling, 2024
- HTML
Self-Supervised Siamese Autoencoders
- F. Baier, S. Mair, S. G. Fadel
- Advances in Intelligent Data Analysis XXII, 2024
- arXiv, DOI
Exploring the Poincaré Ellipsis
- S. G. Fadel*, T. Paulsen*, U. Brefeld
- International Workshop on Mining and Learning with Graphs (ECML/PKDD), 2023
Studying the Propagation of Information in VAE Decoders
- Y. Rudolph, S. G. Fadel, S. Mair, U. Brefeld
- Northern Lights Deep Learning Workshop, 2022
Principled Interpolation in Normalizing Flows
- S. G. Fadel*, S. Mair*, R. da S. Torres, U. Brefeld
- Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2021
- slides, poster, arXiv, DOI
Contextual Movement Models Based on Normalizing Flows
- S. G. Fadel*, S. Mair*, R. da S. Torres, U. Brefeld
- AStA Advances in Statistical Analysis, Special Issue on Statistics in Sports, 2021
- DOI
Efficient Normalizing Flows to Polytopes
- S. G. Fadel, S. Mair, R. da Silva Torres, U. Brefeld
- Northern Lights Deep Learning Workshop, 2020
An Appropriate Prior Distribution for Interpolating Latent Samples in Flow-based Generative Models (abstract)
- S. G. Fadel, S. Mair, R. da Silva Torres, U. Brefeld
- Northern Lights Deep Learning Workshop, 2020
Neural Relational Inference for Disaster Multimedia Retrieval
- S. G. Fadel, R. da S. Torres
- Multimedia Tools and Applications, 2020
- DOI
Link Prediction in Dynamic Graphs for Recommendation
- S. G. Fadel, R. da S. Torres
- NeurIPS Workshop on Relational Representation Learning, 2018
- arXiv
Graph-based Early Fusion for Flood Detection
- R. de O. Werneck, Í. C. Dourado, S. G. Fadel, S. Tabbone, R. da S. Torres
- IEEE International Conference on Image Processing (ICIP), 2018
- DOI
UPDis: A User-Assisted Projection Technique for Distance Information
- T. A. T. T. Neves, S. G. Fadel, G. M. Hilasaca, F. M. Fatore, F. V. Paulovich
- Information Visualization, 2018
- DOI
Exploiting ConvNet Diversity for Flooding Identification
- K. Nogueira, S. G. Fadel, Í. C. Dourado, R. de O. Werneck, J. A. V. Muñoz, O. A. B. Penatti, R. T. Calumby, L. T. Li, J. A. dos Santos, R. da S. Torres
- IEEE Geoscience and Remote Sensing Letters, 2018
- DOI
Visualizing the Hidden Activity of Artificial Neural Networks
- P. E. Rauber, S. G. Fadel, A. X. Falcão, A. C. Telea
- IEEE Transactions on Visualization and Computer Graphics, 2016
- DOI
LoCH: A Neighborhood-based Multidimensional Projection Technique for High-Dimensional Sparse Spaces
- S. G. Fadel, F. M. Fatore, F. S. L. G. Duarte, F. V. Paulovich
- Neurocomputing, 2015
- DOI
On the Effectiveness of User Manipulation in Multidimensional Projections
- S. G. Fadel, F. V. Paulovich
- Conference on Graphics, Patterns and Images, XXVIII; Workshop on Visual Analytics, Information Visualization and Scientific Visualization, 2015
Nmap: A Novel Neighborhood Preservation Space-filling Algorithm
- F. S. L. G. Duarte, F. Sikansi, F. M. Fatore, S. G. Fadel, F. V. Paulovich
- IEEE Transactions on Visualization and Computer Graphics, 2014
- DOI