About me
I am a postdoctoral researcher at the Technical University of Denmark, funded by the Danish Data Science Academy.
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.
These interests into a geometric perspective started during my PhD, where I worked on graph-based representations of data. For example, I investigated temporal graph-based recommender systems and a geometric take on latent representation interpolation in normalising flows using hyperspheres.
Prior to that, I focused on data (or information) visualization, with an emphasis on making high-dimensional data understandable through interactive two-dimensional 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
- 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
- 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
Background
- Postdoc researcher (2024─present)
- Postdoc researcher (2023─2024)
- Postdoc researcher (2021─2023)
- Machine Learning Group with Ulf Brefeld
- Institute of Information Systems, Leuphana University
- Visiting researcher (2019─2020, 15 months)
- Machine Learning Group
- Supervised by Ulf Brefeld
- Ph.D. in Computer Science (2016─2021)
- "Learning in non-Euclidean Domains"
- Institute of Computing, University of Campinas
- Supervised by Ricardo da S. Torres
- Visiting researcher (2015─2016, 3 months)
- Scientific Visualization and Computer Graphics
- Supervised by Alex Telea
- M.Sc. in Computer Science (2014─2016)
- "Understanding Interactive Multidimensional Projections"
- Institute of Mathematical and Computer Sciences, University of São Paulo
- Supervised by Fernando Paulovich
- B.Sc. in Computer Science (2010─2013)
- "Multidimensional Projections based on Convex Hulls"
- Institute of Mathematical and Computer Sciences, University of São Paulo