Research

My main research interests are on unsupervised machine learning, particularly with geometrically-inspired representations. During my PhD, I worked on problems ranging from temporal graphs to generative modeling. Prior to my PhD research, my research interests focused on data visualization.

Teaching

I have been regularly teaching courses every semester since 2020. I mostly teach for graduate (MSc. level) courses, but in the past 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

• Exploring the Poincaré Ellipsis
  • S. G. Fadel*, T. Paulsen*, U. Brefeld
  • International Workshop on Mining and Learning with Graphs (ECML/PKDD), 2023
• Self-Supervised Siamese Autoencoders
  • F. Baier, S. Mair, S. G. Fadel
  • Preprint, 2023
  • arXiv
• 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
  • slidesposterarXivDOI
• 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 (2023─present)

• Division of Statistics and Machine Learning with Fredrik Lindsten
• Department of Computer and Information Science, Linköping University

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 (2017─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