From 1st October 2019 to 31st September 2020, I was Research Engineer in the BKB team of the LaBRI.
I obtained my Master degree in Computer Sciences (Software Engineering) in 2019 at the University of Bordeaux.
Information Visualization, Deep Learning, Graph Visualisation, Automated Evaluation
My research interests are centered around Information Visualization (VIS) and Artificial Intelligence (AI) (Machine Learning [ML] and Deep Learning [DL]) ; more specifically AI applications for VIS (AI4VIS).
With the outbreak of IoT, the amount and complexity of collected data keeps increasing. This makes their
efficient restitution even more important and many visualization techniques that yesterday enabled their
exploration are now outdated. Hence, the VIS community keeps on defining new visualizations and
guidelines that scale to these complex data.
In this context, the objective of my research is to propose new solutions, based on Deep Learning, to automatically generate and/or evaluate visualizations. For these two tasks, the heterogeneity of modern data makes it difficult to design efficient algorithms/protocols that can be generalized to any datum. On the other hand, DL techniques require large amounts of data to train and become efficient. Leveraging Deep Learning techniques in the context of VIS could enable to explore more exhaustively the parameters space of visualization techniques ; and define automated and reproducible criteria for VIS evaluations.
Leveraging the background of the BKB team of LaBRI, most applications of my AI4VIS researches are dedicated to Graph Visualization.
Most PDF are accessible on [loanngio]
|Overlap Removal by Stochastic Gradient Descent with(out) Shape Awareness IEEE Transactions on Visualization and Computer Graphics (TVCG) 2024 Extended version 10.1109/TVCG.2024.3351479
|Guaranteed Visibility in Scatterplots with Tolerance IEEE Visualization (VIS 2023) Oct 2023 10.1109/TVCG.2023.3326596
|Toward Efficient Deep Learning for Graph Drawing (DL4GD) IEEE Transactions on Visualization and Computer Graphics (TVCG) 2022 Extended version 10.1109/TVCG.2022.3222186
|FORBID: Fast Overlap Removal By stochastic gradIent Descent for Graph Drawing International Symposium on Graph Drawing and Network Visualization (GD2022) 2022 10.1007/978-3-031-22203-0_6 Best Paper Award (Track 2)
|Color and Shape efficiency for outlier detection from automated to user evaluation Visual Informatics 2022 10.1016/j.visinf.2022.03.001
|Deep Neural Network for DrawiNg Networks, (DNN)² International Symposium on Graph Drawing and Network Visualization (GD2021) Sep, 2021 10.1007/978-3-030-92931-2_27
|Analysis of Deep Neural Networks Correlations with Human Subjects on a Perception Task International Conference Information Visualisation Jul, 2021 10.1109/IV53921.2021.00029
|Toward automatic comparison of visualization techniques: Application to graph visualization Visual Informatics 2020 10.1016/j.visinf.2020.04.002