My research interests span across information visualization, network visual analysis, and interactions. I am especially interested in the design of scalable techniques or systems that enable interactive exploration of large or complex datasets. In the past, I have worked with parallel coordinates, multiple coordinated views, multidimensional projection, and graph drawing.
Short CV
- Postdoctoral Researcher, Inria, AVIZ / Université Paris-Saclay, LISN (12/2019 — 05/2022)
- Advised by Jean-Daniel Fekete
- Ph.D. in Computer Science, LaBRI / Université de Bordeaux (09/2016 — 11/2019)
- Thesis: “Addressing scaling challenges in interactive exploratory visualization with abstraction and spatial distortion”", advised by David Auber and Romain Bourqui
- MSc in Computer Science at ENSEIRB-Matméca with Honors (2012 – 2015)
More here [PDF].
News
2022-07-15 | Our paper on decision-making for progressive bar charts was accepted to IEEE VIS 2022. |
2020-10-26 | Our entry for the VAST Challenge 2020 Mini-Challenge 1 received an award. Take a look at the entry here. |
2019-12-01 | Started as a postdoc at Aviz, Inria Saclay. |
2019-11-26 | Defended my thesis (manuscript). |
2019-07-04 | An extended version of our hierarchical parallel coordinate paper was accepted by the Big Data Research journal. |
Recent publications
Studying Early Decision Making with Progressive Bar Charts
IEEE Transactions on Visualization and Computer Graphics, 2023
VAST 2020 Contest Challenge: GraphMatchMaker: Visual Analytics for Graph Comparison and Matching
IEEE Computer Graphics and Applications, 2021
HiePaCo: Scalable hierarchical exploration in abstract parallel coordinates under budget constraints
Big Data Research, 2019
Projects
GraphletMatchMaker
Set of tools developed as part of our submission to the Visual Analytics Science and Technology (VAST) Challenge 2020 (Mini Challenge 1).
LonGenEx (DALIA)
Visual exploration tool for longitudinal gene expression data from vaccine trials
CoPHI
Interactive web application based on abstract parallel coordinates for exploring multidimensional data.