Demo Data from EDBT 2026 / reviewers in the wild / expert
Mattéo Clémot
dblp:356/7802
· DBLP profile ↗
3ranked-venue papers
2as first author
3since 2021 · last 2026
0009-0000-2524-0244ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author · 2 since 2021Theory of computation · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
2 papers |
Computational geometry · 100% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
dimensionality reduction |
1.0 | 1 | 2026 | Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality Reduction · IEEE Trans. Vis. Comput. Graph. 2026 |
Computational geometry › topological data analysis
alpha complex |
0.9 | 1 | 2025 | When Alpha-Complexes Collapse onto Codimension-1 Submanifolds · SoCG 2025 |
Computational geometry › triangulation
delaunay triangulation |
0.9 | 1 | 2025 | When Alpha-Complexes Collapse onto Codimension-1 Submanifolds · SoCG 2025 |
Computational geometry › triangulation
restricted delaunay complex |
0.9 | 1 | 2025 | When Alpha-Complexes Collapse onto Codimension-1 Submanifolds · SoCG 2025 |
Computational geometry › geometric modeling and processing › point cloud analysis › geometric reconstruction
shape reconstruction |
0.9 | 1 | 2025 | When Alpha-Complexes Collapse onto Codimension-1 Submanifolds · SoCG 2025 |
Computational geometry › topological data analysis
persistent homology |
0.3 | 1 | 2026 | Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality Reduction · IEEE Trans. Vis. Comput. Graph. 2026 |
Methods — techniques the papers use, named apart from their topics
topological autoencoder · 2.0persistent homology · 2.0cascade distortion · 2.0vertical collapse · 0.9alpha-complex simplification · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality ReductionabstractThis paper presents a novel topology-aware dimensionality reduction approach aiming at accurately visualizing the cyclic patterns present in high dimensional data. To that end, we build on the Topological Autoencoders (TopoAE) (Moor et al., 2020) formulation. First, we provide a novel theoretical analysis of its associated loss and show that a zero loss indeed induces identical persistence pairs (in high and low dimensions) for the 0-dimensional persistent homology ($\text{PH}^{0}$) of the Rips filtration. We also provide a counter example showing that this property no longer holds for a naive extension of TopoAE to $\text{PH}^{d}$ for $d\geq 1$. Based on this observation, we introduce a novel generalization of TopoAE to 1-dimensional persistent homology ($\text{PH}^{1}$), called TopoAE++, for the accurate generation of cycle-aware planar embeddings, addressing the above failure case. This generalization is based on the notion of cascade distortion, a new penalty term favoring an isometric embedding of the 2-chains filling persistent 1-cycles, hence resulting in more faithful geometrical reconstructions of the 1-cycles in the plane. We further introduce a novel, fast algorithm for the exact computation of $\text{PH}^{}$ for Rips filtrations in the plane, yielding improved runtimes over previously documented topology-aware methods. Our method also achieves a better balance between the topological accuracy, as measured by the Wasserstein distance, and the visual preservation of the cycles in low dimensions. Mattéo Clémot, Julie Digne, Julien Tierny |
IEEE Trans. Vis. Comput. Graph. | 1 |
| 2025 | When Alpha-Complexes Collapse onto Codimension-1 SubmanifoldsabstractGiven a finite set of points P sampling an unknown smooth surface ℳ ⊆ ℝ³, our goal is to triangulate ℳ based solely on P. Assuming ℳ is a smooth orientable submanifold of codimension 1 in ℝ^d, we introduce a simple algorithm, Naive Squash, which simplifies the α-complex of P by repeatedly applying a new type of collapse called vertical relative to ℳ. Naive Squash also has a practical version that does not require knowledge of ℳ. We establish conditions under which both the naive and practical Squash algorithms output a triangulation of ℳ. We provide a bound on the angle formed by triangles in the α-complex with ℳ, yielding sampling conditions on P that are competitive with existing literature for smooth surfaces embedded in ℝ³, while offering a more compartmentalized proof. As a by-product, we obtain that the restricted Delaunay complex of P triangulates ℳ when ℳ is a smooth surface in ℝ³ under weaker conditions than existing ones. Dominique Attali, Mattéo Clémot, Bianca B. Dornelas, André Lieutier |
SoCG | 2 |
| 2023 | Neural skeleton: Implicit neural representation away from the surface
Mattéo Clémot, Julie Digne |
Comput. Graph. | 1 |