VLDB 2026 Research / reviewers in the wild / expert
Sushovan Majhi
dblp:255/5256
· DBLP profile ↗
6ranked-venue papers
4as first author
6since 2021 · last 2026
0000-0001-8689-4321ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 4 · 3 first-author · 4 since 2021Theory of computation · 2 · 1 first-author · 2 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Lower Bounding the Gromov-Hausdorff Distance in Metric GraphsabstractLet $G$ be a finite, connected metric graph and let $X\subseteq G$ be a subset. If $X$ is sufficiently dense in $G$, we show that the Gromov--Hausdorff distance matches the Hausdorff distance, namely $d_\gh(G,X)=d_\h(G,X)$. When the metric graph is the circle $G=S^1$ with circumference $2π$, a recent study established the equality $d_\gh(S^1,X)=d_\h(S^1,X)$ whenever $d_\gh(S^1,X)<\fracπ{6}$. Our results relax this hypothesis to $d_\gh(S^1,X)<\fracπ{3}$, and furthermore, we show that the constant $\fracπ{3}$ is the best possible. We lower bound the Gromov--Hausdorff distance $d_\gh(G,X)$ by the Hausdorff distance $d_\h(G,X)$ via a simple topological obstruction: the existence of a possibly discontinuous function $f\colon G \to X$ with too small distortion contradicts the connectedness of $G$. Henry Adams, Sushovan Majhi, Fedor Manin, Ziga Virk, Nicolò Zava |
SoCG | 2 |
| 2026 | Hausdorff vs Gromov-Hausdorff Distances
Henry Adams, Florian Frick, Sushovan Majhi, Nicholas McBride |
Discret. Comput. Geom. | 3 |
| 2025 | Demystifying Latschev's Theorem: Manifold Reconstruction from Noisy Data
Sushovan Majhi |
Discret. Comput. Geom. | 1 |
| 2024 | Demystifying Latschev's Theorem: Manifold Reconstruction from Noisy DataabstractFor a closed Riemannian manifold $\mathcal{M}$ and a metric space $S$ with a small Gromov$\unicode{x2013}$Hausdorff distance to it, Latschev's theorem guarantees the existence of a sufficiently small scale $β>0$ at which the Vietoris$\unicode{x2013}$Rips complex of $S$ is homotopy equivalent to $\mathcal{M}$. Despite being regarded as a stepping stone to the topological reconstruction of Riemannian manifolds from a noisy data, the result is only a qualitative guarantee. Until now, it had been elusive how to quantitatively choose such a proximity scale $β$ in order to provide sampling conditions for $S$ to be homotopy equivalent to $\mathcal{M}$. In this paper, we prove a stronger and pragmatic version of Latschev's theorem, facilitating a simple description of $β$ using the sectional curvatures and convexity radius of $\mathcal{M}$ as the sampling parameters. Our study also delves into the topological recovery of a closed Euclidean submanifold from the Vietoris$\unicode{x2013}$Rips complexes of a Hausdorff close Euclidean subset. As already known for Čech complexes, we show that Vietoris$\unicode{x2013}$Rips complexes also provide topologically faithful reconstruction guarantees for submanifolds. Sushovan Majhi |
SoCG | 1 |
| 2024 | Approximating Gromov-Hausdorff distance in Euclidean space
Sushovan Majhi, Jeffrey Scott Vitter, Carola Wenk |
Comput. Geom. | 1 |
| 2024 | Distance measures for geometric graphs
Sushovan Majhi, Carola Wenk |
Comput. Geom. | 1 |