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Sushovan Majhi

dblp:255/5256 · DBLP profile ↗
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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

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 · 83% Graph algorithms and graph theory · 17%

Topics — the 6 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational geometry
topological data analysis
1.822026
Lower Bounding the Gromov-Hausdorff Distance in Metric Graphs · SoCG 2026
Demystifying Latschev's Theorem: Manifold Reconstruction from Noisy Data · SoCG 2024
Computational geometry › distance measures
gromov-hausdorff distance
1.012026
Lower Bounding the Gromov-Hausdorff Distance in Metric Graphs · SoCG 2026
Computational geometry
metric geometry
1.012026
Lower Bounding the Gromov-Hausdorff Distance in Metric Graphs · SoCG 2026
Graph algorithms and graph theory › metric graph theory
metric graphs
1.012026
Lower Bounding the Gromov-Hausdorff Distance in Metric Graphs · SoCG 2026
Computational geometry › geometric modeling and processing › point cloud analysis › geometric reconstruction
manifold reconstruction
0.812024
Demystifying Latschev's Theorem: Manifold Reconstruction from Noisy Data · SoCG 2024
Computational geometry › topological data analysis
persistent homology
0.212024
Demystifying Latschev's Theorem: Manifold Reconstruction from Noisy Data · SoCG 2024

Methods — techniques the papers use, named apart from their topics

topology · 1.0metric geometry · 1.0sectional curvature · 0.8gromov-hausdorff distance · 0.8
YearPublicationVenuePosition
2026 Lower Bounding the Gromov-Hausdorff Distance in Metric Graphs
abstract
Let $G$ 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
SoCG2
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 Data
abstract
For 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
SoCG1
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