Demo Data from EDBT 2026 / reviewers in the wild / expert
Zahed Rahmati
dblp:97/8408
· DBLP profile ↗
14ranked-venue papers
9as first author
2since 2021 · last 2026
0000-0002-6942-3453ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 7 · 6 first-authorGraphics, computer vision, multimedia, augmented reality and games · 5 · 3 first-author · 1 since 2021Artificial intelligence and machine learning · 2 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 2 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1
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
1 paper |
Computational geometry · 67% Algorithms and data structures · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithms and data structures › similarity search › nearest neighbor search
all nearest neighbors |
0.2 | 1 | 2013 | Kinetic data structures for all nearest neighbors and closest pair in the plane · SoCG 2013 |
Computational geometry › proximity problems
closest pair |
0.2 | 1 | 2013 | Kinetic data structures for all nearest neighbors and closest pair in the plane · SoCG 2013 |
Computational geometry › geometric data structures
kinetic data structures |
0.2 | 1 | 2013 | Kinetic data structures for all nearest neighbors and closest pair in the plane · SoCG 2013 |
Methods — techniques the papers use, named apart from their topics
kinetic data structures · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | HBPP: A transformer-inspired architecture for physics-informed learning of parametric PDEs
Mahdi Safari, Zahed Rahmati, Mostafa Abbaszadeh, Mostafa Shamsi |
Neurocomputing | 2 |
| 2022 | Corrigendum to "Approximating the minimum closest pair distance and nearest neighbor distances of linearly moving points" [Comput. Geom. 60 (2017) 2-7]
Timothy M. Chan, Zahed Rahmati |
Comput. Geom. | 2 |
| 2020 | Simplified Emanation Graphs: A Sparse Plane Spanner with Steiner Points
Bardia Hamedmohseni, Zahed Rahmati, Debajyoti Mondal |
SOFSEM | 2 |
| 2020 | RAC drawings in subcubic area
Zahed Rahmati, Fatemeh Emami |
Inf. Process. Lett. | 1 |
| 2019 | Kinetic k-Semi-Yao graph and its applications
Zahed Rahmati, Mohammad Ali Abam, Valerie King, Sue Whitesides |
Comput. Geom. | 1 |
| 2018 | A Clustering-Based Approach to Kinetic Closest Pair
Zahed Rahmati, Timothy M. Chan |
Algorithmica | 1 |
| 2018 | An improved approximation algorithm for the discrete Fréchet distance
Timothy M. Chan, Zahed Rahmati |
Inf. Process. Lett. | 2 |
| 2017 | Approximating the minimum closest pair distance and nearest neighbor distances of linearly moving points
Timothy M. Chan, Zahed Rahmati |
Comput. Geom. | 2 |
| 2015 | A simple, faster method for kinetic proximity problems
Zahed Rahmati, Mohammad Ali Abam, Valerie King, Sue Whitesides, Alireza Zarei |
Comput. Geom. | 1 |
| 2014 | Kinetic Reverse k-Nearest Neighbor Problem
Zahed Rahmati, Valerie King, Sue Whitesides |
IWOCA | 1 |
| 2014 | (Reverse) k-nearest neighbors for moving objectsabstractWe introduce a new, simple method for answering reverse and nearest neighbor queries for moving objects, e.g., mobile players in a multiplayer game, where a trajectory is known to the system for each object, at least in the short-term. See [Rahmati 2014]. Zahed Rahmati, Valerie King, Sue Whitesides |
MIG | 1 |
| 2013 | Kinetic data structures for all nearest neighbors and closest pair in the planeabstractThis paper presents a kinetic data structure (KDS) for solutions to the all nearest neighbors problem and the closest pair problem in the plane. For a set P of n moving points where the trajectory of each point is an algebraic function of constant maximum degree s, our kinetic algorithm uses O(n) space and O(n log n) preprocessing time, and processes O(n2β22s+2(n)log n) events with total processing time O(n2β22s+2(n)log2 n), where βs(n) is an extremely slow-growing function. In terms of the KDS performance criteria, our KDS is efficient, responsive (in an amortized sense), and compact. Zahed Rahmati, Valerie King, Sue Whitesides |
SoCG | 1 |
| 2012 | Kinetic and Stationary Point-Set Embeddability for Plane Graphs
Zahed Rahmati, Sue Whitesides, Valerie King |
GD | 1 |
| 2011 | Kinetic Euclidean Minimum Spanning Tree in the Plane
Zahed Rahmati, Alireza Zarei |
IWOCA | 1 |