Sameera Ponda

1.2k citations
22 papers · 782 · h-index 13

Impact in

Papers in

Sameera Ponda

22 papers receiving 761 citations

Peers

Sameera Ponda
Comparison fields: 5 of 74
  • Computer Networks and Communications 399
  • Aerospace Engineering 338
  • Computer Vision and Pattern Recognition 204
  • Structural Biology 9
  • Artificial Intelligence 185
Replace C.R. Weisbin with:
C.R. Weisbin United States
Chen Huang China
Stefan Bieniawski United States
Patrick Vandewalle Belgium
Oleg Burdakov Sweden
Yunfeng Shao China
Gianluca Furano Netherlands
Hailiang Xiong China
J. Balaram United States
Niklas Wahlström Sweden
Sameera Ponda relative to C.R. Weisbin United States C.R. Weisbin's profile →
Citations per field
00.5×10×12.7×
C.R. Weisbin · 1×
Citations per year

Countries citing papers authored by Sameera Ponda

Since Specialization
Citations

This map shows the geographic impact of Sameera Ponda's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sameera Ponda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sameera Ponda more than expected).

Fields of papers citing papers by Sameera Ponda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sameera Ponda. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sameera Ponda. The network helps show where Sameera Ponda may publish in the future.

Co-authors

The 23 scholars most cited alongside Sameera Ponda, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sameera Ponda Line = papers co-authored together Sameera Ponda links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020164
2 2009120
3 201085
4 201265
5 201056
6 201150
7 201143
8 201338
9 201229
10 201227
11 201221
12 201021
13 201721
14 20139
15 20118
16 20118
17 20118
18 20122
19
Planning under Uncertainty using Nonparametric Bayesian Models
20122
20 20112

About Sameera Ponda

Sameera Ponda is a scholar working on Computer Networks and Communications, Aerospace Engineering, Artificial Intelligence, Mechanical Engineering and Management Science and Operations Research, having authored 22 papers that have together received 782 indexed citations. Recurring topics across this work include Distributed Control Multi-Agent Systems (12 papers), UAV Applications and Optimization (5 papers), Modular Robots and Swarm Intelligence (4 papers), Optimization and Search Problems (3 papers), Robotic Path Planning Algorithms (3 papers), Target Tracking and Data Fusion in Sensor Networks (3 papers), Opportunistic and Delay-Tolerant Networks (3 papers) and Robotics and Sensor-Based Localization (2 papers). The work is most often cited by research in Computer Networks and Communications (399 citations), Aerospace Engineering (338 citations), Computer Vision and Pattern Recognition (204 citations), Structural Biology (9 citations) and Artificial Intelligence (185 citations). Sameera Ponda has collaborated with scholars based in United States, South Korea and India. Frequent co-authors include Jonathan P. How, Luke B. Johnson, Han‐Lim Choi, Emilio Frazzoli, Jun Gong, Marc G. Bellemare, Ziyu Wang, Subhodeep Moitra, Marlos C. Machado and Salvatore Candido. Their work appears in journals such as Optics Express, IEEE Journal on Selected Areas in Communications, Nature, Unmanned Systems and Journal of Aerospace Information Systems.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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