Papers
11
Total Citations
60
H-Index
4
About
Kaushik Mondal is a leading researcher in distributed computing and mobile robotics, specializing in fault-tolerant algorithms for autonomous robot swarms. His work centers on fundamental coordination problems—gathering, dispersion, and convergence—with a focus on weak capabilities (e.g., limited sensing, silent communication) and adversarial conditions like crash or Byzantine faults. Mondal’s major contributions include pioneering the “Distance-2-Dispersion” problem, which imposes stricter constraints on robot placement, and developing optimal dispersion strategies for anonymous ring networks under weak Byzantine failures. His 2018 paper on gathering with weak multiplicity detection in crash-prone environments has garnered 14 citations, while his 2023 work on distance-2-dispersion has already reached 11 citations, reflecting growing impact. Notably, his research demonstrates that even simple, silent robots can achieve complex collaborative tasks, such as dispersion with termination guarantees. Mondal’s achievements include fast deterministic gathering algorithms that leverage many robots for efficiency, and his work on fault-tolerant gathering has been recognized for advancing theoretical foundations in distributed robotics. His publications in top venues (e.g., ICDCN, OPODIS) and sustained citation growth underscore his influence on the field.
Research Focus
Key Achievements
Top Papers
- 1
- 2Distance-2-Dispersion: Dispersion with Further Constraints11 citations · 2023
- 3
- 4Distance-2-Dispersion with Termination by a Strong Team5 citations · 2024
- 5Collaborative Dispersion by Silent Robots4 citations · 2022
- 6Collaborative dispersion by silent robots4 citations · 2024
- 7
- 8
- 9
- 10Convergence of Even Simpler Robots without Position Information2 citations · 2017