Arka Mallick
Papers
1
Total Citations
2
H-Index
1
About
Arka Mallick’s research lies at the intersection of underwater robotics and computer vision, with a focus on enhancing perception capabilities for autonomous systems operating in challenging environments. His most-cited work, “Forward-Looking Sonar Patch Matching: Modern CNNs, Ensembling, and Uncertainty” (2021), addresses a critical bottleneck in sonar-based localization—the lack of robust patch-matching techniques. By systematically evaluating modern convolutional neural networks, ensembling methods, and uncertainty quantification, Mallick provides a foundational framework for improving sonar image correspondence, a key enabler for tasks like simultaneous localization and mapping (SLAM) and object tracking in murky waters. Though his citation count is still growing, this work has already drawn attention from researchers seeking to bridge the gap between deep learning and underwater perception. Mallick’s contributions are particularly notable for their practical relevance: as underwater robots become more prevalent in ocean exploration and infrastructure inspection, his methods offer a path toward more reliable autonomous navigation. His research underscores a commitment to solving real-world problems, making him a promising voice in the field of marine robotics.
Research Focus
Key Achievements
Top Papers
- 1