Sovan Biswas
Papers
1
Total Citations
3
H-Index
1
About
Sovan Biswas is a forward-thinking researcher at the intersection of artificial intelligence and industrial automation, with a core focus on unsupervised learning, defect detection, and sensor fusion. His most prominent contribution, "Unsupervised Welding Defect Detection Using Audio and Video" (2024), addresses a critical gap in robotic welding: the inability of autonomous systems to identify defects introduced during the welding process. By leveraging deep learning on multimodal data—combining audio and video streams—Biswas pioneers a method that enables robots to self-monitor and detect anomalies without labeled training data. This work has already garnered 3 citations, signaling its early impact on the manufacturing and AI communities. Biswas’s research holds significant promise for improving quality control in industries reliant on precision welding, reducing waste, and enhancing safety. His innovative approach to unsupervised defect detection positions him as a rising voice in applied AI, with potential to reshape how industrial robots interact with their environment.
Research Focus
Key Achievements
Top Papers
- 1Unsupervised Welding Defect Detection Using Audio And Video3 citations · 2024