Taimur Hassan
Papers
5
Total Citations
39
H-Index
4
About
Dr. Taimur Hassan is a pioneering researcher at the intersection of artificial intelligence, robotics, and sensing technologies, whose work spans human activity recognition, agricultural automation, and safe human-robot collaboration. His most cited paper, a comprehensive 2024 review on through-the-wall human activity recognition using radar technologies (21 citations), establishes him as a leading voice in non-invasive sensing, particularly with ultra-wideband radar for surveillance and health monitoring. In agricultural AI, Dr. Hassan has made significant contributions through deep transfer learning for date fruit classification (2023) and the innovative SwishFormer architecture (2025), which integrates visual and tactile imagery for robust fruit ripeness assessment—a critical advance for smart harvesting and reducing food waste. His work in robotics safety is equally impactful: he developed a continuous wavelet transform-CNN for efficient collision detection in collaborative robots (2024), and introduced Seg-CURL (2023), a segmented contrastive unsupervised reinforcement learning method that dramatically improves sim-to-real transfer for visual robotic manipulation. With over 39 citations across his top papers, Dr. Hassan’s research demonstrates a rare ability to bridge theoretical innovation with practical deployment, tackling real-world challenges from fruit quality control to safer industrial automation.
Research Focus
Key Achievements
Top Papers
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- 3Date Fruit Classification System using Deep Transfer Learning4 citations · 2023
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