Deep Banerjee
Papers
2
Total Citations
63
H-Index
2
About
Deep Banerjee is a leading researcher in the emerging field of in-materio intelligence, where physical materials are engineered to perform computational tasks directly, bypassing traditional silicon-based architectures. His work focuses on hardware-based machine intelligence, particularly through reservoir computing (RC) networks that mimic biological neural processing. Banerjee’s most cited paper, “Emergence of In‐Materio Intelligence from an Incidental Structure of a Single‐Walled Carbon Nanotube–Porphyrin Polyoxometalate Random Network” (2022, 57 citations), demonstrates a groundbreaking physical RC platform using a random network of carbon nanotubes and porphyrin-polyoxometalate. This system achieves efficient, low-power neural network training and has been successfully applied to robot-based object classification through time-series tactile sensing. His research bridges materials science and neuromorphic computing, offering a scalable, energy-efficient alternative to conventional AI hardware. With over 60 cumulative citations, Banerjee’s contributions are paving the way for next-generation intelligent systems that learn and adapt directly within their physical substrate, making him a key figure in the evolution of embodied artificial intelligence.
Research Focus
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Top Papers
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