Takumi Kotooka
Papers
2
Total Citations
63
H-Index
2
About
Takumi Kotooka is a rising star in the emerging field of physical reservoir computing, where he pioneers the use of complex, disordered materials to perform machine intelligence directly in hardware. His research focuses on developing "in-materio" computing platforms that leverage the incidental structures of nanomaterials—such as random networks of single-walled carbon nanotubes combined with porphyrin-polyoxometalate—to create efficient, brain-like computational systems. Kotooka’s most cited work (2022, 57 citations) demonstrates a breakthrough in hardware-based machine intelligence, showing that these random networks can serve as physical reservoirs for time-series data processing. He further advanced this concept by integrating the system into a robotic platform for tactile object classification, proving its real-world applicability. By replacing traditional silicon-based neural networks with material-based computing, Kotooka’s contributions offer a path toward ultra-low-power, adaptive AI systems that learn from their physical environment. His work sits at the intersection of nanotechnology, materials science, and neuromorphic engineering, and has quickly garnered attention for its elegant simplicity and practical potential.
Research Focus
Key Achievements
Top Papers
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