Yingyi Wen
Papers
1
Total Citations
1
H-Index
1
About
Yingyi Wen is a rising researcher at the forefront of neuromorphic computing and hardware-algorithm co-design. Her work centers on developing efficient, brain-inspired computing systems by leveraging the unique properties of emerging materials. Wen’s major contribution lies in pioneering analog reservoir computing using solution-processed two-dimensional materials, demonstrating how their intrinsic nonlinearity can be harnessed for iterative mapping—a critical function for tracing chaotic dynamics in applications like motion tracking, spatiotemporal pattern recognition, and anomaly detection. Her 2025 paper on this hardware-algorithm co-design approach, already garnering early citations, addresses a fundamental bottleneck in digital computing by offloading computationally expensive reservoir activation to physical substrates. This work bridges materials science and machine learning, offering a path toward low-power, high-speed analog processors. Wen’s achievements highlight her ability to integrate device physics with algorithmic innovation, positioning her as a key voice in the next generation of unconventional computing. Her research promises to reshape how we implement recurrent neural networks for real-time, energy-constrained environments.
Research Focus
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Top Papers
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