Xiaoyue Fan

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About

Xiaoyue Fan is a pioneering researcher at the intersection of neuromorphic computing and advanced materials, with a primary focus on hardware-algorithm co-design for next-generation artificial intelligence systems. Her most notable contribution lies in advancing analog reservoir computing—a recurrent neural network paradigm capable of tracing chaotic dynamics for applications like motion tracking, spatiotemporal pattern recognition, and anomaly detection. Fan’s groundbreaking 2025 work, "Hardware-algorithm co-design in analog reservoir computing with nonlinearity of solution-processed 2D materials," tackles a critical bottleneck: the iterative nonlinear mapping required for reservoir activation, which remains computationally prohibitive for digital systems. By leveraging solution-processed two-dimensional materials, she demonstrates how intrinsic material nonlinearity can be harnessed directly in hardware, bypassing traditional digital constraints. This innovative approach not only enhances energy efficiency but also paves the way for real-time, edge-computing applications. With her work already garnering attention in the emerging field of physical reservoir computing, Fan is recognized for bridging materials science and machine learning—offering a tangible path toward scalable, low-power neuromorphic hardware that could revolutionize how we process chaotic, time-dependent data.