Yucheng Liang

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About

Yucheng Liang’s research bridges cutting-edge computer vision and socially impactful robotics, with a focus on real-time systems and human-robot interaction. A key contribution is the development of GPU-accelerated real-time stereo estimation using binary neural networks, a breakthrough that enables efficient depth perception for autonomous vehicles and robotics. This work, which has garnered 38 citations, addresses critical demands for low-latency, energy-efficient processing in embedded systems. In parallel, Liang has advanced human-centered robotics through a pioneering social work project in China, studying how socially assistive robots (SARs) can support older adults living alone. This empirical study, cited 12 times, provides vital insights into technology acceptance and loneliness reduction among aging populations. By combining hardware-aware deep learning with applied social robotics, Liang’s research demonstrates a rare ability to optimize algorithmic performance while addressing real-world societal challenges. Their work not only pushes the boundaries of efficient neural computation but also shapes the ethical deployment of assistive technologies, making significant strides toward robots that are both faster and more compassionate.