Papers
3
Total Citations
47
H-Index
3
About
Yang Ni is a pioneering researcher at the intersection of reinforcement learning, robotics, and energy-efficient AI. His work fundamentally challenges the resource-intensive nature of modern deep learning by championing hyperdimensional computing (HDC) as a lightweight, brain-inspired alternative for real-world control systems. Ni’s most influential paper, "HDPG" (27 citations), demonstrates how to replace traditional, hand-crafted robotic controllers with self-learning agents that achieve human-level adaptability without the computational overhead of deep neural networks. He further advanced this paradigm with "DARL" (14 citations), which deploys HDC-based reinforcement learning on edge devices, proving that intelligent decision-making is possible in energy-constrained environments. His latest work, "Brain-Inspired Hyperdimensional Computing in the Wild" (6 citations), introduces ReactHD, a symbolic learning framework that successfully controls wheeled robots in real-world scenarios, bridging the gap between theoretical efficiency and practical deployment. By showing that HDC can match or exceed the performance of deep RL while consuming a fraction of the energy, Ni is shaping a future where autonomous systems are not only smarter but also greener and more accessible.