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About

Zhenning Zhou is a leading researcher in robotic tactile perception and medical robotics, whose work bridges the gap between machine learning and physical human-robot interaction. His primary research areas include robotic palpation for tumor detection, tactile object classification, and multi-sensor motion tracking. Zhou’s most impactful contribution is the development of ordinal classification methods for robotic palpation, enabling robots to recognize the depth of hard inclusions in soft tissue—a critical step for tumor resection in robot-assisted surgery. His 2022 paper on this topic has garnered 27 citations, while his TactONet framework for hardness classification, which leverages unimodal probability to capture ordinal information, has been cited 23 times. Zhou has also advanced grasp stability prediction using graph convolutional networks (20 citations) and pioneered a shortcut-enhanced LSTM-GCN network for human motion tracking (17 citations). His recent work on dual autoencoder-based joint learning for depth classification and dynamic liquid volume estimation using spiking neural networks demonstrates his ongoing innovation. With over 90 total citations and a growing portfolio of high-impact publications, Zhou is shaping the future of tactile sensing and autonomous surgical systems.