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About

Uri Maoz is a leading researcher at the intersection of neuroscience, machine learning, and human-machine interfaces. His work focuses on decoding neural and muscular signals to understand and predict human intention and action. Maoz’s major contributions lie in developing advanced computational methods—including deep learning with attentional mechanisms and manifold learning—to interpret complex, high-dimensional biological data. His most cited paper (48 citations) introduces an end-to-end CNN with attentional mechanisms for motor-imagery EEG classification, a breakthrough for brain-computer interfaces (BCIs) used in neurorehabilitation and assistive robotics. He has also pioneered the decoding of object weight from electromyography (EMG) during grasping, achieving high accuracy despite the noise and complexity of multi-muscle signals. His work on dimensionality reduction for EMG classification (17 citations) further advances practical human-machine interfaces, from electrical wheelchairs to virtual environments. By bridging raw biological signals with actionable machine outputs, Maoz is helping to create more intuitive, responsive prosthetics and assistive technologies, with significant implications for healthcare and human augmentation.