Zonghan Du is a Biomedical Engineering MS student at Brown University and a member of the BrainGate research team. His work lies at the intersection of neural engineering, computational neuroscience, and machine learning, with a focus on advancing neural decoding and understanding motor-related neural dynamics in humans.

Before joining BrainGate, Zonghan Du worked in Tsinghua University Neural Engineering Laboratory and collaborated with researchers at Fudan University and Sun Yat-sen University on research in neural decoding and neural signal processing. He developed Bayesian- and bio-inspired decoding architectures, cross-modal latent-space models for ECoG and movement signals, and state-space neural decoders based on Dynamic Movement Primitives (DMPs) to improve robust and real-time intention decoding. His projects span intracortical, ECoG, and EEG recordings and bridge algorithmic development with physiological insight.

At BrainGate, Zonghan studies intracortical neural activity during clinical trials to characterize task-related and spontaneous neural dynamics, with the goal of improving clinical monitoring and neural-based assistive communication systems. His research integrates spectral and time-domain analysis, point-process modeling, and machine learning pipelines to analyze human neural population activity.

Zonghan’s long-term research vision is to develop interpretable and efficient neural decoding models informed by neural dynamics, and to build task-driven analyses of brain population activity that reveal the computational principles supporting movement and intention. He believes that understanding neural dynamics can guide decoder design, while advances in neural decoding can deepen our understanding of neural representation. Ultimately, he aims to contribute to neurotechnology and fundamental principles of neuroscience that restore mobility and communication, and to extend these principles toward embodied intelligent systems inspired by biological neural computation.