Guangzhi Tang

Guangzhi Tang

Assistant Professor

DACS, Maastricht University

I am an Assistant Professor in the Department of Advanced Computing Sciences at Maastricht University. My research in Edge AI and Neuromorphic Computing develops cost-effective, brain-inspired computing paradigms to tackle the high expenses of modern AI systems. I also have extensive research and industry experience in hardware-aware optimization and reinforcement learning, focusing on practical applications in edge computing and robotics.

Before joining academia, I was a researcher at imec, the world-leading research & innovation center for nanoelectronics and digital technologies. I was a core member at imec advancing the SENECA neuromorphic processor, event-based neural networks, and the corresponding software. I completed my PhD at Rutgers University in the United States, advised by Dr. Konstantinos Michmizos. During my PhD, I bridged robotics and brain science by developing robust, efficient, and adaptive brain-inspired Spiking Neural Networks (SNNs) that address a wide spectrum of robotics challenges on neuromorphic processors.

Interests
  • Edge AI
  • Robotics
  • Neuromorphic Computing
  • Brain-inspired Computing
  • AI-enabled Automation
Education
  • PhD in Computer Science, 2022

    Rutgers, the State Univerisity of New Jersey

  • MSc in Computer Science, 2017

    Rutgers, the State Univerisity of New Jersey

  • BSc in Computer Science, 2015

    Nanjing University

Selected Publications

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(2024). Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces. In NCE.

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(2024). Optimizing event-based neural networks on digital neuromorphic architecture: a comprehensive design space exploration. In Front. Neurosci..

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(2023). SENECA: Building a fully digital neuromorphic processor, design trade-offs and challenges. In Front. Neurosci..

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(2023). Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design. In ISCAS 2023.

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(2022). Decoding EEG With Spiking Neural Networks on Neuromorphic Hardware. In TMLR.

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(2022). A Spiking Neural Network Mimics the Oculomotor System to Control a Biomimetic Robotic Head without Learning on a Neuromorphic Hardware. In TMRB.

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(2020). Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control. In CoRL 2020.

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(2019). Spiking neural network on neuromorphic hardware for energy-efficient unidimensional SLAM. In IROS 2019.

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