Guangzhi Tang (G.)
I am an Assistant Professor in Edge Computing 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.
Expertises
- Edge AI
- Sustainable AI
- Neuromorphic Computing
- Efficient Deep Learning
- Robotics
- Brain-inspired Computing
- AI-enabled Automation
Career history
- [2024 - Now] Assistant Professor - Department of Advanced Computing Sciences - Maastricht University, Maastricht, Netherlands
- [2022 - 2024] Researcher - Hardware Efficient AI - imec, Eindhoven, Netherlands
- [2021-2021] PhD Research Intern - Neuromorphic Computing Lab - Intel, Hillsboro, United States
- [2017-2022] PhD Researcher - Department of Computer Science - Rutgers University, Piscataway, United States