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Guangzhi Tang is an Assistant Professor in the Department of Advanced Computing Sciences at Maastricht University. His research interests lie in Edge AI and Neuromorphic Computing, where he develops cost-effective, brain-inspired computing paradigms to tackle the high expenses of modern AI systems. With extensive experience in hardware-aware optimization and reinforcement learning, he focuses on practical applications in edge computing and robotics. Before joining academia, Guangzhi was a researcher at imec, a leading research and innovation center in nanoelectronics and digital technologies. He was a core member of the team advancing the SENECA neuromorphic processor and event-based neural networks along with the corresponding software. Guangzhi completed his PhD at Rutgers University in the United States, under the supervision of Dr. Konstantinos Michmizos. His doctoral research bridged robotics and brain science, developing robust, efficient, and adaptive brain-inspired Spiking Neural Networks (SNNs) slated to address a wide spectrum of robotics challenges through neuromorphic processors. His expertise encompasses Edge AI, Sustainable AI, Neuromorphic Computing, Efficient Deep Learning, Robotics, Brain-inspired Computing, and AI-enabled Automation.
Maastricht University • Maastricht, Netherlands
Assistant Professor in the Department of Advanced Computing Sciences.
imec • Eindhoven, Netherlands
Conducting research in the area of hardware-efficient AI.
Neuromorphic Computing Lab - Intel • Hillsboro, United States
Internship focusing on neuromorphic computing.
Department of Computer Science - Rutgers University • Piscataway, United States
Research focused on Spiking Neural Networks and robotics challenges.
The School of Business and Economics (SBE) encompasses departments like Organization, Strategy, Entrepreneurship, Finance, and Economics.