Zhiqiang received his PhD from Imperial College London, and both his MSc and BSc degrees from Shanghai Jiao Tong University. His research focuses on efficient and trustworthy AI/ML systems, with particular emphasis on AI accelerators, FPGA-based computing, hardware-aware machine learning, and design automation for domain-specific architectures. He is interested in building AI systems that are not only accurate, but also fast, energy-efficient, and suitable for deployment in real-world constrained environments.

Before joining the University of Bristol, he was a Research Associate in the Department of Computing at Imperial College London, where he worked in the Custom Computing Research Group. His research there covered reconfigurable computing, quantization-aware training, compiler-based hardware generation, and low-latency machine learning for scientific applications, including real-time data processing for particle physics experiments at CERN. 

Prior to his academic career, Zhiqiang gained significant industry experience in hardware design. He worked as a Senior Design Engineer at Marvell Technology, focusing on CPU microarchitecture and computer arithmetic, and later as an FPGA Specialist at China Financial Futures Exchange Technology, where he developed ultra-low-latency FPGA systems for financial applications.

Zhiqiang has collaborated with leading academic and industrial partners, including Imperial College London, CERN, Intel, Xilinx/AMD, UBC, and Science Tokyo. His research has received international recognition, including Best Paper Awards at FPT‘25 and FCCM’26. His broader research vision is to enable the next generation of AI/ML systems through algorithm–hardware co-design, making advanced machine learning more efficient, reliable, and accessible across science, engineering, healthcare, and edge computing applications.

PhD and internship applications are welcome.