Shiqi Zhao received the B.S. degree in applied physics from Xi’an Polytechnic University, Xi’an, China, in 2016, and the M.S. degree in integrated circuits and intelligent systems from Peking University, Beijing, China, in 2019. He joined the CenBRAIN Neurotech Center of Excellence as a Ph.D. Student, and obtained his Ph.D. degree from Westlake University / Zhejiang University in 2023. Shiqi has taken up a position as an Assistant Professor at Northeastern University in 2023.

Dr. Shiqi Zhao is interested in deep learning algorithm and digital circuits design. He is now following his passion to do research on low-precision neural network for biomedical applications and deep neural network hardware or neuromorphic hardware digital circuits design. His project aims to build a hardware based on software and hardware co-design for biomedical signal and image processing.  

[1] Zhao S, Yang J, Wang J, Sawan M. A 0.99-to-4.38 μJ/class Event-Driven Hybrid Neural Network Processor for Full-Spectrum Neural Signal Analyses[J]. IEEE Transactions on Biomedical Circuits and Systems, 2023.

[2] Wu D*, Zhao S*, Yang J, Sawan M. Low-Power FPGA-based Task-aware Compression and Energy-efficient Analysis Processor for Continuous Health Monitoring[J]. IEEE Transactions on Biomedical Circuits and Systems , 2023.

[3] Zhao S, Fang C, Yang J, Sawan M. Emerging Energy-Efficient Biosignal-Dedicated Circuit Techniques: A Tutorial Brief[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2022.