Dr. Yang’s research involves AI-based closed-loop neuromodulation system-on-chip, to revolutionize the diagnosis and treatment of brain diseases like autism, epilepsy, and neurodegenerative disorders through real-time signal acquisition, AI-based processing, and neuromodulation. Additionally, innovative cutting-edge, low-power brain-computer interface chips that amplify, digitize, and wirelessly transmit brain signals, laying the groundwork for AI-driven brain signal decoding and device control. His specific research directions include high-throughput low-power IC, closed-loop neural modulation SoC, and AI-assisted neural decoding. The work in neuromorphic computing employs innovative algorithms and hardware, aiming to replicate the human brain's efficiency and intelligence. The processors, based on spiking neural networks, significantly outperform traditional processors in energy efficiency and low-power performance, applicable in intelligent sensing, robotics, and biomedical scenarios.

[1] Chen Feng, Lu Cao, Di Wu, En Zhang, Ting Wang, Xiaowei Jiang, Chenhao Zhou, Jinbo Chen, Hui Wu, Siyu Lin, Qiming Hou, Chin-Teng Lin, Junming Zhu, Jie Yang, Mohamad Sawan, Yue Zhang. " Acoustic inspired brain-to-sentence decoder for logo syllabic language." BioRxiv, https://doi.org/10.1101/2023.11.05.562313.

[2] C. Fang, C. Wang, S. Zhao, F. Tian, J. Yang, M. Sawan, " A 510μW 0.738-mm2 6.2-pJ/SOP Online Learning Multi-Topology SNN Processor with Unified Computation Engine in 40-nm CMOS." IEEE Transactions on Biomedical Circuits and Systems, vol. 17, No. 3, 2023, pp.507-520.

[3] Yang, Jie, and Sawan, Mohamad. "From Seizure Detection to Smart and Fully Embedded Seizure Prediction Engine: A Review." IEEE Transactions on Biomedical Circuits and Systems, vol. 14, no. 5, Oct. 2020, pp. 1008-1023.