Dr. Jie Yang is a Research Professor at Westlake University. He is an IEEE Senior Member and Associate Editor of Neuroelectronics. He received his B.S. from Tianjin University (2010), Ph.D. from the Institute of Semiconductors, CAS (2015), and completed his postdoctoral training at the University of Calgary (2015–2019). His research focuses on “protecting, decoding, and emulating the brain,” covering closed-loop neuromodulation, brain-computer interfaces, and neuromorphic computing. Dr. Yang has been selected for multiple talent programs, including the Westlake Pearl Talent Program, Hangzhou Overseas High-Level Talent Program, and Zhejiang Provincial High-Level Talent Program. He currently leads national and provincial key research projects, including the China 2030 Brain Science Initiative and the Zhejiang "Pioneer" R&D Program. He has designed 20+ chips and published 100+ papers as first or corresponding author in leading journals such as JSSC, TBioCAS, TBME, JNE, and JBHI, as well as top international conferences including ISSCC, ECCV, and ICLR.

Dr. Yang’s research centers on three major themes — Protecting, Decoding, and Emulating the Brain — encompassing neuromodulation, brain-computer interfaces (BCIs), and neuromorphic computing.

Protecting the Brain: Neurological disorders such as autism, epilepsy, OCD, depression, Parkinson’s disease, and Alzheimer’s pose lifelong risks and societal burdens. We develop closed-loop neuromodulation system-on-chips that integrate real-time signal acquisition, AI-based signal processing, and neurostimulation, enabling precise diagnosis and treatment for neurodegenerative diesease.

Decoding the Brain: To establish efficient communication between the brain and the external world, we design high-throughput, low-power BCI chips capable of recording thousands of channels of neural signal. Combined with AI-assisted neural decoders, these systems support accurate translation of brain signals into speech and motor control, serving applications in speech restoration, rehabilitation, and intention recognition.

Emulating the Brain: Inspired by the brain’s extraordinary energy efficiency, we build neuromorphic processors that support energy-efficient, low-power computing and on-chip learning. Compared to traditional architecture, our chips achieve orders-of-magnitude improvements in energy efficiency, and have been deployed in biomedical devices, intelligent sensing, and embodied robotics systems that integrate perception, control, and learning in real time.

[1] C. Fang, C. Wang, S. Zhao, F. Tian, J. Yang, M. Sawan, " An Energy-Efficient Unstructured Sparsity-Aware Deep SNN Accelerator With 3-D Computation Array." IEEE Journal of Solid-State Circuits, vol. 60, No. 3, 2025, pp. 977 - 989.

[2] 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 logosyllabic language." Cyborg and Bionic Systems, vol. 6, no. 0257, Apr. 2025.

[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.