个人简历：
Yuanning Li is an Assistant Professor in the School of Biomedical Engineering at ShanghaiTech University, where he is the principal investigator and director of the Computational Cognitive and Translational Neuroscience Lab. Before joining ShanghaiTech, Dr. Li was a postdoctoral scholar in the Department of Neurological Surgery at the University of California, San Francisco, where he worked with Edward Chang. Prior to UCSF, he completed his PhD in the joint Program in Neural Computation & Machine Learning at Carnegie Mellon University and the University of Pittsburgh.
Dr. Li’s research interests primarily lie in the intersection between computational, cognitive neuroscience and machine learning. His research works have been published on peer-reviewed journals including Nature Neuroscience, Nature Communications, Science Advances,PNAS and Cell Reports. He has given research talks at international conferences, including the annual meetings of the Society for Neuroscience (SfN) and the Vision Sciences Society (VSS). He was a recipient of the 2020 NIH Outstanding Scholars in Neuroscience Award, and 2018 SfN Trainee Professional Development Award.
研究领域：
Neural electrophysiology and neuroimaging
Computational and cognitive neuroscience
Neurolinguistics
Machine learning and artificial intelligence
Brain-computer interface and neural engineering
教学与课程：
BME2111/2127 Neural Signal Processing and Machine Learning (BME graduate course)
BT2003 Neural Signal Acquisition and Artificial Intelligence Technology (Biotechnology graduate
course)
BME1106 Biomedical Signals and Systems II (BME undergraduate course)
SI361 Multi-modal Brain-Computer Interfaces: Algorithms and Systems
学术任职：
Editorial board member:
PLOS Computational Biology, AI in Neuroscience
Reviewer for journals:
Nature Communications, Science Advances, PNAS, PLOS Biology, eLife, NeuroImage,
Cerebral Cortex, Journal of Neurophysiology, Journal of Neuroscience Methods,
PLOS Computational Biology, PLOS One, Scientific Reports, Fundamental Research,
IEEE Transactions on Biomedical Engineering (TBME), IEEE Transactions
on Cognitive and Developmental Systems(TCDS), IEEE Journal of Biomedical and
Health Informatics (JBHI)
Membership:
Society for Neuroscience
Vision Sciences Society
Society for the Neurobiology of Language
SfN Trainee Professional Development Awards (TPDA) Selection Committee Member
SfN International Travel Awards Selection Committee Member
代表性论文：
Selected publications: (* corresponding author, # co-first authors)
Zhang, D.#, Wang, Z.#, Qian, Y.#, Zhao, Z., Liu, Y., Hao, X., Li, W., Lu, S., Zhu, H., Chen, L., Xu, K., Li, Y.*, Lu, J.* (2024). A brain-to-text framework for decoding natural tonal sentences. Cell Reports, Vol. 43, Issue 11, 114924.
Li, Y.*#, Yang, H.#, & Gu, S.* (2024). Enhancing neural encoding models for naturalistic perception with a multi-level integration of deep neural networks and cortical networks. Science Bulletin, 69(11), 1738-1747.
Li, Y., Anumanchipalli, G., Mohamed, A., Chen, P., Carney, L. H., Lu, J., Wu, J., Chang, E.F.* (2023) Dissecting neural computations of the human auditory pathway using deep neural networks for speech. Nature Neuroscience, 26, 1-17.
Lu, J.#, Li, Y.#, Zhao, Z.#, Liu, Y., Zhu, Y., Mao, Y., Wu, J., Chang, E. F. (2023) Neural control of lexical tone production in human laryngeal motor cortex. Nature Communications, 14, 1-14.
Liu, Y., Zhao, Z., Xu, M., Yu, H., Zhu, Y., Zhang, J., Bu, L., Zhang, X., Lu, J.*, Li, Y.*, Ming, D., & Wu, J.* (2023). Decoding and synthesizing tonal language speech from brain activity. Science Advances, 9(23), eadh0478, 1-10.
Li, Y.#, Tang, C.#, Lu, J.#, Wu, J., & Chang, E. F. (2021). Human cortical encoding of pitch in tonal and non-tonal languages. Nature Communications, 12, 1161, 1-12.
Li, Y.*, Ward, M. J., Richardson, R. M., G’Sell, M., & Ghuman, A. S. (2020). Endogenous activity modulates stimulus and circuit-specific neural tuning and predicts perceptual behavior. Nature Communications, 11, 4014, 1-11.
Full list of publications:
https://scholar.google.com/citations?user=qETQrrkAAAAJ&hl=en
实验室介绍：
The Computational Cognitive and Translational Neuroscience Lab combines theories and techniques from cognitive neuroscience, computational neuroscience, neurosurgery, machine learning and artificial intelligence. Following “neural basis – computational models – clinical and engineering applications”, the lab aims on the neural basis of high-level cognitive functions, such as language, builds computational models based on modern machine learning, and extends the neuroscientific findings to brain-inspired artificial intelligence and neural engineering applications.
The main research topics include:
Neural basis of speech and language processing
Neural encoding and computation models of visual and auditory perception
Speech decoding and brain-computer interface of Chinese language
Close-loop neural modulation
