Dr. Quanying Liu, joined Southern University of Science and Technology (SUSTech) in September 2019, as an Assistant Professor of the Department of Biomedical Engineering, Doctoral Supervisor, PI of Neural Computing and Control Laboratory (NCC lab). Before joining SUSTech, Quanying obtained her PhD degree from ETH Zurich and received postdoctoral training at Caltech. 

Quanying's research focuses on interactions among neuroscience, machine learning and control theory, including multi-modal neural signal processing (EEG, sEEG, fMRI, DTI), AI for neuroscience (explainable AI to interpret the structure and function of the brain), optimization for neuromodulation (tES, TMS, electrical stimulation). 

Dr. Quanying Liu has published over 60 research articles in top journals/conferences such as The Innovation, PNAS, Neuroimage, Neural Networks, Neurocomputing, HBM, JNE, IJNS, JBHI. Her work has been cited more than 1800 times, with an H factor of 22. She is the associate editor of IEEE Journal of Translational Engineering in Health and Medicine (JTEHM).

For anyone who is interested in joining NCC lab, please feel free to email me.

Education
2013-2017 PhD in Biomedical Engineering, ETH Zurich, Switzerland (Doctoral thesis: “Brain Network Imaging based on High-density Electroencephalography”. Supervisors: Dr. Nicole Wenderoth and Dr. Dante Mantini)
2010-2013 Master in Computer Science, Lanzhou University, China
2006-2010 Undergraduate in Electrical Engineering, Lanzhou University, China

Academic Positions
2019-present Assistant Professor in Department of Biomedical Engineering, Southern University of Science and Technology (PI of Neural Computing & Control lab)
2017-2019 Postdoctoral Scholar in Department of Computing and Mathematical Sciences (CMS), California Institute of Technology (Principal Investigator: Dr. John Doyle)
2017-2019 Independent researcher in Neurosciences, Huntington Medical Research Institute, US
2016-2017 Visiting Scholar in Research Center for Motor Control and Neuroplasticity, KU Leuven, Belgium
2016.10 Late-Summer School on Non-Invasive Brain Stimulation, University Medical Center Freiburg, Germany
2014-2015 Visiting Scholar in Department of Experimental Psychology, University of Oxford, UK

Awards and Scholarships

The New Brain 30 (2023)

AAIC travel award (2019)

Estes Stars Award (2018)

Representative Work of NCC lab: 

(Full list see Google Scholar: https://scholar.google.com/citations?user=UpP9hJ8AAAAJ&hl=en)

1) Li, Dongyang, Chen Wei, Shiying Li, Jiachen Zou, and Quanying Liu. "Visual decoding and reconstruction via eeg embeddings with guided diffusion." arXiv preprint arXiv:2403.07721 (2024).

2) Qu, Youzhi, Chen Wei, Penghui Du, Wenxin Che, Chi Zhang, Wanli Ouyang, Yatao Bian, .., and Quanying Liu. "Integration of cognitive tasks into artificial general intelligence test for large models." arXiv preprint arXiv:2402.02547 (2024).

3) Wang, Song, Chen Wei, Kexin Lou, Dongfeng Gu, and Quanying Liu. "Advancing EEG/MEG Source Imaging with Geometric-Informed Basis Functions." arXiv preprint arXiv:2401.17939 (2024).

4) Qu, Y., Du, P., Che, W., Wei, C., Zhang, C., Ouyang, W., ... & Liu, Q.* (2024). Promoting interactions between cognitive science and large language models. The Innovation, 100579.

5) M Wang, K Lou, Z Liu, P Wei, Liu, Q.* (2023) Multi-objective optimization via evolutionary algorithm (MOVEA) for high-definition transcranial electrical stimulation of the human brain, NeuroImage

6) Ye, Z., Qu, Y., Liang, Z., Wang, M., & Liu, Q.* (2023). Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network. Human Brain Mapping

7) Ye, Z., Huang, R., Wu, Q., & Liu, Q.* (2023, November). SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanations. In Thirty-seventh Conference on Neural Information Processing Systems.

8) Z Liang, Z Luo, K Liu, J Qiu, Liu, Q.*, (2022). Online Learning Koopman Operator for Closed-Loop Electrical Neurostimulation in Epilepsy, IEEE-JBHI 

9) Yu, J., Li, C., Lou, K., Wei, C., & Liu, Q.* (2022). Embedding decomposition for artifacts removal in EEG signals. Journal of Neural Engineering, 19(2), 026052.

10) Zhang, H., Zhao, M., Wei, C., Mantini, D., Li, Z., & Liu, Q.* (2021). EEGdenoiseNET: A benchmark dataset for deep learning solutions of eeg denoising. Journal of Neural Engineering, 18(5), 056057.