Yongjie Zhu

I am a postdoc researcher at the CS Department, University of Helsinki working with Aapo Hyvärinen. Before that, I completed a PhD within the IT Faculty, University of Jyväskylä under the supervision of Tapani Ristaniemi and Fengyu Cong. My thesis is available here.

My research focuses on unsupervised machine learning, mainly on nonlinear ICA, tensor component analysis, and disentangled representation learning, applicable to analyze brain imaging data especially recorded during resting state and natural stimuli. Specifically, I would like to develop a computational model/method to examine the spatiotemporal dynamics of brain functional networks and associate them with behavioral traits. I'm also interested in applying unsupervised or self-supervised learning to disentangle the underlying cognitive stages/processes in order to better understand the principles of information processing in the brain.

• Our paper, Unsupervised representation learning of spontaneous MEG data with Nonlinear ICA, was finally accepted at NeuroImage 2023. Code is available here.
• Our pape, Dynamic Community Detection for Brain Functional Networks during Music Listening with Block Component Analysis, has been accepted for publication with IEEE Trans. on Neural Systems and Rehabilitation Engineering (TNSRE) 2023.

• H. Hälvä, S. Le Corff, L. Lehéricy, J. So, Y. Zhu, E. Gassiat, A. Hyvärinen, Disentangling identifiable features from noisy data with structured nonlinear ICA
  35th Advances in Neural Information Processing Systems, NeurIPS 2021
  [pdf] [code] [bib]

  @inproceedings{halva2021disentangling,
    title={Disentangling identifiable features from noisy data with structured nonlinear ICA},
    author={Hälvä, Hermanni and Le Corff, Sylvain and Leh{\'e}ricy, Luc and So, Jonathan and Zhu, Yongjie and Gassiat, Elisabeth and Hyvärinen, Aapo},
    booktitle={Advances in Neural Information Processing Systems (NeurIPS 2021)},
    volume={34},
    pages={1624--1633},
    year={2021}
  }
  

• Y. Zhu, X. Wang, K. Mathiak, P. Toiviainen,T. Ristaniemi,J. Xu,Y. Chang, F. Cong, Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression
  International Journal of Neural Systems (IJNS), 2021
  [pdf] [code] [bib]

  @article{zhu2020altered,
    title={Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression},
    author={Zhu, Yongjie and Wang, Xiaoyu and Mathiak, Klaus and Toiviainen, Petri and Ristaniemi, Tapani and Xu, Jing and Chang, Yi and Cong, Fengyu},
    journal={International Journal of Neural Systems},
    volume={31},
    number={03},
    pages={2150001},
    year={2021},
    publisher={World Scientific}
  }
  

• Y. Zhu, J. Liu, C. Ye, K. Mathiak, P. Astikainen, T. Ristaniemi, F. Cong, Discovering dynamic task-modulated functional networks with specific spectral modes using MEG
  NeuroImage, 2020
  [pdf] [code] [supplement] [bib]

  @article{zhu2020discovering,
    title={Discovering dynamic task-modulated functional networks with specific spectral modes using MEG},
    author={Zhu, Yongjie and Liu, Jia and Ye, Chaoxiong and Mathiak, Klaus and Astikainen, Piia and Ristaniemi, Tapani and Cong, Fengyu},
    journal={NeuroImage},
    volume={218},
    pages={116924},
    year={2020},
    publisher={Elsevier}
  }
  

• Y. Zhu, J. Liu, T. Ristaniemi, F. Cong, Distinct patterns of functional connectivity during the comprehension of natural, narrative speech
  International Journal of Neural Systems (IJNS), 2020
  [pdf] [bib]

  @article{zhu2020distinct,
    title={Distinct patterns of functional connectivity during the comprehension of natural, narrative speech},
    author={Zhu, Yongjie and Liu, Jia and Ristaniemi, Tapani and Cong, Fengyu},
    journal={International journal of neural systems},
    volume={30},
    number={03},
    pages={2050007},
    year={2020},
    publisher={World Scientific}
  }
  

• Y. Zhu, C. Zhang, H. Poikonen, P. Toiviainen, M. Huotilainen, K. Mathiak,T. Ristaniemi, F. Cong, Exploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening
  Brain Topography, 2020
  [pdf] [code] [bib]

  @article{zhu2020exploring,
    title={Exploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening},
    author={Zhu, Yongjie and Zhang, Chi and Poikonen, Hanna and Toiviainen, Petri and Huotilainen, Minna and Mathiak, Klaus and Ristaniemi, Tapani and Cong, Fengyu},
    journal={Brain Topography},
    volume={33},
    number={3},
    pages={289--302},
    year={2020},
    publisher={Springer}
  }
  

• Y. Zhu, J. Liu, T. Ristaniemi, F. Cong, Deriving electrophysiological brain network connectivity via tensor component analysis during freely listening to music
  IEEE Trans on Neural Systems & Rehabilitation Engineering (TNSRE), 2019
  [pdf] [code] [bib]

  @article{zhu2019deriving,
    title={Deriving electrophysiological brain network connectivity via tensor component analysis during freely listening to music},
    author={Zhu, Yongjie and Liu, Jia and Mathiak, Klaus and Ristaniemi, Tapani and Cong, Fengyu},
    journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
    volume={28},
    number={2},
    pages={409--418},
    year={2019},
    publisher={IEEE}
  }
  

• Y. Zhu, X. Li, T. Ristaniemi, F. Cong, Measuring the task induced oscillatory brain activity using tensor decomposition
  44th International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
  [pdf] [poster] [bib]

  @inproceedings{zhu2019measuring,
    title={Measuring the task induced oscillatory brain activity using tensor decomposition},
    author={Zhu, Yongjie and Li, Xueqiao and Ristaniemi, Tapani and Cong, Fengyu},
    booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
    pages={8593--8597},
    year={2019},
    organization={IEEE}
  }
  


For a full list of publications please see here