Shuo Zhou

I am an academic fellow in machine learning at the Machine Learning Research Group, Department of Computer Science, and deputy head of AI research engineering at Centre for Machine Intelligence, University of Sheffield.

My current research focuses on developing interpretable machine learning methods and tools for medical data analysis. I am a co-creator and core developer of open-source library PyKale, which provides a range of accessible multi-modal and transfer learning algorithms.

I serve as a reviewer of IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Cognitive and Developmental Systems (TCDS), and Cerebral Cortex. I also served as a PC member of IJCAI 2022, 2023. I am one of the organisers of the Meta-Learning for Multimodal Data interest group and awardee of the Post-Doctoral Enrichment Awards (PDEA) 2022 programme of the Alan Turing Institute.

I currently have one open PhD position available. If you are interested, please contact me by email with: 1) your CV; 2) one representative piece of your writing (e.g., paper, dissertation, technical report); 3) code samples via an attachment or providing your GitHub page link; and 4) a description of a collaboration experience in your Application Statement.

selected publications

IEEE TMI

Improving Multi-Site Autism Classification via Site-Dependence Minimization and Second-Order Functional Connectivity

Mwiza Kunda, Shuo Zhou, Gaolang Gong, and 1 more author

IEEE Transactions on Medical Imaging, 2022

Bib LINK Code

@article{kunda2022improving,
  title = {Improving Multi-Site Autism Classification via Site-Dependence Minimization and Second-Order Functional Connectivity},
  author = {Kunda, Mwiza and Zhou, Shuo and Gong, Gaolang and Lu, Haiping},
  journal = {IEEE Transactions on Medical Imaging},
  doi = {10.1109/TMI.2022.3203899},
  year = {2022},
  publisher = {IEEE},
  url = {https://ieeexplore.ieee.org/document/9874890},
}

CIKM

PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python

Haiping Lu, Xianyuan Liu, Shuo Zhou, and 6 more authors

In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022

Bib Code

@inproceedings{pykale-cikm2022,
  title = {{PyKale}: Knowledge-Aware Machine Learning from Multiple Sources in {Python}},
  author = {Lu, Haiping and Liu, Xianyuan and Zhou, Shuo and Turner, Robert and Bai, Peizhen and Koot, Raivo and Chasmai, Mustafa and Schobs, Lawrence and Xu, Hao},
  booktitle = {Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM)},
  doi = {10.1145/3511808.3557676},
  year = {2022},
}

PhD Thesis

Interpretable Domain-Aware Learning for Neuroimage Classification

Shuo Zhou

University of Sheffield, 2022

Bib LINK

@phdthesis{zhou2022interpretable,
  title = {Interpretable Domain-Aware Learning for Neuroimage Classification},
  author = {Zhou, Shuo},
  year = {2022},
  school = {University of Sheffield},
  url = {https://etheses.whiterose.ac.uk/31044/1/PhD_thesis_ShuoZhou_170272834.pdf},
}

AAAI

Side information dependence as a regularizer for analyzing human brain conditions across cognitive experiments

Shuo Zhou, Wenwen Li, Christopher Cox, and 1 more author

In Proceedings of the AAAI Conference on Artificial Intelligence, 2020

Bib LINK Code

@inproceedings{zhou2020side,
  title = {Side information dependence as a regularizer for analyzing human brain conditions across cognitive experiments},
  author = {Zhou, Shuo and Li, Wenwen and Cox, Christopher and Lu, Haiping},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {34},
  number = {04},
  pages = {6957--6964},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6179},
}