Shuo Zhou

I am a lecturer in machine learning and the PGR admission tutor at the School of Computer Science, University of Sheffield.

My current research focuses on robust, interpretable, and automated machine learning, with applications in biomedical and biological data analysis, particularly in neuroimaging and protein modelling. I am a co-creator and core developer of two open-source libraries, PyKale and KaleLinear, which provides a range of accessible multimodal and transfer learning algorithms.

I serve as a reviewer for the IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Cognitive and Developmental Systems (TCDS), IEEE Transactions on Neural Systems & Rehabilitation Engineering (TNSRE), IEEE Journal of Biomedical and Health Informatics (JBHI), Cerebral Cortex, Autism Research, European Child & Adolescent Psychiatry, and Frontiers in Neuroinformatics. Additionally, I served as a PC member for the International Joint Conference on Artificial Intelligence (IJCAI) in 2022, 2023, and 2024, as well as for the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2024, 2025, and 2026.

news

Oct 19, 2026	I currently have one open PhD position on “Multimodal Machine Learning for Protein and Metabolic Engineering towards Sustainable Biomanufacturing”. 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.

Oct 19, 2026

I currently have one open PhD position on “Multimodal Machine Learning for Protein and Metabolic Engineering towards Sustainable Biomanufacturing”. 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

NMI

Towards deployment-centric multimodal AI beyond vision and language

Xianyuan Liu, Jiayang Zhang, Shuo Zhou, and 8 more authors

Nature Machine Intelligence, 2025

DOI Bib HTML

@article{liu2025towards,
  title = {Towards deployment-centric multimodal AI beyond vision and language},
  author = {Liu, Xianyuan and Zhang, Jiayang and Zhou, Shuo and van der Plas, Thijs L and Vijayaraghavan, Avish and Grishina, Anastasiia and Zhuang, Mengdie and Schofield, Daniel and Tomlinson, Christopher and Wang, Yuhan and others},
  journal = {Nature Machine Intelligence},
  pages = {1--13},
  year = {2025},
  publisher = {Nature Publishing Group UK London},
  doi = {https://doi.org/10.1038/s42256-025-01116-5},
}

GigaScience

Group-specific discriminant analysis enhances detection of sex differences in brain functional network lateralization

Shuo Zhou, Junhao Luo, Yaya Jiang, and 3 more authors

GigaScience, 2025

DOI Bib HTML Code

@article{zhou2025group,
  title = {Group-specific discriminant analysis enhances detection of sex differences in brain functional network lateralization},
  author = {Zhou, Shuo and Luo, Junhao and Jiang, Yaya and Wang, Haolin and Lu, Haiping and Gong, Gaolang},
  journal = {GigaScience},
  volume = {14},
  pages = {giaf082},
  year = {2025},
  publisher = {Oxford University Press},
  doi = {https://doi.org/10.1093/gigascience/giaf082},
}

MICCAI

Foundation-Model-boosted multimodal learning for fMRI-based neuropathic pain drug response prediction

Wenrui Fan, LM Riza Rizky, Jiayang Zhang, and 5 more authors

In International Conference on Medical Image Computing and Computer-Assisted Intervention, 2025

DOI Bib HTML

@inproceedings{fan2025foundation,
  title = {Foundation-Model-boosted multimodal learning for fMRI-based neuropathic pain drug response prediction},
  author = {Fan, Wenrui and Rizky, LM Riza and Zhang, Jiayang and Chen, Chen and Lu, Haiping and Teh, Kevin and Selvarajah, Dinesh and Zhou, Shuo},
  booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages = {238--248},
  year = {2025},
  organization = {Springer},
  doi = {https://doi.org/10.1007/978-3-032-05182-0_24},
}

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

DOI Bib HTML 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

DOI Bib HTML 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},
}

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

DOI Bib HTML 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},
  doi = {https://doi.org/10.1609/aaai.v34i04.6179}
}