Peisong Zhang

Singapore

peisongzhang@u.nus.edu

Hi! I recently completed my Master’s degree in Precision Health and Medicine (PHM) at the Yong Loo Lin School of Medicine, National University of Singapore , where I conducted my capstone project under the supervision of Prof. Dianbo Liu . I obtained my Bachelor’s degree in Applied Physics from the School of Science, Jimei University, in 2023, advised by Prof. Zhijin Wang .

My research is driven by a vision to build self-improving AI systems that can learn, reason, and adapt within the complex dynamics of human health. I am particularly interested in developing causal and temporal models that move beyond prediction—toward understanding how interventions shape patient trajectories and how machines can continuously refine their medical reasoning from real-world evidence.

Starting January 2026, I will be joining the CogAI4Sci Lab as a Ph.D. student, supervised by Prof. Dianbo Liu and co-supervised by Prof. Roger Foo, where I will work on self-improving AI for medicine—developing adaptive learning systems that continuously refine their reasoning and decision-making from clinical experience.

I am actively looking for **two research interns** who are passionate about *causal treatment outcome estimation for healthcare*. If you are interested in working on **causal representation learning**, **time-series modeling**, or **AI for medical decision support**, please feel free to drop me an email!

news

Oct 10, 2023	One paper (coauthor) on infectious diseases forecasting using multi-head oriented attention mechanism was accepted by Applied Intelligence in 2023.
Sep 1, 2023	One paper (coauthor) on load forecasting using GNN was published by Energy in 2023.
Jul 24, 2023	I receive the offer of Admission to the Master of Science in Precision Health and Medicine from Yang Loo Lin School of Medicine, National University of Singapore. And I will be studying at NUS as a master’s student from 15th Jan., 2024 for 2 years.
Jun 1, 2023	One paper (coauthor) on HFMD forecasting using transfer learning was published by NEPL in 2023.
Dec 15, 2022	One paper (coauthor) on nonalcoholic fatty liver disease intelligent diagnosis using multi-scale CNN was published by the 19th UIC in 2022.

selected publications

2022

Dual-grained directional representation for infectious disease case prediction

Peisong Zhang, Zhijin Wang, Yaohui Huang, and 1 more author

Knowledge-Based Systems, 2022

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The uncertain infection transmission causes challenges in accurate disease prediction. Numerous methods have been proposed to capture the temporal pictures from past observations within equal time intervals, which are called single-grained time series. However, these methods are not suitable for capturing uncertain temporal dynamics from infectious disease time series, since the infectious diseases may propagate in the incubation period. To address this issue, this paper proposes a Dual-Grained Directional Representation (DGDR) to generate predictions, via consolidating the representations of an equal-grained time series and several fine-grained time series. Firstly, the proposed DGDR learns a transformed segmentation into three kinds of representations. And then those representations from both equal-grained data and fine-grained data are temporally consolidated to connect with outputs. Extensive experiments on two real infectious disease datasets are done to validate the proposed DGDR. Compared with the other twelve methods, MAE value is decreased by 31.5%, RMSE value is decreased by 29.9%, and R2 value is improved by 87.6%.
@article{journal/knosys2022/Zhang, title = {Dual-grained directional representation for infectious disease case prediction}, journal = {Knowledge-Based Systems}, volume = {256}, pages = {109806}, year = {2022}, issn = {0950-7051}, publisher = {Elsevier}, doi = {https://doi.org/10.1016/j.knosys.2022.109806}, url = {https://www.sciencedirect.com/science/article/pii/S0950705122009194}, author = {Zhang, Peisong and Wang, Zhijin and Huang, Yaohui and Wang, Mingzhai}, dimensions = {true}, }
An Oriented Attention Model for Infectious Disease Cases Prediction

Peisong Zhang, Zhijin Wang, Guoqing Chao, and 2 more authors

In Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence, Jul 2022

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Effective infectious disease prediction supports the success of infection prevention and control. Several attention-based predictive models can be applied to undertake the prediction task. However, using a single attention mechanism can only capture local information, i.e. part of the temporal dynamics from time series. In this paper, we take for the hypothesis that using multiple attention from different aspects could improve prediction accuracy. An oriented attention model (OAM) is proposed to draw temporal dynamics in several aspects, via oriented attention units and their aggregation. Firstly, time series are represented as oriented transformations. And then those representations are consolidated to connect with outputs. Intensive experiments on two real infectious disease datasets show OAM’s effectiveness.
@inproceedings{conference/ieaaie2022/Zhang, author = {Zhang, Peisong and Wang, Zhijin and Chao, Guoqing and Huang, Yaohui and Yan, Jingwen}, title = {An Oriented Attention Model for Infectious Disease Cases Prediction}, booktitle = {Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence}, year = {2022}, month = jul, publisher = {Springer International Publishing}, address = {Cham, Japan}, pages = {124--136}, dimensions = {true}, }