Zhimeng Guo

Brief Bio

I am a graduate student at Penn State, dedicated to enhancing the reliability and alignment of machine learning systems with human values. My work focuses on challenges associated with robustness and alignment, such as addressing spurious correlations, mitigating hallucinations, and improving aspects like model interpretation and model personalization. Here is my CV and Research Statement.

2021 - Present: Graduate student at Penn State. I work on reliable AI.
2017 - 2021: B.Sc. at UESTC, advised by Prof. Lianli Gao and Prof. Linyuan Lv.

Publications

On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused?
Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen, and Dinghao Wu. arXiv, 2023.
[bib] [paper]

@article{zhang2023safety,
  title={On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused?},
  author={Zhang, Hangfan and Guo, Zhimeng and Zhu, Huaisheng and Cao, Bochuan and Lin, Lu and Jia, Jinyuan and Chen, Jinghui and Wu, Dinghao},
  journal={arXiv preprint arXiv:2310.01581},
  year={2023}
}

Towards Fair Graph Neural Networks via Graph Counterfactual.
Zhimeng Guo, Jialiang Li, Teng Xiao, Yao Ma, and Suhang Wang. CIKM, 2023.
[bib] [paper] [code]

@article{guo2023towards,
  title={Towards Fair Graph Neural Networks via Graph Counterfactual},
  author={Guo, Zhimeng and Li, Jialiang and Xiao, Teng and Ma, Yao and Wang, Suhang},
  journal={arXiv preprint arXiv:2307.04937},
  year={2023}
}

Counterfactual Learning on Graphs: A Survey.
Zhimeng Guo, Teng Xiao, Charu Aggarwal, Hui Liu, and Suhang Wang. arXiv, 2023.
[bib] [paper] [code]

@misc{guo2023counterfactual,
  title={Counterfactual Learning on Graphs: A Survey}, 
  author={Zhimeng Guo and Teng Xiao and Charu Aggarwal and Hui Liu and Suhang Wang},
  year={2023},
  eprint={2304.01391},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

Fairness-aware Message Passing for Graph Neural Networks.
Huaisheng Zhu, Guoji Fu, Zhimeng Guo, Zhiwei Zhang, Teng Xiao, and Suhang Wang. arXiv, 2023.
[bib] [paper]

@article{zhu2023fairnessaware,
  author = {Zhu, Huaisheng and Fu, Guoji and Guo, Zhimeng and Zhang, Zhiwei and Xiao, Teng and Wang, Suhang},
  journal = {arXiv},
  year = {2023},
  title = {Fairness-aware Message Passing for Graph Neural Networks},
  volume = {abs/2306.11132},
}

Decoupled Self-supervised Learning for Graphs.
Teng Xiao, Zhengyu Chen, Zhimeng Guo, Zeyang Zhuang, and Suhang Wang. NeurIPS, 2022.
[bib] [paper]

@inproceedings{xiao2022decoupled,
  author = {Xiao, Teng and Chen, Zhengyu and Guo, Zhimeng and Zhuang, Zeyang and Wang, Suhang},
  booktitle = {Conference on Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  pages = {620--634},
  title = {Decoupled Self-supervised Learning for Graphs},
  volume = {35},
}

Label-Wise Graph Convolutional Network for Heterophilic Graphs.
Enyan Dai, Shijie Zhou, Zhimeng Guo, and Suhang Wang. LoG, 2022.
[bib] [paper] [code]

@article{dai2022comprehensive,
  title={A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability},
  author={Dai, Enyan and Zhao, Tianxiang and Zhu, Huaisheng and Xu, Junjie and Guo, Zhimeng and Liu, Hui and Tang, Jiliang and Wang, Suhang},
  journal={arXiv preprint arXiv:2204.08570},
  year={2022}
}

Link Prediction on Heterophilic Graphs via Disentangled Representation Learning.
Shijie Zhou, Zhimeng Guo, Charu Aggarwal, Xiang Zhang, and Suhang Wang. arXiv, 2022.
[bib] [paper] [code]

@article{zhou2022link,
  author = {Zhou, Shijie and Guo, Zhimeng and Aggarwal, C. and Zhang, Xiang and Wang, Suhang},
  journal = {arXiv},
  year = {2022},
  title = {Link Prediction on Heterophilic Graphs via Disentangled Representation Learning},
  volume = {abs/2208.01820},
}

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability.
Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang. arXiv, 2022.
[bib] [paper] [code]

@article{dai2022a,
  author = {Dai, Enyan and Zhao, Tianxiang and Zhu, Huaisheng and Xu, Junjie and Guo, Zhimeng and Liu, Hui and Tang, Jiliang and Wang, Suhang},
  journal = {arXiv},
  year = {2022},
  title = {A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability},
  volume = {abs/2204.08570},
}

Teaching

IST 597 Data Science for Researchers and Scholars, Fall 2023.
DS 330 Visual Analytics, Spring 2024.

Awards / Acknowledgements / Services

Reviewer and subreviewer for AISTATS, NeurIPS, ICLR, SIGIR, IJCAI.
Outstanding Undergraduate Thesis Award.
Ranked 46/4103 in IEEEXtreme 24-Hour Programming Competition.
Meritorious Winner in 2019 MCM/ICM Mathematical Contest in Modeling.

Last update on Sep 10th, 2023.