Shuaicheng Zhang

Virginia Tech - Computer Science.

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About Me

Hi there, I am a final-year PhD student in the Department of Computer Science at Virginia Tech, fortunately advised by Dr. Dawei Zhou. My research is centered on open world graph learning - developing machine learning systems that operate effectively on real world evolving, heterogeneous graph data through unified theoretical and algorithmic innovations, enabling capability of reasoning over complex relational data at scale and significantly enhance decision-making in critical applications.

🔍 Research Focus

  • Graph representation learning, exploring expressive and scalable models for complex graph data
  • Graph neural networks (GNNs) for heterogeneous and heterophilous graphs
  • Graph-augmented foundation models, investigation into LLMs enhanced with graph structure
  • Graph learning applications grounding theoretical advances in impactful domains such as predictive maintenance and AI for finance

Internship

Microsoft
2025 — Research Intern, Microsoft Research, Redmond, WA, USA
MIT-IBM Lab
2023 — Research Intern, MIT-IBM Lab, Cambridge, MA, USA
Deloitte
2021 — NLP Research Intern, Nature Language Processing Team, Deloitte, NY, USA
Hundsun Technologies
2017 — Software Development Engineer Intern, Trust Team, Hundsun Technologies Inc., Hangzhou, China

News

09/2025 One paper accepted at Neurips 2025 — see you in San Deigo 😊!
07/2025 Our paper "When Heterophily Meets Heterogeneity" received the KDD 2025 Best Paper Award in the Dataset and Benchmark track.
06/2025 Starting my internship at Microsoft on GNN for Database Foundation Model— see you in Redmond 😊!
05/2025 One paper accepted at KDD Benchmark 2025 — see you in Toronto 😊!
05/2025 Recognized as EXCELLENT Reviewer (Top 25%) by KDD 2025 February Track 😊!
12/2024 Recognized as OUTSTANDING Reviewer (Top 10%) by KDD 2025 August Track 😊!
10/2024 One paper accepted at IEEE BigData 2025 — see you in DC 😊!
07/2024 One paper accepted at KDD 2025 — see you in Toronto 😊!
09/2023 One paper accepted at ICAIF 2023 with oral — see you in New York 😊!
07/2023 Attending ICML 2023 in Honolulu, HI — see you at our poster session 😊!
05/2023 New preprint available!
05/2023 Joining IBM-MIT Lab this summer to work on deep graph generative models!
05/2023 One paper accepted at ICML 2023 — see you in Hawaii 😊!
04/2023 Thank you to ML Day-Amazon 2023 for the travel grant!
03/2023 Selected as a CCI Cyber Innovation Scholar!
10/2022 Attending CIKM 2022 in Atlanta, GA — see you at our workshop 😊!
10/2022 Thank you to CIKM 2022 NSF for the travel award!
06/2022 Serving as Publicity Chair of the 1st Trustlog workshop@CIKM22
05/2022 My website is alive! 🎉
04/2022 One paper accepted at Findings of NAACL 2022 — see you in Seattle 😊!

Publications

2025

  1. gpatcher.jpg
    HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations
    Shuaicheng Zhang, Haohui Wang, Junhong Lin, and 5 more authors Zhang, Shuaicheng and Wang, Haohui and Lin, Junhong and Guo, Xiaojie and Zhu, Yada and and Zhang, Si and Fu, Dongqi and Zhou, Dawei
    2025
    to appear
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    When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark
    Junhong Lin, Xiaojie Guo, Shuaicheng Zhang, and 3 more authors Lin, Junhong and Guo, Xiaojie and Zhang, Shuaicheng and Zhou, Dawei and Zhu, Yada and Shun, Julian
    In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025
    DOI
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    MentorPDM: Learning Data-Driven Curriculum for Multi-Modal Predictive Maintenance
    Shuaicheng Zhang, Tuo Wang, Stephen Adams, and 5 more authors Zhang, Shuaicheng and Wang, Tuo and Adams, Stephen and Bhattacharya, Sanmitra and Tiyyagura, Sunil Reddy and Bowen, Edward and Veeramani, Balaji and Zhou, Dawei
    In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1, 2025
    DOI
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    Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions
    A Kulkarni, F Alotaibi, X Zeng, and 6 more authors Kulkarni, A and Alotaibi, F and Zeng, X and Wu, L and Zeng, T and Yao, BM and Liu, M and Zhang, Shuaicheng and …
    CoRR, 2025
    arXiv preprint

2024

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    UnifiedGT: Towards a Universal Framework of Transformers in Large‑Scale Graph Learning
    Junhong Lin, Xiaojie Guo, Shuaicheng Zhang, and 3 more authors Lin, Junhong and Guo, Xiaojie and Zhang, Shuaicheng and Zhou, Dawei and Zhu, Yada and Shun, Julian
    In IEEE International Conference on Big Data (BigData), 2024
    DOI

2023

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    Personalized Federated Learning under Mixture of Distributions
    Shuaicheng Zhang, Yue Wu, Wenchao Yu, and 5 more authors Zhang, Shuaicheng and Wu, Yue and Yu, Wenchao and Liu, Yanchi and Gu, Quanquan and Zhou, Dawei and Chen, Haifeng and Cheng, Wei
    In Proceedings of the 40th International Conference on Machine Learning (ICML), 2023
    DOI
  2. tgeditor.png
    TGEditor: Task‑Guided Graph Editing for Augmenting Temporal Financial Transaction Networks
    Shuaicheng Zhang, Yada Zhu, and Dawei Zhou Zhang, Shuaicheng and Zhu, Yada and Zhou, Dawei
    In Proceedings of the Fourth ACM International Conference on Artificial Intelligence and Finance (ICAIF), 2023
    DOI

2022

  1. graphtransformer.png
    Extracting Temporal Event Relation with Syntax‑guided Graph Transformer
    Shuaicheng Zhang, Qiang Ning, and Lifu Huang Zhang, Shuaicheng and Ning, Qiang and Huang, Lifu
    In Findings of the Association for Computational Linguistics: NAACL, 2022
    DOI

My Cats

Cats are connoisseurs of comfort.

– James Herriot

Here are some photos of my cats. Each one has their own unique personality and brings joy to my life in their own special way.

Jojo
Joy
From left to right: Jojo and Joy enjoying their favorite spots.
Cat 4
Cat 5
More of my cats being their adorable selves.

CV

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