🎓 About Me

🏫 I am currently a fifth-year Ph.D. student in the Department of Electrical and Computer Engineering at the University of Virginia, where I am advised by Prof. Jundong Li. Before joining UVA, I earned my B.E. in Electronic Engineering from Tsinghua University in 2020, where my diploma thesis was advised by Prof. Ji Wu.

📝 My research focuses on Generalizable Machine Learning (GML) and its applications in trustworthy AI solutions. My work spans diverse data modalities, including graphs, knowledge bases, and text, with the aim of ensuring fairness, interpretability, and efficiency in AI systems.

🤝 Looking for collaborations to work on impactful projects!

💼 I am actively seeking faculty positions! I would greatly appreciate it if you share any opportunities. Thank you!

🔍 Research Interests

My research aims to tackle real-world challenges in Generalizable Machine Learning (GML) and Trustworthy AI, focusing on the following:

• Generalization with Minimal Labeled Data: Designing frameworks for few-shot learning, meta-learning, and task-adaptive model generalization.
• Robust Learning with Noisy and Unlabeled Data: Leveraging weak supervision to enhance model robustness across tasks.
• Fairness and Interpretability in AI: Developing fairness-aware GML frameworks and interpretable AI methods for socially impactful applications.

🔥 News and Updates

• 2025.01:  🎉 Three papers (one Spotlight first-author, one first-author) are accepted at ICLR 2025! See you (maybe) in Singapore!
• 2025.01:  🎉 Our paper (first-author), Generative Risk Minimization for Out-of-Distribution Generalization on Graphs, is accepted at TMLR!
• 2024.12:  🎉 Four papers (one first-author Oral, one Oral) are accepted at AAAI 2025! See you in Philadelphia!
• 2024.10:  🎉 Two papers are accepted at EMNLP 2024 Main! See you in Miami!
• 2024.10:  🎉 Two papers on Fairness in Large Language Models are accepted at NeurIPS SoLaR (One Spotlight)!
• 2024.10:  🎉 Our paper, “Mixture of Demonstrations for In-Context Learning,” is accepted at NeurIPS 2024! See you in Vancouver!
• 2024.10:  🎉 One paper is accepted at IEEE BigData 2024!
• 2024.10:  🎉 One paper is accepted at WSDM 2024!
• 2024.10:  🎉 Our paper, “Federated Graph Learning with Graphless Clients,” is accepted at TMLR!
• 2024.09:  🎉 Published “Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization” at ICDM 2024.
• 2024.09:  🎉 Our survey “Knowledge Editing for Large Language Models: A Survey,” is accepted at ACM Computing Surveys.
• 2024.07:  🎉 Our paper, “Understanding and Modeling Job Marketplace with Pretrained Language Models,” is accepted at CIKM 2024 Applied Research Track!
• 2024.05:  🎉 Two papers are accepted at ACL 2024 Findings!
• 2024.02:  🎉 Our paper, “Interpreting Pretrained Language Models via Concept Bottlenecks”, is accepted at PAKDD 2024 with Best Paper Award!

📜 Selected Publications

2025

• ICLR Spotlight: CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models [PDF] [GitHub]
  Song Wang*, Peng Wang*, Tong Zhou, Yushun Dong, Zhen Tan, Jundong Li.

• ICLR: Reasoning of Large Language Models over Knowledge Graphs with Super-Relations [PDF]
  Song Wang, Junhong Lin, Xiaojie Guo, Julian Shun, Jundong Li, Yada Zhu.

• ICLR: Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective
  Yushun Dong, Yinhan He, Patrick Soga, Song Wang, Jundong Li.

• TMLR: Generative Risk Minimization for Out-of-Distribution Generalization on Graphs [PDF]
  Song Wang, Zhen Tan, Yaochen Zhu, Chuxu Zhang, Jundong Li.

• AAAI Oral: BrainMAP: Learning Multiple Activation Pathways in Brain Networks [PDF]
  Song Wang*, Zhenyu Lei*, Zhen Tan, Jiaqi Ding, Xinyu Zhao, Yushun Dong, Guorong Wu, Tianlong Chen, Chen Chen, Aiying Zhang, Jundong Li.

• AAAI Oral: Tuning-Free Accountable Intervention for LLM Deployment - A Metacognitive Approach
  Zhen Tan, Jie Peng, Song Wang, Lijie Hu, Tianlong Chen, Huan Liu.

• AAAI: Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective
  Yiming Xu, Zhen Peng, Bin Shi, Xu Hua, Bo Dong, Song Wang, Chen Chen.

• AAAI: Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
  Xingbo Fu, Zihan Chen, Yinhan He, Song Wang, Binchi Zhang, Chen Chen, Jundong Li.

2024

• NeurIPS: Mixture of Demonstrations for In-Context Learning [PDF]
  Song Wang*, Zihan Chen*, Chengshuai Shi, Cong Shen, Jundong Li.

• ACM Computing Surveys: Knowledge Editing for Large Language Models: A Survey [PDF]
  Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, Jundong Li.

• ICDM: Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization [PDF]
  Song Wang, Xiaodong Yang, Rashidul Islam, Huiyuan Chen, Minghua Xu, Jundong Li, Yiwei Cai.

• NeurIPS SoLaR Spotlight: On Demonstration Selection for Improving Fairness in Language Models [PDF]
  Song Wang, Peng Wang, Yushun Dong, Tong Zhou, Lu Cheng, Yangfeng Ji, Jundong Li.

• ACL Findings: FastGAS: Fast Graph-based Annotation Selection for In-Context Learning
  Zihan Chen, Song Wang, Cong Shen, Jundong Li.

• ACL Findings: Knowledge Graph-Enhanced Large Language Models via Path Selection
  Haochen Liu, Song Wang, Yaochen Zhu, Yushun Dong, Jundong Li.

• EMNLP Main: Large Language Models for Data Annotation: A Survey [PDF]
  Zhen Tan*, Dawei Li*, Song Wang*, Alimohammad Beigi, Bohan Jiang, Amrita Bhattacharjee, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu.

• EMNLP Main: Glue Pizza and Eat Rocks - Exploiting Vulnerabilities in Retrieval-Augmented Generative Models
  Zhen Tan, Chengshuai Zhao, Raha Moraffah, Yifan Li, Song Wang, Jundong Li, Tianlong Chen, Huan Liu.

• IEEE BigData: KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models
  Zaiyi Zheng, Yushun Dong, Song Wang, Haochen Liu, Qi Wang, Jundong Li.

• WSDM: Demystify Epidemic Containment in Directed Networks: Theory and Algorithms
  Yinhan He, Chen Chen, Song Wang, Guanghui Min, Jundong Li.

• CIKM: Understanding and Modeling Job Marketplace with Pretrained Language Models
  Yaochen Zhu, Liang Wu, Binchi Zhang, Song Wang, Qi Guo, Liangjie Hong, Luke Simon, Jundong Li.

• PAKDD Best Paper: Interpreting Pretrained Language Models via Concept Bottlenecks [PDF]
  Zhen Tan, Lu Cheng, Song Wang, Yuan Bo, Jundong Li, Huan Liu.

2023

• TKDD: Learning Hierarchical Task Structures for Few-shot Graph Classification [PDF]
  Song Wang, Yushun Dong, Xiao Huang, Chen Chen, Jundong Li.

• EMNLP: Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance [PDF]
  Song Wang, Zhen Tan, Ruocheng Guo, Jundong Li.

• ECAI: Fair Few-shot Learning with Auxiliary Sets [PDF]
  Song Wang, Jing Ma, Lu Cheng, Jundong Li.

• SIGKDD: Federated Few-shot Learning [PDF]
  Song Wang, Xingbo Fu, Chen Chen, Jundong Li.

• SIGKDD: Contrastive Meta-Learning for Few-shot Node Classification [PDF]
  Song Wang*, Zhen Tan*, Huan Liu, Jundong Li.

• TKDE: Fairness in Graph Mining: A Survey [PDF]
  Yushun Dong, Jing Ma, Song Wang, Chen Chen, Jundong Li.

• AAAI: Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
  Yushun Dong, Song Wang, Jing Ma, Ninghao Liu, Jundong Li.

• WSDM: Few-shot Node Classification with Extremely Weak Supervision [PDF]
  Song Wang, Yushun Dong, Kaize Ding, Chen Chen, Jundong Li.

2022

• LoG (Spotlight): Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification [PDF]
  Zhen Tan*, Song Wang*, Kaize Ding*, Jundong Li, Huan Liu.

• NeurIPS: Graph Few-shot Learning with Task-specific Structures [PDF]
  Song Wang, Chen Chen, Jundong Li.

• SIGKDD: Task-Adaptive Few-shot Node Classification [PDF]
  Song Wang, Kaize Ding, Chuxu Zhang, Chen Chen, Jundong Li.

• IJCAI: FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs [PDF]
  Song Wang, Yushun Dong, Xiao Huang, Chen Chen, Jundong Li.

• SIGIR: Recognizing Medical Search Query Intent by Few-shot Learning
  Yaqing Wang*, Song Wang*, Yanyan Li, Dejing Dou.

Full Publication List (Google Scholar)

📖 Education

• University of Virginia: Ph.D. in Electrical and Computer Engineering (2020-Present)
• Tsinghua University: B.E. in Electronic Engineering (2016-2020)

💁 Service and Volunteering

• Conference Reviewer: NeurIPS, ICML, ACL, SIGKDD, NAACL, EMNLP.
• Mentorship: Directly supervised 6 undergraduate and graduate students, many of whom have published at top venues like NAACL and NeurIPS.

🐱 Hobbies and Interests

Beyond research, I enjoy:
🏊‍🚴🏃 Triathlon (training)
⚾ Baseball (playing)
🌟 Exploration Applications of AI for social good (e.g., personalized chatbot on social media)

Feel free to contact me for collaborations, research discussions, or just to connect!

🌍 Visits