Jianwei Wang is currently a postdoctoral research fellow at Univeristy of New South Wales (UNSW), working with Prof. Wenjie Zhang, Prof. Xuemin Lin, Prof. Ying Zhang, Prof. Kai Wang. He received his PhD degree in Computer Science and Engineering from UNSW University in 2025. He received his B.Eng. degree in Chu Kochen Honors College from Zhejiang University in 2021.

His current research focuses on trustworthy data quality management, particularly leveraging techniques based on graph-language foundation models for the accurate, automatic, and efficient understanding, improvement, and evolution of complex data systems.

Topic of Interest:

• 1: Relational deep learning and reasoning (especially the graph-enhanced foundation models) for data preparation and management.
• 2: Multimodal data preparation and management.
• 3: Reinforced data training and quality optimization.
• 4: Neuro-Symbolic AI for explainable data preparation and interdisciplinary application, especially for quantum error correction, marketing strategy, biological drug design, financial security, and more.

Two to three fully funded PhD scholarships are available at UNSW. If you are interested in pursuing a Master’s, MPhil, or PhD degree in big data preparation and management in UNSW, please feel free to get in touch.

🔥 News

• June. 2025. Excited to launch the personal website 🎉🎉!
• Jan. 2026. Add the blog session to share my observations.

📄 Publications

(* indicates corresponding author)

• preprint and under review:
  • Shiyuan Liu, Jianwei Wang*, Xuemin Lin, Lu Qin, Wenjie Zhang, Ying Zhang. HyperJoin: LLM-augmented Hypergraph Link Prediction for Joinable Table Discovery.
  • Mengqi Wang, Jianwei Wang*, Qing Liu, Xiwei Xu, Zhenchang Xing, Liming Zhu, Wenjie Zhang. Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection.
  • Tianyuan Zhou, Jianwei Wang, Shuo Zhang, Kai Wang, Ying Zhang, Wenjie Zhang. Multivariate Time Series Imputation with Signal-Noise Disentangled Graph Propagation.
  • Mengshi Chen, Jianwei Wang, Kai Wang, Tengchao Li, Ruijia Wu, Xuemin Lin, Wenjie Zhang . Graph Feature Imputation with Joint Latent-Space Alignment and Data-Space Denoising.
  • Jianwei Wang, Yuehai Wang, Kai Wang*, Xuemin Lin, Wenjie Zhang, and Ying Zhang. Ensemble-based Deep Multilayer Community Search.
  • Mengshi Chen, Yuxiang Sun, Tengchao Li, Jianwei Wang*, Kai Wang, Wenjie Zhang, Ying Zhang, and Xuemin Lin. Empowering Tabular Data Preparation with Language Models: Why and How?.
  • Jianwei Wang, Mengqi Wang, Yinsi Zhou, Zhenchang Xing, Qing Liu, Xiwei Xu, Wenjie Zhang, and Liming Zhu. LLM-based HSE Compliance Assessment: Benchmark, Performance, and Advancements.
• 2026:
  • Jiawei Zhou, Ruicheng Zhu, Mengshi Chen, Jianwei Wang*, and Kai Wang*. TrustResearcher: Automating Knowledge-Grounded and Transparent Research Ideation with Multi-Agent Collaboration. WWW demo.
• 2025:
  • Jianwei Wang, Kai Wang*, Ying Zhang*, Wenjie Zhang, Xiwei Xu, and Xuemin Lin. On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message Passing. VLDB.
  • Jingye Zhao, Jianan Shen, Jianwei Wang*, Tianyuan Zhou, Ruijia Wu, Kai Wang, and Xuemin Lin. Account Risk Detection in Large-Scale Financial Graphs with Auxiliary Asset Prediction. TKDE.
  • Mo Zhou, Jianwei Wang*, Xuanmeng Zhang, Dylan Campbell, Kai Wang, Long Yuan, Wenjie Zhang, and Xuemin Lin*. ProbDiffFlow: An Efficient Learning-Free Framework for Probabilistic Single-Image Optical Flow Estimation. Frontiers of Computer Science.
  • Jiawei Zhou, Kai Wang*, Jianwei Wang, Kunpeng Zhang, and Xuemin Lin. COMET: An Interactive Framework for Efficient and Effective Community Search via Active Learning. INFORMS Journal on Computing (UTD 24).
• 2024:
  • Jianwei Wang, Kai Wang*, Xuemin Lin, Wenjie Zhang, and Ying Zhang. Efficient Unsupervised Community Search with Pre-trained Graph Transformer. VLDB.
  • Jianwei Wang, Ying Zhang*, Kai Wang*, Xuemin Lin, and Wenjie Zhang. Missing Data Imputation with Uncertainty-Driven Network. SIGMOD.
  • Jianwei Wang, Kai Wang*, Xuemin Lin, Wenjie Zhang, and Ying Zhang. Neural Attributed Community Search at Billion Scale. SIGMOD.

✍️ Blogs

• Feb. 2026: A Functional Taxonomy of LLM Roles in Large-scale Data Preparation. (coming soon)

💴 Working and Teaching Experience

• 2024.11 - Now, Postdoctoral Research Fellow, Univeristy of New South Wales, Sydney, Australia
• 2025, Guest Lecturer (COMP9311, 2025T3, UNSW).
• 2021 - 2024, Casual Academic and Teaching Assistant.
  • Data Analytics for Graphs (COMP9312, 2022, UNSW)
  • Data Science and Decision (COMP1001, 2023, UNSW)
  • Database Systems (COMP9311, 2022, 2023, UNSW)
  • Database Systems Implementation (COMP9315, 2023, UNSW)

📖 Education

• 2021.09 - 2025.04, Ph.D. candidate, College of Computer Science and Engineering, Univeristy of New South Wales, Sydney, Australia
• 2017.09 - 2021.06, B.Eng. in Chu Kochen Honors College, Zhejiang University, China.

👩‍🏫 Professional Service

• Organizing Committee
  • Local Chair of Autralian Database Conference (ADC) 2025.
• Program Committee
  • 2026: VLDB 2026 (Shadow PC),
  • 2025: ICDE 2025, ICLR 2025, APWeb-WAIM 2025, CIKM 2025
  • 2024: APWeb-WAIM 2024, ICDE 2024 (External reviewer).
  • 2023: ECML (External reviewer)
• Invited Journal Reviewer:
  • ACM Transaction on Database Systems (TODS)
  • INFORMS Journal on Computing (IJOC)
  • ACM SIGMOD Record

🏆 Awards

• May. 2024: National Outstanding Self-financed International Student Scholarship.
• May. 2024: SIGMOD Travel Award.
• June. 2021: Outstanding graduate and outstanding undergraduate thesis in Zhejiang University.
• Sep. 2016: First Prize, Chinese Chemistry Olympiad (CChO, Provincial Competition Area).