Jiale Han

Research Assistant Professor

Center for AI for Social Science

Education Background
  • 2018-2023: Beijing University of Posts and Telecommunications, Computer Science and Technology, Doctoral Degree (Full-time)
  • 2021-2023: Singapore University of Technology and Design, Visiting PhD Student
  • 2014-2018: Xidian University, Telecommunications Engineering, Bachelor’s degree (Full-time)

 

Work Experience：

  • 2026-Present: Shenzhen Loop Area Institute, Research, Assistant Professor
  • 2024-2026: Hong Kong University of Science and Technology, Post-doctoral Fellow
Research Fields
1.Core Research Interests: Large Language Models, Natural Language Processing, AI Agents and Multi-Agent Systems, Social Simulation. 2.Interdisciplinary Research Fields: AI + Business, Financial Text Mining, Supply Chain Network Analysis, Financial Risk P
Email
jialehan@slai.edu.cn
Biography

Jiale Han is a Research Assistant Professor at Shenzhen Loop Area Institute. Before joining the Institute, she conducted postdoctoral research at the School of Business and Management, Hong Kong University of Science and Technology. She received her PhD in Computer Science from Beijing University of Posts and Telecommunications in 2023. Her research lies at the intersection of AI and business, focusing on large language models, natural language processing, AI agents, social simulation, and their applications in business and finance. She has published over 30 papers in leading venues such as ICLR, ICML, TKDE, and ACL, with multiple manuscripts under major revision or review at UTD-24 business journals. Research collaborations in AI, business, and finance, as well as applications from outstanding students, are welcome.

Academic Publications
  1. Li X, Gao J, Lin S, Zhou X, Zhang C, Cheng B, Han J*, Wang B. Human or Machine? A Preliminary Turing Test for Speech-to-Speech Interaction. International Conference on Learning Representations, 2026. (ICLR 2026, CCF A)
  2. Chen Y, Zhang S, Han J, Meng F, Jiang H, Cheng B, Tong Q, Liu X. CGSVD: Cascaded Granular Singular Value Decomposition for Large Language Model Compression. International Conference on Machine Learning, 2026. (ICML 2026, CCF A)
  3. Zhang S, Chen Y, Han J*, Cheng B, Ma J. Beyond A Fixed Seal: Adaptive Stealing Watermark in Large Language Models. Findings of the Association for Computational Linguistics, 2026. (ACL 2026 Findings)
  4. Gu W, Wang H, Han J*, Li X, Wu Z, Xiao H, Cheng B. Analyze–Compose–Execute: A Dynamic Dialogue Framework for Multi-Agent Debate. Annual AAAI Conference on Artificial Intelligence, 2026. (AAAI 2026, CCF A)
  5. Xiao H, Li X, Pan D, Zhang L, Song Z, Han J*, Lai S, Chen W, Tang J, Wang B. Can Audio Language Models Listen Between the Lines? A Study on Metaphorical Reasoning via the UNSPOKEN. ACM International Conference on Multimedia, 2025. (ACMMM 2025 Brave New Ideas, Oral, CCF A)
  6. Chen Y, Cheng B, Han J*, Zhang Y, Li Y, Zhang S. DLP: Dynamic Layerwise Pruning in Large Language Models. International Conference on Machine Learning, 2025. (ICML 2025, CCF A)
  7. Wei Y, Han J, Yang Y. Adapting General-Purpose Embedding Models to Private Datasets Using Keyword-based Retrieval. Findings of the Association for Computational Linguistics, 2025. (ACL 2025 Findings)
  8. Wu Z, Cheng B, Han J, Ma J, Zhang S, Chen Y, Li C. VideoQA-TA: Temporal-Aware Multi-Modal Video Question Answering. Joint International Conference on Computational Linguistics, Language Resources and Evaluation, 2025. (COLING 2025, CCF B, Outstanding Paper)
  9. Han J, Cheng B, Wan Z, Lu W. Towards Hard Few-Shot Relation Classification. IEEE Transactions on Knowledge and Data Engineering, 2023. (TKDE 2023, CCF A)
  10. Han J, Zhao S, Cheng B, Ma S, Lu W. Generative Prompt Tuning for Relation Classification. Findings of the Association for Computational Linguistics: EMNLP, 2022. (Findings of EMNLP 2022)
  11. Han J, Cheng B, Lu W. Exploring Task Difficulty for Few-Shot Relation Extraction. Conference on Empirical Methods in Natural Language Processing, 2021. (EMNLP 2021, CCF B)
  12. Han J, Cheng B, Nan G. Learning Discriminative and Unbiased Representations for Few-Shot Relation Extraction. ACM International Conference on Information and Knowledge Management, 2021. (CIKM 2021, CCF B)
  13. Han J, Cheng B, Wang X. Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion. Findings of the Association for Computational Linguistics: EMNLP, 2020. (Findings of EMNLP 2020)
  14. Han J, Cheng B, Wang X. Two-Phase Hypergraph based Reasoning with Dynamic Relations for Multi-Hop KBQA. International Joint Conference on Artificial Intelligence, 2020. (IJCAI 2020)

 

Research Awards：

2025：Outstanding Paper, The 31st International Conference on Computational Linguistics