Hang Jiang is an Assistant Professor of Information Systems and Computer Science at Northeastern University, with a joint appointment in the D’Amore-McKim School of Business and the Khoury College of Computer Sciences. Jiang’s research focuses on natural language processing, large language models, human–AI interaction, computational social science, and AI for enterprise.

Jiang’s research agenda is motivated by a central question: how can AI systems be developed to foster a healthier, more connected, and more pluralistic society. His work takes an interdisciplinary approach, combining computational methods from machine learning and natural language processing with perspectives from the social, cognitive, and design sciences. His research has resulted in publications at leading computer science venues including ACL, NAACL, CHI, CSCW, COLING, and CIKM, as well as interdisciplinary journals such as JAMIA and Cognitive Science. Jiang is also a co-author of the award-winning Harvard Business School case study AI Wars, which explores strategic and organizational challenges surrounding AI adoption.

In addition to academic research, Jiang brings substantial industry research experience to his work. He has held research positions at the Allen Institute for AI, Google Research, IBM Research, and Apple, where he worked on applied machine learning, natural language processing, and human-centered AI systems. These experiences shape his interest in bridging research and practice, particularly in the design and deployment of AI systems that support communication and decision-making in organizational settings.

Education

  • PhD Media Arts and Sciences, Massachusetts Institute of Technology
  • MS Symbolic Systems, Stanford University
  • BS Computer Science and BA Linguistics, Emory University

Selected Publications

  • Overney, C., Jiang, H., Haider, U., Moe, C., Mangat, J., Pantano, F., McMillian, E. G., Riggins, P., & Gillani, N. (2026). Human-AI narrative synthesis to foster shared understanding in civic decision-making. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI).
  • Jiang, H., Zhang, X., Mahari, R., Kessler, D., Ma, E., August, T., Li, I., Pentland, A. S., Kim, Y., Roy, D., & Kabbara, J. (2024). Leveraging large language models for learning complex legal concepts through storytelling. Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL).
  • Jiang, H., Zhang, X., Cao, X., Breazeal, C., Roy, D., & Kabbara, J. (2024). PersonaLLM: Investigating the ability of large language models to express personality traits. Findings of the North American Chapter of the Association for Computational Linguistics (NAACL).
  • Wu, A., Higgins, M., Zhang, M., & Jiang, H. (2023). AI wars (Harvard Business School Case No. 723-434). Harvard Business School.
  • Jiang, H., Beeferman, D., Roy, B., & Roy, D. (2022). CommunityLM: Probing partisan worldviews from language models. Proceedings of the International Conference on Computational Linguistics (COLING).
  • Zhang, Y.*, Jiang, H.*, Miura, Y., Manning, C. D., & Langlotz, C. P. (2022). Contrastive learning of medical visual representations from paired images and text. Proceedings of Machine Learning for Healthcare (MLHC).
  • *Co-first authors.