Photo shot in summer 2025.

Contact

Hi, I am a postdoctoral AI researcher at the MIT-IBM Watson AI Lab. I design, build, and evaluate agentic AI systems for real‑world work, with a focus on tool use, multi‑agent collaboration, and holistic benchmarking for reliability, safety, and impact. My interests span large language models (LLMs), agentic AI, natural language processing (NLP), and human–AI interaction (HAI).

I completed my Ph.D. at MIT advised by Deb Roy, where I studied the foundations of LLMs and their applications to human communication at scale. Earlier, I earned an M.S. in Symbolic Systems from Stanford University, affiliated with the Stanford NLP Group, and a B.S. in Computer Science and a B.A. in Linguistics, Summa Cum Laude, from Emory University.

I enjoy exploring ideas in both research and product settings, and have spent fun summers with AI2, Google, IBM Research, Apple, ETS, and CMU. I welcome research inquiries, collaborations, and mentorship opportunities. If you'd like to connect, please email me .


News


Selected Publications

(* indicate equal contribution)

Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling
Hang Jiang, Xiajie Zhang, Robert Mahari, Daniel Kessler, Eric Ma, Tal August, Irene Li, Alex ‘Sandy’ Pentland, Yoon Kim, Deb Roy, and Jad Kabbara
ACL 2024
[Paper] [arXiv] [Code]

PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits
Hang Jiang, Xiajie Zhang, Xubo Cao, Cynthia Breazeal, Deb Roy, and Jad Kabbara
NAACL (Findings) 2024
[Paper] [arXiv] [Code] [Slides] [IC2S2]
Covered by [Science] [ACM]

Bridging Dictionary: AI-Generated Dictionary of Partisan Language Use
Hang Jiang*, Doug Beeferman*, William Brannon, Andrew Heyward and Deb Roy
CSCW Demo 2024
[Paper] [arXiv] [Demo] [Paper Demo] [Game Demo] [Latest Demo]

CommunityLM: Probing Partisan Worldviews from Language Models
Hang Jiang, Doug Beeferman, Brandon Roy, and Deb Roy
COLING 2022
[Paper] [arXiv] [Code] [Video] [Slides] [Poster] [Models]

Contrastive Learning of Medical Visual Representations from Paired Images and Text
Yuhao Zhang*, Hang Jiang*, Yasuhide Miura, Christopher D. Manning, and Curtis P. Langlotz
MLHC 2022
[Paper] [OpenReview] [arXiv] [Video]
ConVIRT inspired OpenAI's CLIP [Paper] [Blog]

LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
Hang Jiang*, Sairam Gurajada*, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray
ACL 2021
[Paper] [arXiv] [Code] [Slides] [Video]
Oral Presentation


Teaching