Tenghao Huang
Senior Applied Scientist, Office of Applied Research, Microsoft.
I build language-model agents that learn from experience, use tools effectively, and help people tackle complex tasks.
Joining the University of Utah as an Assistant Professor in Fall 2027. I am recruiting Ph.D. students — see hiring details.
Research interests
- LLM agents and post-training: long-horizon planning, tool use, agentic memory, and proactive information gathering.
- Human-centered AI: social reasoning, grounded dialogue, and systems that collaborate effectively with people.
- AI creativity: evaluating creative outputs, generating compelling narratives, and balancing diversity with quality. I coauthored the NAACL tutorial Creative Planning with Language Models.
Background
At USC Viterbi, I worked with Muhao Chen and Jonathan May. I earned my bachelor’s degree at UNC-Chapel Hill, where I worked with Snigdha Chaturvedi and Colin Raffel. I have also interned at Amazon Alexa AI, IBM Research, and Salesforce AI Research. My research has been reported by MIT Technology Review, and I received the EMNLP 2024 Outstanding Paper Award.
Hiring
I am recruiting Ph.D. students to join my lab at the University of Utah in Fall 2027.
- Apply: Use the Kahlert School of Computing Ph.D. application and list me as a potential advisor.
- Get in touch: Email ohara.lab.recruiting@gmail.com with the subject [PRSP PhD] + your current position and institution. A brief note about your research interests and why the lab is a good fit is a great place to start. I may not be able to reply to every message.
news
| Feb 20, 2026 | Invited talk at Microsoft on Generative Learning via Adversarial Reward Estimation. |
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| Aug 25, 2025 | I am excited to return as a part-time research intern at Microsoft in Fall 2025. My spring intern paper Teaching Language Models to Gather Information Proactively is accepted by EMNLP-Findings 2025. I will keep working on post-training LLMs to align with human preferences:) |
| May 15, 2025 | Our paper R2D2: Remembering, Replaying and Dynamic Decision Making with a Reflective Agentic Memory is accepted by ACL 2025! |
| May 04, 2025 | We’re so excited to have our tutorial presented at NAACL 2025. This is a tutorial on Creative Planning in Large Language Models. |
selected publications
- arXiv
- arXivGLARE: Generative Learning via Adversarial Reward Estimation For Social Dynamics ForecastingarXiv preprint arXiv:2609.12165, 2026
- ACLGTA: Generating Long-Horizon Tasks for Web Agents at ScaleProceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026
- ICMLOptimizing Diversity and Quality through Base-Aligned Model CollaborationProceedings of the 43rd International Conference on Machine Learning (ICML), 2026
- EMNLPTeaching Language Models To Gather Information ProactivelyFindings of the Association for Computational Linguistics: EMNLP, 2025
- ACLR2D2: Remembering, Replaying and Dynamic Decision Making with a Reflective Agentic MemoryProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL), 2025
- EMNLPAre Large Language Models Capable of Generating Human-Level Narratives?Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024Outstanding Paper Award