Curriculum Vitae (CV)

🔬 Research Interests

My research focuses on building reliable LLM agents for real-world decision-making tasks that require multi-step reasoning over diverse sources of information. I work at the intersection of retrieval-augmented generation (RAG), tool-augmented language models (TALMs), and learning-based methods to improve agent behavior in imperfect and evolving environments.

• Tool use strategies for realistic data workflows: Enabling LLM agents to use tools effectively for dynamic, multi-step reasoning, while designing reliable domain-specific tools that integrate into agentic frameworks for planning, acting, and validation over heterogeneous data.

• Agent framework optimization via signals and learning: Designing task-dependent feedback signals, such as verifiability checks, tool outcomes, and consistency constraints, and using them to train or adapt agents to be more correct, robust, and efficient in realistic scenarios.

🎓 Education

• Ph.D. in Computer Science and Engineering, University of Notre Dame, Notre Dame, IN
  • August 2022 - Present
  • GPA: 3.958
  • Advisor: Dr. Meng Jiang
• B.S. in Computer Science & B.S. in Mathematics, Texas Christian University, Fort Worth, TX
  • August 2017 - December 2021
  • GPA: 4.0
  • Departmental Honors

💼 Industry Experience

• Applied Scientist Intern (Team Rufus), Amazon, Palo Alto, CA
  • September 2024 - May 2025
  • Project: Improving the Tool Using and Function Calling Capabilities of LLM(s)
  • Mentors: Dr. Tianyi Liu, Dr. Zhuofeng Wu, Jingfeng Yang, Dr. Haoming Jiang

🔬 Research Projects

• Knowledge Augmented & Tool-Use LLM(s)
  • September 2024 - Present
  • Working on tool-use capabilities of LLM(s).
• Factuality In The Wild & Retrieval-Augmented Generation
  • April 2024 - September 2024
  • Improving automated fact-checking systems, especially for claims in the wild.
• Information Retrieval Enhancement Using Text Expansion
  • August 2022 - April 2024
  • Improving diversity in query expansion in document retrieval.

📚 Publications

Preprints

• Hy Dang, Quang Dao, Meng Jiang. Open, Reliable, and Collective: A Community-Driven Framework for Tool-Using AI Agents. arXiv preprint, 2026.

Conference Papers

• Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang. Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates. The Conference on Empirical Methods in Natural Language Processing (EMNLP 2025).

• Yining Lu, Noah Ziems, Hy Dang, and Meng Jiang. Optimizing Decomposition for Optimal Claim Verification. Annual Meeting of the Association for Computational Linguistics (ACL 2025).

• Hy Dang, Minh Nguyen, Bo Mei. StTime-Net: Combining both Historical and Textual Factors for Stock Movement Prediction, in Proceedings of International Conference on Artificial Neural Networks (ICANN), Bristol, UK, 2022.

• Quang Truong, Minh Nguyen, Hy Dang, Bo Mei. Housing price prediction via improved machine learning techniques, Procedia Computer Science 174, 433-442.

Workshop Papers (* indicates equal contributions)

• Hy Dang, Bang Nguyen, Noah Ziems, Meng Jiang. Embedding Mental Health Discourse for Community Recommendation. 4th Workshop on Computational Approaches to Discourse, joint with The 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023).

• Meng Jiang, Hy Dang, Lingbo Tong. A Quantitative Review on Language Model Efficiency Research. LLM Symposium in conjunction with International Joint Conference on Artificial Intelligence (IJCAI 2023).

🎤 Presentations

• Hy Dang, Mengxia Yu, Meng Jiang. Improving Diversity of Query Expansion in Document Retrieval. Midwest Speech and Language Days (MSLD 2025).

• Hy Dang. Wound Healing Modeling Using Partial Differential Equations And Deep Learning. Presentation at National Collegiate Research Conference (NCRC 2022).

• Hy Dang, Luis Mantilla, S. Zhang, Andy Borum. Bifurcations of an elastic ring with interacting particles, Student Talk/Poster Session Presentation at the Canadian Undergraduate Mathematics Conference (CUMC 2020).

• Hy Dang. Wound Healing Modeling Using Partial Differential Equations And Deep Learning, Poster Presentation at the 3rd Annual Meeting of the SIAM Texas-Louisiana Section, 2020.

• Hy Dang. Wound Healing Modeling Using Partial Differential Equations And Deep Learning, Sixteenth Annual Texas Undergraduate Mathematics Conference (TUMC 2021).

🏆 Awards & Funding

• OpenAI Researcher Access Program, OpenAI
  • Award Amount: $10,000 in OpenAI API credits
  • January 2025 - July 2025
• Best Presentation Award, Notre Dame Data Mining Lab
  • Presentation Title: Expansion is all you need
  • Spring 2023, Spring 2024

• Student Research Symposium Best Poster Award, TCU, Spring 2021

• Science and Engineering Research Center Grant, TCU, Fall 2019

• Academic Achievement Award, TCU, May 2018

🤝 Service

• Teaching Assistant at University of Notre Dame:
  • CSE 20110: Discrete Mathematics - Fall 2022
  • CSE 40171: AI and Society - Spring 2023
• Reviewer: TKDE 2023, KnowledgeNLP-KDD’23, ICWSM 2024, ICWSM 2025, ARR Review.