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Background / Experience

Curriculum Vitae (CV)

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Office: 355 Fitzpatrick Hall, Notre Dame, IN 46565 · hdang@nd.edu

Research Interests

My research focuses on building reliable and self-improving LLM agents capable of complex, multi-step reasoning and decision-making. I investigate how agents can effectively use and create tools, learn from experience, and autonomously evolve their capabilities.

  • Tool-Augmented Agents: Developing methods for effective tool use and reliable tool construction, improving agents’ tool-use accuracy and the intrinsic reliability of tools in complex, real-world workflows.

  • Self-Improving and Evolving Agents: Enabling agents to acquire, refine, and evolve reusable skills and strategies through feedback and experience, improving adaptability, robustness, and efficiency.

Education

  • Ph.D. Candidate 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 (Applied Science Team), Oracle, Redwood City, CA
    • June 2026 - September 2026
    • Developed an agentic AI framework for skill evolution, enabling LLM agents to construct, refine, and reuse task-specific skills for complex reasoning and decision-making.
    • Mentors: Dr. Julien Yu, Dr. Mihaela Bornea, Dr. Hiya Roy, Hitesh Patel, Dr. Yinsheng Wang, Dr. Avi Sil (Manager)
  • 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

  • ToolBox Reliability for LLM Agents
    • May 2025 - Present
    • Building a community-driven toolbox to improve tool-integrated LLM reliability by addressing both tool-use accuracy and intrinsic tool accuracy.
  • 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

Conference Papers

  • Hy Dang, Quang Dao, Meng Jiang. Open, Reliable, and Collective: A Community-Driven Framework for Tool-Using AI Agents. Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP 2026 Demo Track). Paper · Project page

  • Hy Dang, Yuwen Lu, Jason Spicer, Tamara Kay, Di Yang, Yang Yang, Jay Brockman, Meng Jiang, and Toby Jia-Jun Li. Uncovering Disparities in Rideshare Drivers’ Earning and Work Patterns: A Case Study of Chicago. Proceedings of the ACM on Human-Computer Interaction (CSCW 2026). Paper

  • 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

  • Outstanding Reviewer, ARR October 2025 Cycle

  • Tinker Research Grant, Thinking Machines Lab
    • Award Amount: $5,000 in Tinker credits
  • 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: CSUR, TKDE 2023, KnowledgeNLP-KDD’23, ICWSM 2024, ICWSM 2025, ARR Review.