PhD Student at Rice UniversityI specialize in human-centered artificial intelligence, focusing on AI transparency and improving human-robot collaboration. Currently, I’m a researcher in a Human-Centered AI and Robotics Lab, where I explore how AI can help people better understand and work alongside intelligent systems. I aim to design AI that both supports people in their tasks and makes working with technology more intuitive and effective.
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Rice UniversityDepartment of Computer Science
Ph.D. Student, Fall 2025July 2025 - Present -
Rice UniversityDepartment of Computer Science
Master's StudentJan 2024 - May 2025 -
Rice UniversityDepartment of Computer Science
B.S. in Computer Science
Minor in MathematicsAug 2020 - Dec 2023
Experience
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Rice UniversityHuman-Centered AI and Robotics Lab ResearcherApr 2022 - Present
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Rice UniversityGraduate Student Handbook Revision LeadAug 2023 - Present
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Delta Air LinesArtificial Intelligence Engineer InternJun 2024 - Aug 2024
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Rice UniversityComputer Science Teaching AssistantAug 2021 - May 2024
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Rice UniversityJones College WebmasterMar 2022 - Dec 2023
Honors & Awards
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2025 NSF GRFP Honorable MentionApr 2025
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2nd Place, IEEE Computer Society North America Student ChallengeDec 2024
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AIES 2024 Student Travel AwardOct 2024
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Rice Engineering Alumni (REA) Graduate Student Travel GrantOct 2024
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Rice University MS Fellowship AwardJan 2024
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President's Honor RollDec 2023
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Segal AmeriCorps Education AwardAug 2021
News
Selected Publications (view all )
PPS: Personalized Policy Summarization for Explaining Sequential Behavior of Autonomous Agents
Harrison Huang*, Peizhu Qian*, Vaibhav Unhelkar (* equal contribution)
AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2024 Spotlight
Policy summarization methods aim to showcase key examples of agent behaviors to their human users. Yet, existing methods produce “one-size-fits-all” summaries for a generic audience ahead of time. Drawing inspiration from research in pedagogy, we posit that personalized policy summaries can more effectively enhance user understanding. To evaluate this hypothesis, this paper presents and benchmarks a novel technique: Personalized Policy Summarization (PPS). PPS discerns a user’s mental model of the agent through a series of algorithmically generated questions and crafts customized policy summaries to enhance user understanding. Unlike existing methods, PPS actively engages with users to gauge their comprehension of the agent behavior, subsequently generating tailored explanations on the fly. Through a combination of numerical and human subject experiments, we confirm the utility of this personalized approach to explainable AI.
PPS: Personalized Policy Summarization for Explaining Sequential Behavior of Autonomous Agents
Harrison Huang*, Peizhu Qian*, Vaibhav Unhelkar (* equal contribution)
AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2024 Spotlight
Policy summarization methods aim to showcase key examples of agent behaviors to their human users. Yet, existing methods produce “one-size-fits-all” summaries for a generic audience ahead of time. Drawing inspiration from research in pedagogy, we posit that personalized policy summaries can more effectively enhance user understanding. To evaluate this hypothesis, this paper presents and benchmarks a novel technique: Personalized Policy Summarization (PPS). PPS discerns a user’s mental model of the agent through a series of algorithmically generated questions and crafts customized policy summaries to enhance user understanding. Unlike existing methods, PPS actively engages with users to gauge their comprehension of the agent behavior, subsequently generating tailored explanations on the fly. Through a combination of numerical and human subject experiments, we confirm the utility of this personalized approach to explainable AI.