Xiyang Hu

About

Professor Xiyang Hu leads the Generative Learning and Augmented Decision (GLAD) Lab, which focuses on cutting-edge research in Generative AI, LLM-powered agents, Trustworthy AI, Human-AI Collaboration, and Computational Social Science. The lab emphasizes a collaborative and supportive environment where innovation meets impact, encouraging students and researchers to push boundaries and tackle challenging problems in AI and machine learning. Professor Hu advises Ph.D. students across multiple programs at Arizona State University, including Computer Information Systems, Computer Science, and Data Science, and mentors Master's and undergraduate students with a strong foundation in AI and machine learning. The GLAD Lab fosters a culture of trust, collaboration, creativity, and ambition, aiming to publish in top-tier AI/ML and interdisciplinary venues while supporting members to develop their own research identities and lead projects. Professor Hu's approach highlights the importance of mutual growth, ownership, and belonging within the research community.

Research topics

• Computer Science
• Political Science
• Machine Learning
• Artificial Intelligence
• Statistics
• Data science
• Programming language
• Database

Selected publications

• Data-centric Artificial Intelligence: A Survey

  ACM Computing Surveys · 2025-01-06 · 153 citations

  reviewSenior author

  Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI . The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we…

  PublisherDOI

• Systematizing Confidence in Open Research and Evidence (SCORE)

  2021 · 67 citations

  Assessing the credibility of research claims is a central, continuous, and laborious part of the scientific process. Credibility assessment strategies range from expert judgment to aggregating existing evidence to systematic replication efforts. Such assessments can require substantial time and effort. Research progress could be accelerated if there were rapid, scalable, accurate credibility indicators to guide attention and resource allocation for further assessment. The SCORE program is creati…

  PublisherDOI

• Distilling the knowledge from large-language model for health event prediction

  Scientific Reports · 2024-12-27 · 17 citations

  articleOpen access

  Health event prediction is empowered by the rapid and wide application of electronic health records (EHR). In the Intensive Care Unit (ICU), precisely predicting the health related events in advance is essential for providing treatment and intervention to improve the patients outcomes. EHR is a kind of multi-modal data containing clinical text, time series, structured data, etc. Most health event prediction works focus on a single modality, e.g., text or tabular EHR. How to effectively learn fro…

  PublisherOA PDFDOI

• Fair Machine Learning in Healthcare: A Survey

  IEEE Transactions on Artificial Intelligence · 2024-09-30 · 9 citations

  articleSenior author

  The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (ML) in healthcare. However, these methods can perpetuate or even exacerbate existing disparities, leading to fairness concerns such as the unequal distribution of resources and diagnostic inaccuracies among different demographic groups. Addressing these fairness problems is paramount to prevent further entrenchment of social injustices. In this survey, we analyz…

  PublisherDOI

• TopV: Compatible Token Pruning with Inference Time Optimization for Fast and Low-Memory Multimodal Vision Language Model

  2025-06-10 · 5 citations

  article

  Vision-Language Models (VLMs) demand substantial computational resources during inference, largely due to the extensive visual input tokens for representing visual information. Previous studies have noted that visual tokens tend to receive less attention than text tokens, suggesting their lower importance during inference and potential for pruning. However, their methods encounter several challenges: reliance on greedy heuristic criteria for token importance and incompatibility with FlashAttenti…

  PublisherDOI

Recent grants

• CAREER: Human-Centric Big Network Embedding

  NSF · $397k · 2018–2022

• III: Small: Collaborative Research: A General Feature Learning Framework for Dynamic Attributed Networks

  NSF · $252k · 2017–2021

• CRII: III: Novel Embedding Algorithms for Large-Scale and Complex Attributed Networks

  NSF · $175k · 2017–2020

Frequent coauthors

• Mengnan Du

  New Jersey Institute of Technology

  79 shared

• Ninghao Liu

  70 shared

• Daochen Zha

  Rice University

  61 shared

• Kaixiong Zhou

  50 shared

• Huan Liu

  50 shared

• Na Zou

  Texas A&M University

  46 shared

• Kwei-Herng Lai

  39 shared

• Qingquan Song

  36 shared

Labs

• Generative Learning and Augmented Decision (GLAD) LabPI

  Welcome to our lab! 😆

Education

• Ph.D.

  Carnegie Mellon University

Similar researchers at Rice University

• Xiao Liuh-71
• Jonathan Davish-46
• Zhongju (John) Zhangh-41