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Read About Jennifer Pan

Read About Jennifer Pan

· Sir Robert Ho Tung Professor of Chinese Studies, Professor of Communication, Senior Fellow at the Freeman Spogli Institute for International Studies and Professor, by courtesy, of Political Science and of Sociology

Stanford University · Communication

Active 1995–2026

h-index58
Citations15.0k
Papers524143 last 5y
Funding$1.2M

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

Jennifer Pan is a political scientist whose research focuses on political communication, digital media, and authoritarian politics. She is the Sir Robert Ho Tung Professor of Chinese Studies, Professor of Communication, and a Senior Fellow at the Freeman Spogli Institute for International Studies. Additionally, she holds courtesy appointments in Political Science and Sociology. Dr. Pan's research employs experimental and computational methods with large-scale datasets on political activity to explore the role of digital media in politics, including how political censorship, propaganda, and information manipulation operate in the digital age, and how these influence political preferences and behaviors. Her work has been published in peer-reviewed journals such as the American Political Science Review, American Journal of Political Science, Journal of Politics, Science, and Nature.

Research topics

  • Computer Science
  • Biology
  • Cell biology
  • Biochemistry
  • Chemistry
  • Medicine
  • Artificial Intelligence
  • Neuroscience
  • Cancer research
  • Operating system

Selected publications

  • Aligning Multimodal Sequential Recommendations via Robust Direct Preference Optimization with Sparse MoE

    arXiv (Cornell University) · 2026-03-31

    preprintOpen accessSenior author

    Preference-based alignment objectives have been widely adopted, from RLHF-style pairwise learning in large language models to emerging applications in recommender systems. Yet, existing work rarely examines how Direct Preference Optimization (DPO) behaves under implicit feedback, where unobserved items are not reliable negatives. We conduct systematic experiments on multimodal sequential recommendation to compare common negative-selection strategies and their interaction with DPO training. Our c…

    PublisherDOI

  • Aligning Multimodal Sequential Recommendations via Robust Direct Preference Optimization with Sparse MoE

    arXiv (Cornell University) · 2026-03-31

    articleOpen accessSenior author

    Preference-based alignment objectives have been widely adopted, from RLHF-style pairwise learning in large language models to emerging applications in recommender systems. Yet, existing work rarely examines how Direct Preference Optimization (DPO) behaves under implicit feedback, where unobserved items are not reliable negatives. We conduct systematic experiments on multimodal sequential recommendation to compare common negative-selection strategies and their interaction with DPO training. Our c…

    PublisherOA PDF

  • Active Learning for Multiple Change Point Detection in Non-stationary Time Series with Deep Gaussian Processes

    ArXiv.org · 2025-05-26

    preprintOpen accessSenior author

    Multiple change point (MCP) detection in non-stationary time series is challenging due to the variety of underlying patterns. To address these challenges, we propose a novel algorithm that integrates Active Learning (AL) with Deep Gaussian Processes (DGPs) for robust MCP detection. Our method leverages spectral analysis to identify potential changes and employs AL to strategically select new sampling points for improved efficiency. By incorporating the modeling flexibility of DGPs with the chang…

    PublisherOA PDFDOI

  • Predict Training Data Quality via Its Geometry in Metric Space

    ArXiv.org · 2025-10-12

    preprintOpen accessSenior author

    High-quality training data is the foundation of machine learning and artificial intelligence, shaping how models learn and perform. Although much is known about what types of data are effective for training, the impact of the data's geometric structure on model performance remains largely underexplored. We propose that both the richness of representation and the elimination of redundancy within training data critically influence learning outcomes. To investigate this, we employ persistent homolo…

    PublisherOA PDFDOI

Recent grants

Frequent coauthors

  • Ke Jian Liu

    Stony Brook School

    44 shared

  • Qiang Yang

    44 shared

  • Xiuyun Zhu

    Nuclear and Radiation Safety Center

    39 shared

  • Zhifeng Qi

    Capital Medical University

    33 shared

  • Xunming Ji

    Chinese Institute for Brain Research

    30 shared

  • Yongmei Zhao

    Capital Medical University

    29 shared

  • Rongqiao He

    Guangzhou Experimental Station

    24 shared

  • Yumin Luo

    Chinese Institute for Brain Research

    19 shared

Awards & honors

  • John S. Knight Journalism Fellowships

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