PeopleofSuperintelligence

Researcher

Yuan Yuan

Also known as Y. Yuan, 袁媛

Yuan Yuan is a distributed-learning researcher whose work spans networked social learning, adaptive Newton methods and privacy-aware edge training. She earned degrees in information and computing science, operations research and cybernetics, and computer science and technology from Shanxi University and Shandong University, and received the 2021 ACM Qingdao Doctoral Dissertation Award. Her papers include work on distributed social learning, privacy-protected edge training and decentralized continual learning with on-demand unlearning at ICML in 2026. Since 2025, she is an associate professor at Shandong University.

Timeline

  1. 2016–2021

    Degrees in computing and operations

    She earns a bachelor's degree in information and computing science from Shanxi University in 2016, a master's degree in operations research and cybernetics from Shandong University in 2018, and a PhD in computer science and technology from Shandong University in 2021.

  2. 2020

    Distributed social learning

    In 2020, she coauthors Distributed Social Learning with Imperfect Information in IEEE Transactions on Network Science and Engineering.

  3. 2021

    An ACM dissertation award

    She receives the 2021 ACM Qingdao Doctoral Dissertation Award.

  4. 2023–2025

    A postdoctoral appointment

    From 2023 to 2025, she works as a postdoc at Shandong University.

  5. 2023–2025

    Adaptive pruning-based Newton

    In 2023, she coauthors Adaptive pruning-based Newton's method for distributed learning. Its DANL algorithm uses a simple Hessian initialization, adaptively allocates training regions, and adapts to available resources; its analysis proves linear convergence. The article appears in Theoretical Computer Science in February 2025.

  6. 2024

    Privacy-protected edge training

    In 2024, she coauthors Distributed Learning for Large-Scale Models at Edge With Privacy Protection in IEEE Transactions on Computers. The paper proposes DL-DP for distributed training with differential privacy.

  7. 2026

    On-demand unlearning at ICML

    In 2026, she coauthors Forgetting Whenever You Want, an ICML paper that proposes decentralized class continual learning with on-demand unlearning.

  8. Since 2025

    Associate professor at Shandong

    Since 2025, she is an associate professor at Shandong University.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Shandong University. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  2. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Shandong University. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  3. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  4. The United States employs an estimated 48% of people shaping AIWe estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Yuan Yuan yet. Send us a free-licensed photo.

  1. 袁媛 博士 bdi.qlu.edu.cn. Checked 22 October 2026.
  2. ACM中国图灵大会, ACM TURC 2023 acmturc.com. Checked 22 October 2026.
  3. YUAN YUAN (Shandong University): GPU System Support — Lacuna lacuna.tiptreesystems.com. Checked 22 October 2026.
  4. [2308.10154] Adaptive pruning-based Newton's method for distributed learning arxiv.org. Checked 22 October 2026.
  5. TKN - Literature Database Entry tkn.tu-berlin.de. Checked 22 October 2026.
  6. Distributed Learning for Large-Scale Models at Edge With Privacy… lacuna.tiptreesystems.com. Checked 22 October 2026.
  7. Forgetting Whenever You Want: A Decentralized Continual Learning Framework with On-Demand Unlearning proceedings.mlr.press. Checked 22 October 2026.