PeopleofSuperintelligence

Researcher

Linbo Wang

Linbo Wang is a statistician at the University of Toronto whose research on causal inference connects statistics with machine learning and with robust, explainable and fair AI systems. After undergraduate studies at Peking University, he earned a PhD in biostatistics at the University of Washington in 2016 and spent two years in the Harvard Causal Inference Program with Eric Tchetgen Tchetgen and James Robins. In 2018 he published a multiply robust instrumental-variable estimator with Tchetgen Tchetgen in the Journal of the Royal Statistical Society: Series B, and in 2022 he received the ICSA Outstanding Young Researcher Award. His machine-learning papers include NeurIPS work on generalization measures in 2020 and on causal bandits in 2022, and in 2026 he published the synthetic instrument for sparse causal effects. He received a Canada Research Chair in Causal Machine Learning in 2025 and is an associate professor of statistical sciences at Toronto and a faculty affiliate of the Vector Institute.

Badges

Timeline

  1. 2016

    A biostatistics PhD at Washington

    After undergraduate studies at Peking University, he completes a PhD in biostatistics at the University of Washington in March 2016.

  2. 2018

    Instrumental variables in JRSSB

    With Eric Tchetgen Tchetgen, he publishes "Bounded, Efficient and Multiply Robust Estimation of Average Treatment Effects Using Instrumental Variables" in the Journal of the Royal Statistical Society: Series B in 2018. The paper proposes assumptions that identify the average treatment effect with an instrumental variable and builds multiply robust estimators that are consistent in the union of three observed-data models.

  3. By 2019

    Assistant professor at Toronto

    After two years in the Harvard Causal Inference Program, where he works with Eric Tchetgen Tchetgen and James Robins, he is by February 2019 an assistant professor in the Department of Statistical Sciences at the University of Toronto and the Department of Computer and Mathematical Sciences at U of T Scarborough.

  4. 2020–2022

    NeurIPS work on generalization and bandits

    He coauthors "In Search of Robust Measures of Generalization" at NeurIPS 2020, which argues that measures of deep-learning generalization should be evaluated within the framework of distributional robustness. In 2022, he coauthors "Adaptively Exploiting d-Separators with Causal Bandits" with Blair Bilodeau and Dan Roy, an oral presentation at NeurIPS whose algorithm needs no oracle knowledge of whether a d-separator is observed.

  5. 2022

    ICSA Outstanding Young Researcher Award

    The International Chinese Statistical Association gives him its 2022 Outstanding Young Researcher Award for research in causal inference, notably in instrumental variable estimation, variable selection and the identification of causal effects.

  6. 2023–2026

    The synthetic instrument

    With Dingke Tang and Dehan Kong, he posts the synthetic instrument to arXiv in April 2023. It is a device built directly from observed exposures that identifies causal effects under sparse causation despite unmeasured confounding, and the paper appears in the Journal of the Royal Statistical Society: Series B in 2026.

  7. 2025

    A Canada Research Chair

    In 2025, he receives a Canada Research Chair in Causal Machine Learning and an Ontario Early Researcher Award. He says he is excited to show how causal inference can help develop robust, explainable and fair AI systems, especially for high-stakes applications.

  8. As of October 2026

    Associate professor at Toronto

    He is an associate professor in the Department of Statistical Sciences and the Department of Computer and Mathematical Sciences at the University of Toronto, with a cross-appointment in computer science. He is also a faculty affiliate at the Vector Institute and an affiliate associate professor at the University of Washington.

Career

  1. Since 2019University of TorontoAssistant Professor
  2. Since 2019University of Toronto ScarboroughAssistant Professor
  3. NowVector InstituteFaculty Affiliate
  4. NowUniversity of WashingtonAffiliate Associate Professor

Education

  1. 2016University of WashingtonPhD in biostatistics
  2. Peking UniversityUndergraduate studies

From the biography.

  1. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes University of Washington. 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.
  2. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of Washington. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  3. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes Vector Institute. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.
  4. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes University of Washington. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.

Colleagues

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

Sources and credits

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  1. Advancing Trustworthy AI: Professor Linbo Wang’s Research at U of T Scarborough utsc.utoronto.ca. Checked 21 October 2026.
  2. Dr. Linbo Wang (University of Toronto) | Mathematics and Statistics - McGill University mcgill.ca. Checked 21 October 2026.
  3. Linbo Wang - PUBLICATIONS sites.google.com. Checked 21 October 2026.
  4. Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables arxiv.org. Checked 21 October 2026.
  5. Linbo Wang | Computer and Mathematical Sciences utsc.utoronto.ca. Checked 21 October 2026.
  6. In Search of Robust Measures of Generalization arxiv.org. Checked 21 October 2026.
  7. Adaptively Exploiting d-Separators with Causal Bandits proceedings.neurips.cc. Checked 21 October 2026.
  8. 2022 ICSA Outstanding Young Researcher Award icsa.org. Checked 21 October 2026.
  9. Linbo Wang - Vector Institute for Artificial Intelligence vectorinstitute.ai. Checked 21 October 2026.
  10. The synthetic instrument: From sparse association to sparse causation arxiv.org. Checked 21 October 2026.
  11. Linbo Wang sites.google.com. Checked 21 October 2026.
  12. Linbo Wang | University of Washington Department of Statistics stat.uw.edu. Checked 21 October 2026.