PeopleofSuperintelligence

Researcher

Johann Gaebler

Also known as Johann D. Gaebler, Hans Gaebler

Johann Gaebler is a statistician and assistant professor of technology, operations, and statistics at the NYU Stern School of Business whose research develops methods to measure discrimination and make AI systems safer and more trustworthy. After earning a bachelor's degree in mathematics at Harvard University in 2017 and a master's degree in the history of science at the University of Oxford, he joined the Knight-Hennessy Scholars at Stanford University in 2020. His 2022 paper "Causal Conceptions of Fairness and their Consequences," coauthored with Hamed Nilforoshan, Ravi Shroff and Sharad Goel, received an Outstanding Paper award at ICML, and his 2024 paper "A Simple, Statistically Robust Test of Discrimination" with Goel won an EAAMO Best Paper Award. Following a 2026 PhD in Statistics at Harvard University, where he was a Stone PhD Scholar and a researcher in the Computational Policy Lab, he joined NYU Stern in September 2026.

Timeline

  1. 2017

    Mathematics at Harvard, history at Oxford

    He graduates from Harvard University in 2017 with a bachelor's degree in mathematics and earns a master's degree in the history of science from the University of Oxford.

  2. 2020

    Knight-Hennessy Scholar at Stanford

    In 2020, he joins the Knight-Hennessy Scholars at Stanford University, pursuing a PhD in computational and mathematical engineering at the Stanford School of Engineering.

  3. 2022

    An ICML Outstanding Paper award

    In July 2022, he coauthors "Causal Conceptions of Fairness and their Consequences" with Hamed Nilforoshan, Ravi Shroff and Sharad Goel. The paper categorizes popular causal definitions of algorithmic fairness into two broad families, shows that both result in strongly Pareto dominated decision policies, and receives an Outstanding Paper award at ICML in 2022.

  4. 2022

    A causal framework for discrimination

    In 2022, Statistics and Public Policy publishes "A Causal Framework for Observational Studies of Discrimination," which he writes with William Cai, Guillaume Basse, Ravi Shroff, Sharad Goel and Jennifer Hill. The study shows that a primary causal quantity in discrimination studies can be estimated under an ignorability condition that may hold approximately in some observational settings.

  5. 2023

    Measuring algorithmic fairness in JMLR

    In 2023, he coauthors "The Measure and Mismeasure of Fairness" with Sam Corbett-Davies, Hamed Nilforoshan, Ravi Shroff and Sharad Goel in the Journal of Machine Learning Research.

  6. 2024

    A robust test of discrimination

    In July 2024, he and Sharad Goel release "A Simple, Statistically Robust Test of Discrimination," which shows that under a common non-parametric assumption at least one of the benchmark and outcome tests must be correct, and applies the approach to 2.8 million police stops across California. The paper receives the Best Paper Award at the Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) Conference in 2024.

  7. 2026

    A Harvard PhD in statistics

    In 2026, he completes a PhD in Statistics at Harvard University with the thesis "The Statistics of Discrimination," having served as a Stone PhD Scholar and a researcher in the Computational Policy Lab. His interests in the lab include data science tools for social problems such as mass incarceration and hiring discrimination.

  8. Since September 2026

    Assistant professor at NYU Stern

    He joins the NYU Stern School of Business as an assistant professor of technology, operations, and statistics in September 2026. His research develops statistical and computational methods to improve organizational decisions and public policy and to make AI systems safer and more trustworthy, with work on responsible AI and alignment.

Career

  1. Since 2026New York UniversityAssistant Professor of Technology, Operations, and Statistics
  2. Harvard UniversityResearcher

Education

  1. From 2020Stanford UniversityPhD
  2. Harvard UniversityBachelor's degree
  3. University of OxfordMaster's degree

From the biography.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes New York University. 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.
  2. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes New York University. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  3. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes New York University. Our estimates of the organizations where 550 people worked directly before joining 9 frontier AI labs across 620 career transitions, comparing the main feeder organizations per lab.
  4. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes New York University. 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 named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. How common is debt imprisonment in U.S. today? — Harvard Gazette news.harvard.edu. Checked 22 October 2026.
  2. Hans Gaebler | Knight-Hennessy Scholars at Stanford University knight-hennessy.stanford.edu. Checked 22 October 2026.
  3. ICML Poster Causal Conceptions of Fairness and their Consequences icml.cc. Checked 22 October 2026.
  4. [2207.05302] Causal Conceptions of Fairness and their Consequences arxiv.org. Checked 22 October 2026.
  5. A Causal Framework for Observational Studies of Discrimination | Harvard Kennedy School hks.harvard.edu. Checked 22 October 2026.
  6. The Measure and Mismeasure of Fairness jmlr.org. Checked 22 October 2026.
  7. [2407.06539] A Simple, Statistically Robust Test of Discrimination arxiv.org. Checked 22 October 2026.
  8. Johann D. Gaebler - NYU Stern stern.nyu.edu. Checked 22 October 2026.
  9. ACM EAAMO Conference Awards · EAAMO Conference 2026 conference2026.eaamo.org. Checked 22 October 2026.
  10. Computational Policy Lab: People policylab.hks.harvard.edu. Checked 22 October 2026.
  11. Research | Jgaeb jgaeb.com. Checked 22 October 2026.