PeopleofSuperintelligence

Researcher

Josef Perktold

Josef Perktold is a statistical computing and econometrics researcher who helps develop the statsmodels and SciPy libraries for Python. After teaching economics at the University of Chicago in 2000 and publishing research on market institutions and household brand choices in 2000 and 2001, he coauthored the paper introducing statsmodels in June 2010. With Wes McKinney and Skipper Seabold, he introduced time series analysis features for scikits.statsmodels in July 2011, and he converted a C function for Halton sequences to Python in June 2013. He coauthored the statsmodels 0.9.0 release with new count models in May 2018, coauthored the SciPy 1.0 paper as an independent researcher in July 2019, and wrote the statsmodels one-way ANOVA module in March 2020. Following his contributions to statsmodels 0.15.0 in August 2026, he is credited as of October 2026 as the author of the treatment-effects example in the statsmodels 0.15.1 development documentation.

Timeline

  1. 2000–2001

    Teaching at Chicago and econometrics papers

    In winter 2000, he teaches Elements of Economic Analysis II in the economics department at the University of Chicago and coauthors "Market Institutions and Quality Enforcement" with Rudolf Kerschbamer and Muriel Niederle for the Econometric Society World Congress 2000. In 2001, he coauthors "Panel data analysis of household brand choices" with Pradeep Chintagunta and Ekaterini Kyriazidou in the Journal of Econometrics.

  2. June 2010

    Presenting statsmodels for Python

    In June 2010, he and Skipper Seabold coauthor "Statsmodels: Econometric and Statistical Modeling with Python," a paper presenting statsmodels as a Python library for statistical and econometric analysis.

  3. July 2011

    Time series analysis in statsmodels

    With Wes McKinney and Skipper Seabold, he coauthors "Time Series Analysis in Python with statsmodels" in July 2011, introducing time series analysis features in scikits.statsmodels.

  4. June 2013

    Halton sequences in Python

    In June 2013, he converts a C function for generating quasi-random Halton sequences to Python.

  5. May 2018

    Count models in statsmodels 0.9.0

    He is listed with Chad Fulton and Kerby Shedden as an author of statsmodels 0.9.0 in May 2018. The 0.9.0 release credits him and Evgeny Zhurko for new count models, including NegativeBinomialP.

  6. July 2019

    The SciPy 1.0 paper

    In July 2019, he coauthors "SciPy 1.0—Fundamental Algorithms for Scientific Computing in Python" as an independent researcher.

  7. March 2020

    One-way statistical tests in statsmodels

    In March 2020, he writes the statsmodels.stats.oneway module for one-way statistical tests, including effectsize_oneway to compute an effect size corresponding to squared Cohen's f.

  8. As of October 2026

    statsmodels 0.15 and treatment effects

    He contributes to the statsmodels 0.15.0 release in August 2026. As of October 2026, he is named in the statsmodels 0.15.1 development documentation as the author of the example on treatment effects under conditional independence.

  1. 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.
  2. 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.
  3. 43% of living people shaping AI work outside their country of birthWe estimate that 43% of living people shaping AI work outside their country of birth (520 of 1,198 people with known birth and employment countries).
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionWe 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.

Sources and credits

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  1. University of Chicago Time Schedules -- Winter 2000 -- Economics timeschedules.uchicago.edu. Checked 22 October 2026.
  2. Market Institutions and Quality Enforcement ideas.repec.org. Checked 22 October 2026.
  3. Panel data analysis of household brand choices ideas.repec.org. Checked 22 October 2026.
  4. StatsModels: Statistics in Python — statsmodels v0.10.2 documentation statsmodels.org. Checked 22 October 2026.
  5. Statsmodels: Econometric and Statistical Modeling with Python - SciPy Proceedings proceedings.scipy.org. Checked 22 October 2026.
  6. Time Series Analysis in Python with statsmodels - SciPy Proceedings proceedings.scipy.org. Checked 22 October 2026.
  7. joepy: Quasi-Random Numbers with Halton Sequences jpktd.blogspot.com. Checked 22 October 2026.
  8. statsmodels · PyPI pypi.org. Checked 22 October 2026.
  9. Release 0.9.0 — statsmodels 0.15.0 statsmodels.org. Checked 22 October 2026.
  10. SciPy 1.0—Fundamental Algorithms for Scientific Computing in Python arxiv.org. Checked 22 October 2026.
  11. statsmodels.stats.oneway — statsmodels 0.15.0 statsmodels.org. Checked 22 October 2026.
  12. Release 0.15.0 — statsmodels 0.15.0 statsmodels.org. Checked 22 October 2026.
  13. Treatment effects under conditional independence — statsmodels 0.15.1 (+577) statsmodels.org. Checked 22 October 2026.