PeopleofSuperintelligence

Researcher

Daniel Lüdecke

Daniel Lüdecke is a researcher and R software author whose performance package includes a machine-learning classifier for model families. He co-led UKE's DAVID dementia-care project from 2014 to 2017 and led a secondary-data analysis of a European ageing survey from 2017 to 2020. He published ggeffects in 2018, which presents tidy data frames of marginal effects, and coauthored bayestestR in 2019 on describing Bayesian effects and their uncertainty. He coauthored a 2021 performance paper on assessing, comparing, and testing statistical models; easystats received a 2023 SIPS Mission Award, and the package's classify_distribution function classified model families with machine learning by 2026. As of October 2026, he is a research fellow in UKE's Institute of Medical Sociology at the Center for Psychosocial Medicine, and he coauthored a 2026 non-randomized controlled trial on personalized case management for caregivers of children with chronic illnesses or disabilities.

Now at
Center for Psychosocial Medicine

Timeline

  1. 2014–2017

    The DAVID dementia-care project

    From 2014 to 2017, he co-leads DAVID, a UKE project on dementia diagnosis, acute treatment, and validation in an internal-medicine ward.

  2. 2017–2020

    A European ageing survey

    From 2017 to 2020, he leads UKE's secondary-data analysis of the Survey of Health, Ageing and Retirement in Europe, focused on well-being and psychosocial environment.

  3. 2018

    Marginal effects with ggeffects

    In 2018, he publishes ggeffects, a paper on tidy data frames of marginal effects from regression models.

  4. 2019

    Bayesian effects with bayestestR

    In 2019, he coauthors bayestestR, a package paper on describing effects and their uncertainty, existence, and significance within a Bayesian framework.

  5. 2021–2026

    The performance package

    With four coauthors, he publishes the 2021 performance paper on assessing, comparing, and testing statistical models. By 2026, performance’s classify_distribution function uses machine learning to classify model families, and he is credited as a developer.

  6. 2023

    An easystats Mission Award

    In 2023, the Society for the Improvement of Psychological Science gives easystats a Mission Award and includes Lüdecke among its credited contributors.

  7. 2026

    Personalized case management

    In 2026, he coauthors a non-randomized controlled trial on personalized case management for caregivers of children with chronic illnesses or disabilities.

  8. As of October 2026

    Research fellow at UKE

    As of October 2026, he is a research fellow in UKE’s Institute of Medical Sociology at the Center for Psychosocial Medicine.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Universität Hamburg. 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 Universität Hamburg. 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 has the highest combined h-index among AI organizationsIncludes Universität Hamburg. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.
  4. 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.

Sources and credits

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  1. UKE - Medizinische Soziologie - Institut für Medizinische Soziologie uke.de. Checked 22 October 2026.
  2. Journal of Open Source Software: ggeffects: Tidy Data Frames of Marginal Effects from Regression Models joss.theoj.org. Checked 22 October 2026.
  3. Journal of Open Source Software: bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework joss.theoj.org. Checked 22 October 2026.
  4. Journal of Open Source Software: performance: An R Package for Assessment, Comparison and Testing of Statistical Models joss.theoj.org. Checked 22 October 2026.
  5. Classify the distribution of a model-family using machine learning — classify_distribution • performance easystats.github.io. Checked 22 October 2026.
  6. SIPS 2023 Awards Announced! improvingpsych.org. Checked 22 October 2026.
  7. Personalized case management for caregivers of children with chronic illnesses or disabilities: A non-randomized controlled trial lifescience.net. Checked 22 October 2026.
  8. UKE - Wissenschaftlerprofil - Daniel Lüdecke uke.de. Checked 22 October 2026.