PeopleofSuperintelligence

Researcher

Peter Prettenhofer

Peter Prettenhofer is a machine-learning researcher and engineer whose work spans cross-language text classification, machine-learning software and robotics. At Bauhaus-Universität Weimar's Webis group, he researched cross-lingual information retrieval and domain adaptation before coauthoring papers on cross-language classification and scikit-learn's shared API. At DataRobot, he led machine-learning engineering and explored deep-learning quantile prediction in May 2023; in October 2025, he was Principal Research Engineer at Neo Cybernetica, working on robotic manipulation. As of October 2026, he is affiliated with Rerun and is scheduled to present a PyTorch session on multimodal dataloaders for physical AI.

Now at
Neo Cybernetica and Rerun
Works
31
Citations
72,389
h-index
9
Badges

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 2009–2010

    Research at Webis

    From 2009 to 2010, he researches cross-lingual information retrieval, information extraction, semi-supervised learning and domain adaptation with Bauhaus-Universität Weimar's Webis group.

  2. 2010

    Cross-language text classification

    With Benno Stein, he coauthors the 2010 ACL paper “Cross-Language Text Classification Using Structural Correspondence Learning.”

  3. 2011

    Scikit-learn in Python

    In 2011, he coauthors “Scikit-learn: Machine Learning in Python,” which describes a Python module of supervised and unsupervised algorithms built for non-specialists.

  4. 2013

    A shared scikit-learn API

    In 2013, he coauthors a paper on scikit-learn's shared interface for learning and processing units and how it supports composition and reusability.

  5. May 2023

    DataRobot engineering leadership

    In May 2023, he is DataRobot's VP of Engineering. He leads its machine-learning engineering team and works on product-innovation initiatives in the CTO's office.

  6. May 2023

    Deep learning for quantiles

    In May 2023, he works with a DataRobot colleague on deep-learning models that predict target quantiles directly. The study compares DeepQuantiles and FTTransformer on eight public datasets.

  7. October 2025

    Robotic manipulation at Neo

    In October 2025, he is Principal Research Engineer at Neo Cybernetica, working on embodied AI systems for robotic manipulation. He gives a guest lecture on vision-based policy learning for robotics and dexterous manipulation.

  8. As of October 2026

    Physical AI data at Rerun

    At Rerun, he is scheduled to join Gijs de Jong for a PyTorch Conference session on multimodal dataloaders for physical AI in October 2026. The session is set to cover multimodal temporal datasets and PyTorch dataloaders for complex access patterns.

Career

  1. Since 2025Neo CyberneticaPrincipal Research Engineer
  2. NowRerun
  3. From 2023DatarobotVP of Engineering
  4. 2009–2010Bauhaus-Universität Weimar

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Most-cited works

  1. Scikit-learn: Machine Learning in Python2012 · 69,582 citationsWith Alexandre Passos, Bertrand Thirion, David Cournapeau, Fabian Pedregosa, Matthieu Brucher and Matthieu Perrot
  2. API design for machine learning software: experiences from the scikit-learn project2013 · 2,073 citationsWith Fabian Pedregosa, Olivier Grisel and Vlad Niculae
  3. Cross-Language Text Classification Using Structural Correspondence Learning2010 · 265 citations
  4. Intrinsic plagiarism analysis2010 · Language Resources and Evaluation · 194 citations
  5. Gradient Boosted Regression Trees in Scikit-Learn2014 · 73 citations

From OpenAlex, as of 21 October 2026.

  1. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Bauhaus-Universität Weimar. 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. 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.
  3. 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.
  4. 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).

Colleagues

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

Sources and credits

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  1. Bauhaus-Universität Weimar: Webis People uni-weimar.de. Checked 21 October 2026.
  2. Cross-Language Text Classification Using Structural Correspondence Learning aclanthology.org. Checked 21 October 2026.
  3. Scikit-learn: Machine Learning in Python jmlr.org. Checked 21 October 2026.
  4. API design for machine learning software: experiences from the scikit-learn project arxiv.org. Checked 21 October 2026.
  5. Innovative Technology Insights: Episode 5: Generating the Future of AI with Peter Prettenhofer of DataRobot [PODCAST] natlawreview.com. Checked 21 October 2026.
  6. Deep Learning for Decision-Making Under Uncertainty | DataRobot Blog datarobot.com. Checked 21 October 2026.
  7. Lecture: Training Neural Networks for Robotic Manipulation ist.kpi.ua. Checked 21 October 2026.
  8. Agentic AI and Next-Gen Intelligence Sessions at PyTorch Conference North America 2026 pytorch.org. Checked 21 October 2026.