PeopleofSuperintelligence

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Martin Wicke

Martin Wicke is a computer scientist whose TensorFlow and TFX work helped developers train and deploy machine-learning models. He earned a Doctor of Science at ETH Zurich in 2007 after undergraduate studies in Kaiserslautern and worked on fluid and solid simulation at Stanford and UC Berkeley, as well as video analysis at HighlightCam. At Google, he coauthored TensorFlow and production-machine-learning papers, supported the TFRT runtime, and contributed to research on monocular-video depth and Gemini. In 2025, he coauthored a system for applying specified modeling changes and evaluating them across machine-learning models.

Now at
Google
Works
60
Citations
29,384
h-index
22
Badges

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 2007

    Doctorate and graphics research

    After undergraduate studies in Kaiserslautern, Germany, he pursues graduate studies at ETH Zurich's Computer Graphics Laboratory with Markus Gross, working on geometric modeling and physically based simulation using particle methods. In 2007, he earns a Doctor of Science for his dissertation, Modeling and Simulation with Relaxed Connectivity Requirements. At the Max Planck Center and Stanford, he researches fluid simulation, sensor-network routing, and indoor localization; he works on unified solid and fluid simulations at UC Berkeley and on video analysis as VP R&D at HighlightCam.

  2. 2015–2016

    TensorFlow goes open source

    In November 2015, TensorFlow's API and reference implementation are released as an open-source package. In 2016, he coauthors TensorFlow: A System for Large-Scale Machine Learning, which describes dataflow graphs that map computations across machines and devices.

  3. 2017

    Estimators and TFX

    In 2017, he coauthors TensorFlow Estimators, a framework for specifying, training, evaluating, and deploying machine-learning models. He also coauthors TensorFlow Extended (TFX), a TensorFlow-based platform implemented at Google to bring models into production.

  4. 2018

    Learning depth from video

    In 2018, he coauthors a CVPR paper on estimating depth and ego-motion from monocular video with consistent 3D geometry across consecutive frames.

  5. 2019

    Maintaining TensorFlow in the open

    In 2019, he works as a software engineer at Google, helping keep TensorFlow active as an open-source project.

  6. 2022

    TFRT runtime support

    In 2022, the TFRT team thanks him for supporting the runtime during its first year.

  7. 2023

    Gemini multimodal models

    In 2023, he coauthors Gemini: A Family of Highly Capable Multimodal Models, a report about models that understand image, audio, video, and text.

  8. 2025

    Automating ML model development at scale

    In 2025, he coauthors Automating ML Model Development at Scale with a team affiliated with Google Inc. The paper introduces HEINZELMAENNCHEN, which applies semantically specified modeling changes to machine-learning models and evaluates them at scale.

Career

  1. Since 2016GoogleSoftware Engineer
  2. 2010–2013University of California, Berkeley
  3. 2008–2010Stanford University
  4. HighlightCamVideo R&D lead

Education

  1. 2007ETH ZurichDoctor of Science

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

Most-cited works

  1. TensorFlow: A system for large-scale machine learning2016 · 16,018 citationsWith Andy Davis, Derek G. Murray, Geoffrey Irving, Martin Abadi, Matthieu Devin and Michael Isard
  2. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems2016 · 9,405 citationsWith Andy Davis, Chris Olah, Derek G. Murray, Fernanda Viégas, Geoffrey Irving and Greg Corrado
  3. Gemini: A Family of Highly Capable Multimodal Models2023 · 988 citationsWith Angeliki Lazaridou, Anja Hauth, Clemens Meyer, David Silver, Emily Pitler and Fabio Viola
  4. Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints2018 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 775 citations
  5. TFX2017 · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · 350 citationsWith Heng-Tze Cheng

From OpenAlex, as of 21 October 2026.

  1. 39% of top AI papers from 2020–2024 list two or more of the people shaping AI, against 16% in 1970–1974Includes Martin Wicke. We estimate that 39% of top papers from 2020–2024 bring together two or more of the people shaping AI, against 16% in 1970–1974. Across 3,593 researchers, 73% co-authored at least one top work with another person we track.
  2. Google had the most people shaping AI every year from 2014 to 2025Includes Google. Our 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. Google has the highest combined h-index among AI organizationsIncludes Google. 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. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes Google. 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

Photo: We have no published photo of Martin Wicke yet. Send us a free-licensed photo.

  1. Short Biography graphics.stanford.edu. Checked 21 October 2026.
  2. Martin Wicke research.google. Checked 21 October 2026.
  3. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems arxiv.org. Checked 21 October 2026.
  4. TensorFlow: A system for large-scale machine learning research.google. Checked 21 October 2026.
  5. TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks arxiv.org. Checked 21 October 2026.
  6. Speaker: Martin Wicke: O'Reilly TensorFlow World | Machine Learning Conference conferences.oreilly.com. Checked 21 October 2026.
  7. TFRT: A Progress Update blog.tensorflow.org. Checked 21 October 2026.
  8. Gemini: A Family of Highly Capable Multimodal Models arxiv.org. Checked 21 October 2026.
  9. Automating ML Model Development at Scale (ICSE 2025 - Software Engineering in Practice (SEIP)) conf.researchr.org. Checked 21 October 2026.