Jonas Mueller
Director, AI Research at Handshake
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJonas Mueller is a Director of AI Research in San Francisco with 11 years of experience building data-centric ML systems, from founding Cleanlab (acquired by Handshake) to developing core AutoML at AWS. He combines academic rigor—a PhD from MIT—with hands-on engineering, contributing to prominent open-source projects like AutoGluon and Cleanlab where he improved Bayesian searchers, dataset-quality tooling, and robust image-data checks. Jonas leads a new AI research lab focused on data and evaluation for frontier AI, publishing work and recruiting top researchers while keeping close to production needs. Notably, his work blends algorithmic advances for noisy real-world labels with practical tooling and documentation that make data quality methods accessible to practitioners.
11 years of coding experience
8 years of employment as a software developer
B.A. Applied Mathematics Statistics, B.A. Applied Mathematics Statistics at University of California, BerkeleyPh.D. Electrical Engineering & Computer Science, Ph.D. Electrical Engineering & Computer Science at Massachusetts Institute of Technology
Github Skills (28)
image-analysis10
python10
image-processing10
data-science10
machine-learning10
ensemble-learning10
deeplearning-ai10
bayesian10
deep-learning10
read-me10
optimisation10
ai10
computer-vision10
documentation10
optimization10
Programming languages (6)
Github contributions (5)
cleanlab/cleanvision
May 2022 - Mar 2023
Automatically find issues in image datasets and practice data-centric computer vision.
Role in this project:
ML Engineer
ML EngineerContributions:268 reviews, 36 commits, 35 PRs in 9 months
Contributions summary:Jonas contributed to the development and improvement of the `cleanvision` repository, a project focused on data-centric computer vision. Their commits showcase a focus on image data quality, including checks for corrupted images and near-duplicate detection. The user also implemented lazy import fixes and added unit tests to improve the robustness of the image dataset processing and issue detection functionalities.
computer-visiondata-centric-aidata-explorationdata-profilingdata-quality
cleanlab/cleanlab
Jan 2022 - Jan 2023
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Role in this project:Technical Writer
Technical WriterContributions:7 releases, 2436 reviews, 199 commits in 11 months
Contributions summary:Jonas's commits focused on updating and improving the project's documentation, specifically within the README file. They addressed broken links, corrected formatting inconsistencies, and improved the overall clarity and structure of the documentation, making it easier to understand and navigate. These revisions included changes to the tutorial sections and example code, indicating an active effort to enhance the user experience and provide a more informative resource. Furthermore, they also updated links for code examples.
operationdata-preprocessingtext-annotationreal-world-datadata-labeling
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