Guillermo Jiménez
Senior Research Scientist at Google DeepMind
Switzerland
Summary
🤩
Rockstar🎓
Top SchoolGuillermo Jiménez is a Senior Research Scientist at Google DeepMind with nine years of experience at the intersection of machine learning research and dataset engineering. He holds a PhD from EPFL and has contributed to high-impact open-source projects like uncertainty-baselines and TensorFlow Datasets, improving dataset preprocessing, augmentation, and real-world annotation handling for ImageNet and CIFAR variants. His work blends rigorous academic research—shaped by roles at EPFL, Oxford, and Google—with hands-on ML engineering to make reproducible, modular dataset pipelines that support SOTA experiments. Based in Switzerland, he brings a practical focus on dataset quality and tooling that often goes unnoticed but is critical for reliable model evaluation and uncertainty estimation.
9 years of coding experience
2 years of employment as a software developer
BSc. Telecommunications Engineering, BSc. Telecommunications Engineering at Universidad Politécnica de MadridDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at EPFL (École polytechnique fédérale de Lausanne)MSc. Electrical Engineering, MSc. Electrical Engineering at Delft University of Technology
Dutch, French, German, English, Spanish
Github Skills (19)
python10
data-science10
image-classification10
data-set10
machine-learning10
data-model10
numpy10
datasets10
deeplearning-ai10
deep-learning10
tensorflow10
user-data10
data-augmentation10
image-processing9
artificial-neural-networks9
Programming languages (2)
Github contributions (5)
google/uncertainty-baselines
Jul 2022 - Oct 2022
High-quality implementations of standard and SOTA methods on a variety of tasks.
Role in this project:
Data Scientist
Data ScientistContributions:25 commits, 1 PR in 2 months
Contributions summary:Guillermo primarily contributed to the dataset processing and augmentation pipeline for image datasets within the uncertainty-baselines repository. They made changes to the ImageNet dataset, including modifications to the preprocessing steps, and test suites, alongside adjustments to the CIFAR dataset's augmentation parameters and Cifar10H/Cifar100N dataset logic. The contributions include adding features to the base dataset class, suggesting a broader effort to create a modular and reusable dataset infrastructure. These changes appear aimed at supporting various machine learning models and experiments.
implementationsstatisticsdata-sciencedeep-learningneural-networks
tensorflow/datasets
Jul 2022 - Aug 2022
TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
Role in this project:ML Engineer
ML EngineerContributions:6 commits, 1 PR, 1 issue in 1 month
Contributions summary:Guillermo primarily contributes to the development and refinement of datasets related to image classification. Their commits involve adding new datasets like CIFAR100-N and CIFAR10-H, fixing annotation discrepancies, and updating dataset metadata. The changes focus on improving dataset accuracy, data loading, and incorporating real-world annotations for enhanced model training and evaluation.
datanumpydeep-learningdatasetmachine-learning
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