Francisco Rivera
Senior Hardware Design Engineer at Intel
Freiburg im Breisgau, Baden-Württemberg, Germany
Summary
🤩
Rockstar🎓
Top SchoolFrancisco Rivera is a Senior Hardware Design Engineer with 7 years of experience bridging hardware design and machine learning software, currently developing AI-driven EDA tools and workflows for Intel Graphics. He combines deep RTL-to-layout expertise and scripting automation from his long Intel tenure with active research in AutoML as a master's student and research assistant at the University of Freiburg. His open-source contributions to prominent AutoML projects like Auto-PyTorch, auto-sklearn and SMAC3 highlight a knack for refactoring core APIs, improving evaluation metrics, and scaling optimization algorithms with parallelism. He has led small teams and delivered practical automation (TCL, Perl, bash) to speed microprocessor design cycles while also contributing to reproducible benchmarking and container support in the AutoML ecosystem. Fluent in turning complex systems into simplified, productive workflows, he pairs hands-on engineering with research-driven innovation. Based in Freiburg, he uniquely blends hardware physical-design know-how with applied ML/AutoML tooling to accelerate design productivity.
7 years of coding experience
7 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at The University of FreiburgMaster of Business Administration, Project Management, Master of Business Administration, Project Management at Universidad Latinoamericana de Ciencia y TecnologiaBachelor's degree, Electrical and Electronics Engineering, Bachelor's degree, Electrical and Electronics Engineering at Universidad de Costa Rica UCR
Spanish, English, German
Github Skills (34)
pytorch10
python10
data-science10
scikit10
pandas10
machine-learning10
hyperparameter-optimization10
deeplearning-ai10
bayesian10
deep-learning10
automl10
optimisation10
scikit-learn10
automated-machine-learning10
optimization10
Programming languages (2)
Github contributions (5)
automl/Auto-PyTorch
Jan 2021 - Jun 2021
Automatic architecture search and hyperparameter optimization for PyTorch
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:269 reviews, 72 commits, 56 PRs in 5 months
Contributions summary:Francisco primarily contributed to the refactoring of the codebase, introducing new features and functionalities. They worked on the core API for automated machine learning tasks, specifically focusing on the base task and related classes. Furthermore, the user made changes related to the training pipeline and metrics, thereby demonstrating their involvement in model development and optimization. The commits show a strong understanding of the AutoPyTorch framework and its integration with PyTorch.
pytorchhyperparameterdeep-learningoptimizationtabular-data
automl/auto-sklearn
Apr 2020 - Aug 2021
Automated Machine Learning with scikit-learn
Role in this project:Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:227 reviews, 81 commits, 91 PRs in 1 year 4 months
Contributions summary:Francisco primarily focused on improving the metrics used for evaluating machine learning models within the auto-sklearn library. Their contributions included incorporating the `balanced_accuracy_score` metric, implementing new evaluation strategies like `iterative_cv`, and supporting calculations for various other metrics such as roc_auc and log_loss. They also addressed critical issues around data validation, multi-label classification, and the clipping of prediction probabilities, enhancing the robustness and functionality of the auto-sklearn framework.
pythonmeta-learningmetalearningbayesian-optimizationhyperparameter-tuning
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