Patrick Bouchon
Software Engineer at Microsoft
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolPatrick Bouchon is a software engineer with four years of experience building high-throughput cloud services and ML-backed tooling, currently on Microsoft’s engineering team after impactful roles at AWS in CloudWatch. He has hands-on experience designing datapaths, load balancing and retry systems that reduced backend costs and improved latency for large-scale customers. Patrick contributes to open-source causal inference tooling (DoWhy), implementing core graphical causal model interfaces and counterfactual/statistical features that bridge ML and principled inference. A Notre Dame CS graduate based in Austin, he brings a mix of production systems rigor and research-oriented ML thinking, plus early internships in NLP and automation that hint at a breadth across data and engineering.
4 years of coding experience
2 years of employment as a software developer
Valedictorian, Valedictorian at Brother Martin High SchoolBachelor of Science in Computer Science, Computer Science, Bachelor of Science in Computer Science, Computer Science at University of Notre Dame
Spanish
Github Skills (8)
machine-learning10
statistical-models10
python10
causal-inference10
data-analysis10
pytest9
scikit-learn9
scikit9
Programming languages (3)
Github contributions (5)
py-why/dowhy
May 2022 - Dec 2022
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:241 reviews, 118 commits, 404 PRs in 7 months
Contributions summary:Patrick contributed significantly to the development of the 'dowhy/dowhy' repository, focusing on building blocks for a graphical causal model (GCM) API. Their work included implementing core causal model interfaces and implementations, which involved setting up fitting and sampling procedures. Furthermore, the user integrated new features such as counterfactuals and independence tests, with an emphasis on statistical methods such as the GCM approach. The user's contributions demonstrate a strong understanding of causal inference, model fitting, and integration of statistical methods within a machine learning framework.
fairness-mlcausal-modelspythoncausalbayesian-networks
py-why/pywhy-notes
Jun 2022 - Jul 2022
Contributions:4 commits, 4 pushes, 1 branch in 1 month
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.