Andres聽Morales
Senior Software Research Development Engineer at Microsoft
Costa Rica
Summary
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Rockstar馃帗
Top SchoolAndres Morales is a Senior Software Research Development Engineer based in Costa Rica with a strong track record across Microsoft and Amazon and about a decade of professional experience. He combines research-grade rigor with production engineering, moving from mobile development to large-scale software and research-focused roles while earning an MSc in Computer Science from Tecnol贸gico de Costa Rica. At Microsoft he advances applied research into deployable systems, and his open-source contributions to DoWhy show hands-on expertise in causal inference鈥攔efactoring core effect-identification and refutation logic to improve robustness and typing. Comfortable bridging experimentation and engineering, he brings practical impact on both APIs and validation methods, making him effective at turning complex models into reliable, testable software.
4 years of coding experience
11 years of employment as a software developer
Master of Science (MSc), Computer Science, Master of Science (MSc), Computer Science at Tecnol贸gico de Costa Rica
Spanish, English
Github Skills (6)
causal10
machine-learning10
causality10
python10
data-science10
causal-inference10
Programming languages (3)
Github contributions (5)
py-why/dowhy
Sep 2022 - Jan 2023
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:
Data Scientist
Data ScientistContributions:61 reviews, 92 commits, 16 PRs in 4 months
Contributions summary:Andres primarily contributed to the `dowhy` library by refactoring and enhancing the `identify_effect` and `refute_estimate` functions, crucial components for causal inference. Their work involved adding type hints, improving the functional API, and implementing refutation methods, suggesting a focus on improving the library's core functionality related to causal effect estimation and model validation. These changes included refactoring of existing refutation methods for enhanced robustness.
fairness-mlcausal-modelspythoncausalbayesian-networks
andresmor-ms/data-wrangling-components
Feb 2022 - Aug 2022
Processing engine and React components for constructing configuration-based data transformation and processing pipelines.
Contributions:22 pushes, 6 branches in 6 months
transformationreactreact-componentspipelinesdata-transformation
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