Mileva Van Tuyl
Senior Software Engineer at Analog Devices
Boston, Massachusetts, United States
Summary
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Rockstar🎓
Top SchoolMileva Van Tuyl is a Senior Software Engineer based in Boston with six years of experience building Python and cloud-native systems that process sensor and health data for ML research and productization. She has moved from data science roles into production software engineering at Analog Devices, where she develops pipelines for edge device telemetry, and previously built ML-driven web apps to improve outcomes for Type 1 diabetics. An open-source contributor to IBM’s AIF360 project, she implemented practical R bindings and usability fixes to a widely used fairness toolkit, demonstrating attention to both tooling and ethical ML. Her background blends academic research at MIT Media Lab and Wellesley with applied engineering, and she brings an unusual combination of signal-processing experience (wearable earbuds project) and curriculum development that supports clear technical communication.
6 years of coding experience
6 years of employment as a software developer
Visiting Student Informatics, Visiting Student Informatics at The University of EdinburghBachelor of Arts - BA Computer Science (Major) Music (Minor), Bachelor of Arts - BA Computer Science (Major) Music (Minor) at Wellesley CollegeMaster of Science - MS Applied Data Science, Master of Science - MS Applied Data Science at New College of Florida
Github Skills (5)
machine-learning10
f10
n10
r10
python4
Programming languages (2)
Github contributions (5)
Trusted-AI/AIF360
Jun 2022 - Jun 2022
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 reviews, 10 commits, 9 PRs in 29 days
Contributions summary:Mileva primarily contributed to the R implementation of the AIF360 library. Their work focused on enhancing the R package by adding functionality like default arguments to existing methods, fixing parameter names, and renaming functions. The commits also included the addition of a new dataset and modifications to existing test files to handle failures. These changes aimed to improve the usability and functionality of the R package for fairness-aware machine learning.
fairness-mlpythondiscriminationbias-reductionfairness-testing
milevavantuyl/AIF360
Jun 2022 - Jan 2025
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Contributions:27 pushes, 17 branches in 2 years 8 months
fairness-mlexplanationsmachine-learningbiasdatasets
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