Michelle Yang
United States
Summary
🤩
Rockstar🎓
Top SchoolMichelle Yang is a University of Washington-trained computer scientist (B.S., 2020) with a strong quantitative background through minors in Chemistry and Mathematics and 11 years of experience in software engineering. She blends rigorous problem-solving skills from her STEM minors with practical software development expertise, making her adept at tackling complex, multidisciplinary problems. Based in the United States, Michelle brings academic depth and hands-on coding acumen to projects that intersect computational and scientific domains. Her profile reflects a long-standing commitment to engineering growth since her undergraduate start in 2016, suggesting early and sustained involvement in software work. Though not heavily represented on public GitHub, her academic combination implies strengths in algorithmic thinking, data analysis, and reliable system implementation.
11 years of coding experience
Bachelor of Science, Computer Science, Bachelor of Science, Computer Science at University of Washington
Github Skills (50)
dsl10
strongly-typed10
feature-engineering10
automl10
structured-data10
automated-machine-learning10
einstein10
salesforce10
spark9
transformers9
scala9
machine-learning9
machine-learning-workflow9
ai9
ml8
Programming languages (9)
Github contributions (5)
MWYang/TransmogrifAI
Oct 2019 - Jan 2020
TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning
Contributions:1 PR, 78 pushes, 63 branches in 3 months
pythondata-sciencemachine-learningmlopsspark
MWYang/AEDT-Explainer
Mar 2022 - Sep 2022
Harvard Kennedy School applied thesis on automated decision systems in city government.
Contributions:8 pushes in 6 months
harvarddecisionmachine-learningthesiscity-government
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