Emily Glanz
Senior Software Engineer at Google
Greater Seattle Area United States
Summary
👤
Senior🎓
Top SchoolEmily Glanz is a Senior Software Engineer with a decade of experience building federated learning infrastructure on Google’s federated learning and analytics team. She combines an electrical engineering background (B.S.E., University of Iowa, high distinction) with practical ML engineering—contributing to TensorFlow Federated by implementing and testing aggregation primitives such as federated min/max and sampling. Her early research work on genomic ML and class-imbalance techniques (SMOTE) informs a pragmatic approach to real-world data challenges in decentralized settings. Emily has taught CS to high school students and served as a TA, demonstrating an ability to communicate complex ideas clearly and mentor others. Based in the Greater Seattle Area, she bridges research and production, shipping robust, test-driven features that advance open-source federated learning toolkits.
10 years of coding experience
2 years of employment as a software developer
CIMBABachelor’s Degree, Electrical Engineering with Honors in Department and High Distinction, Bachelor’s Degree, Electrical Engineering with Honors in Department and High Distinction at University of Iowa
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (9)
ag10
machine-learning10
aggregate10
aggregates10
aggregations10
tensorflow10
federated-learning10
python10
aggregator10
Programming languages (3)
Github contributions (5)
google-parfait/tensorflow-federated
Jun 2019 - Oct 2022
An open-source framework for machine learning and other computations on decentralized data.
Role in this project:
ML Engineer
ML EngineerContributions:17 commits, 1 PR in 3 years 4 months
Contributions summary:Emily contributed to the TensorFlow Federated (TFF) framework by implementing and testing aggregation functions, specifically focusing on federated minimum, maximum, and sampling aggregations. These contributions involved modifying core utility functions and test files within the TFF codebase. The user's work resulted in the addition of new functionalities related to data aggregation in a federated learning context.
pytorchdeep-learningmachine-learningsecure-computationfederated-learning
google/generativemloncloud
Sep 2017 - Jan 2018
Contributions:2 commits, 1 PR, 3 pushes in 4 months
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