Zachary Charles
Research Scientist at Google
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolZachary Charles is a research scientist at Google with a decade of experience building scalable machine learning platforms and algorithms, specializing in federated learning and optimization. With a PhD in Math & Computer Science and prior roles from postdoc research to the AI Residency, he blends deep theoretical insight with hands-on engineering. He contributes to prominent open-source projects like TensorFlow Federated and Google Research’s federated learning codebases, driving practical improvements and API modernizations across distributed ML tooling. Known as a mathematician at heart, he focuses on efficient, provable methods for decentralized training and differential privacy. Based in Seattle, Zachary bridges rigorous research (ICML/NeurIPS publications) with production-grade implementations that improve interoperability and evaluation of model weights.
10 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Mathematics and Computer Science, Doctor of Philosophy - PhD, Mathematics and Computer Science at University of Wisconsin-MadisonBachelor's degree, Mathematics and Computer Science, Bachelor's degree, Mathematics and Computer Science at University of Pennsylvania
Stackoverflow
196reputation
2kreached
10answers
0questions
Github Skills (10)
machine-learning10
tensorflow10
python10
federated-learning10
api-design8
unit-testing7
keras6
random-seed6
google-colaboratory6
scikit-learn6
Programming languages (2)
Github contributions (5)
google-research/federated
Oct 2020 - May 2022
A collection of Google research projects related to Federated Learning and Federated Analytics.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:20 reviews, 178 commits, 3 PRs in 1 year 8 months
Contributions summary:Zachary contributed to the repository by removing instances of the deprecated `tf.io.gfile.glob` function in the `CheckpointManager` class, likely to address compatibility issues within the project's TensorFlow framework. Furthermore, the user modified the `training_utils.py` and `training_utils_test.py` files to allow the evaluation of `ModelWeights` using various trainable attributes. Additionally, the user added a method for sample-based evaluation, introducing new functionality to the `training_utils_test.py` class.
analyticsfederated-analyticsmachine-learningkubernetesfederated-learning
google-parfait/tensorflow-federated
Nov 2019 - Jan 2023
An open-source framework for machine learning and other computations on decentralized data.
Role in this project:ML Engineer
ML EngineerContributions:4 releases, 4 reviews, 318 commits in 3 years 2 months
Contributions summary:Zachary's commits involve replacing calls to `tff.federated_apply` with `tff.federated_map` across multiple files related to the `tensorflow_federated` library. These changes include modifications to the `encoding_utils.py`, `reference_executor_test.py`, `federated_executor_test.py`, `differential_privacy.py`, `optimizer_utils_test.py`, `canonical_form_utils.py`, `lambda_executor_test.py`, `canonical_form.py`, `optimizer_utils.py`, `simple_fedavg.py`, and `computation_utils_test.py` files. The consistent focus on the TFF library, suggests expertise in and contributions to this framework.
pytorchdeep-learningmachine-learningsecure-computationfederated-learning
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