Jeffrey Ma
Student Researcher at Google
Boston, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolJeffrey Ma is a PhD student and researcher in computer science based in Boston with eight years of hands-on experience building ML systems, infrastructure, and low-latency production software. He has shipped ML infra and model-optimization tools at Nuro and Google (notably contributing ModelRun logging support to TensorFlow Extended) and studied fault resiliency in LLM training at AWS, with work under review for MLSys. His background spans quantitative development at Citadel, applied research at Caltech and Stanford, and teaching core systems and programming courses, giving him a rare combination of production engineering, research rigor, and pedagogy. Jeffrey focuses on automating performance optimization and improving how large language models reason over code at repo scale, blending systems-level thinking with ML model monitoring. He is comfortable moving between back-end system design, ML experimentation, and academic publication, and often translates research prototypes into usable infrastructure. An understated strength is his track record of integrating research contributions directly into widely used open-source tooling for production ML pipelines.
8 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Harvard John A. Paulson School of Engineering and Applied SciencesCalifornia Institute of Technology
Github Skills (6)
machine-learning10
logging10
tensorflow10
trainings10
python10
modeling10
Programming languages (5)
Github contributions (5)
tensorflow/tfx
Jun 2020 - Sep 2020
TFX is an end-to-end platform for deploying production ML pipelines
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:19 reviews, 288 commits, 17 PRs in 2 months
Contributions summary:Jeffrey primarily contributed to defining, implementing, and integrating new artifact types within the TFX framework, including `ModelRun`. Their work involved adding support for the logging of model metrics and performance during training, crucial for model monitoring and analysis. The user modified the `GenericExecutor` to provide output paths for model logs, implemented the writing of logs to the `ModelRun` artifact, and updated examples and tutorials to use the `ModelRun` artifact for storing these logs.
machine-learningtfxtensorflowapache-beam
18jeffreyma/bcpmobileapps
Feb 2018 - Jul 2019
Contributions:5 commits, 58 pushes, 1 branch in 1 year 6 months
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