Chao Ma - Co-Founder at Snarkify

Chao Ma

Co-Founder at Snarkify

Seattle, Washington, United States

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Summary

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Rockstar

🎓

Top School

Chao Ma is a Co-Founder and seasoned distributed-systems engineer with 12 years of experience building scalable backend and ML infrastructure, currently based in Seattle. He brings a strong research foundation from a Ph.D. in Mathematics to practical roles spanning applied science at Amazon, protocol architecture at Harmony, and privacy and scaling work for the Ethereum Foundation. An active open-source contributor, he’s contributed core data-loading, RPC and distributed-training improvements to high-profile graph learning projects such as DGL and DGL-KE, enabling large-scale knowledge-graph embedding and distributed samplers. Comfortable moving between low-level debugging and system design, he has repeatedly delivered production-ready distributed training and backend systems for both startups and crypto/public-good platforms.

12 years of coding experience

8 years of employment as a software developer

Bachelor of Science (BS) Mathematics, Bachelor of Science (BS) Mathematics at University of Science and Technology of China

Doctor of Philosophy (Ph.D.) Mathematics, Doctor of Philosophy (Ph.D.) Mathematics at University of Colorado Boulder

Master of Science - MS Mathematics, Master of Science - MS Mathematics at Zhejiang University

Github Skills (17)

c-language10

distributed-training10

python10

data-engineering10

graph10

machine-learning10

data-serialization10

serialization10

deserialization10

rpc10

knowledge-graph10

cprogramming-language10

dgl10

pytorch9

distributed-systems9

Programming languages (4)

C++CHTMLPython

Github contributions (5)

awslabs/dgl-ke

Mar 2020 - Jul 2020

High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.

Role in this project:

Back-end Developer & Data Engineer

Contributions:98 commits, 89 PRs, 51 pushes in 4 months

Contributions summary:Chao's contributions primarily focused on the core logic and data handling within the DGL-KE project. They modified the `KGDataset.py` file, suggesting involvement in data loading and processing related to knowledge graphs. Further, they implemented and modified scripts to run the distributed training setting including the hyper-parameters configuration, indicating a focus on the backend and the ability to run the backend at scale.

graph-learningknowledge-graphs-embeddingsgraph-embeddingsknowledge-graphlarge-scale

dmlc/dgl

Oct 2018 - Dec 2020

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Role in this project:

Back-end Developer

Contributions:6 reviews, 93 commits, 231 PRs in 2 years 1 month

Contributions summary:Chao primarily focused on back-end development within the DGL library, specifically addressing compiler errors related to data type conversions on macOS. They debugged and fixed issues related to data serialization and deserialization within the RPC infrastructure, also making several improvements to the RPC infrastructure and distributed sampler APIs. Additionally, the user contributed to the example code related to the distributed sampler.

pytorchpythondeep-learningmachine-learninggraph-neural-networks

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