Chen Cen - ML Mobile Engineer at Argmax

Chen Cen

ML Mobile Engineer at Argmax

California, United States

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Summary

👤

Senior

🎓

Top School

Chen Cen is a seasoned ML mobile engineer with 12 years of experience bringing state-of-the-art transformer models and on-device inference to production mobile apps. He helped design TensorFlow Lite Task APIs at Google and later built cross-platform mobile ML SDKs at Stripe—work that was featured at Google I/O—and now focuses on deploying SOTA models to resource-constrained devices at Argmax. His open-source contributions include implementing a BERT-based NLClassifier for TensorFlow Lite Support, adding Java APIs, tokenization, and robust tensor handling for mobile NLP. Comfortable across Android, iOS, React Native and native C++, Chen combines deep ML engineering with pragmatic SDK design, and has a track record of shipping infrastructure used by billions of devices.

12 years of coding experience

13 years of employment as a software developer

Machine Learning, Machine Learning at Stanford University

B.S Computer Science, B.S Computer Science at Central South University

MS Computer Science, MS Computer Science at Stony Brook University

Chinese, English, Japanese

Github Skills (9)

tokenize10

machine-learning10

tokenizer10

nlp10

tensorflow-lite10

bert10

java9

metadata9

javas9

Programming languages (3)

JavaC++Jupyter Notebook

Github contributions (5)

tensorflow/tflite-support

Jul 2020 - Feb 2021

TFLite Support is a toolkit that helps users to develop ML and deploy TFLite models onto mobile / ioT devices.

Role in this project:

ML Engineer

Contributions:31 commits in 6 months

Contributions summary:Chen primarily contributed to the development of a Natural Language Classifier (NLClassifier) extension for Bert models within the TFLite support library. Their work involved creating new files, modifying existing ones to incorporate the new classifier, and implementing methods to preprocess and postprocess text data. The contributions included the addition of Java implementations and APIs for the BertNLClassifier, and involved tokenization, and also addressed the handling of various output tensor types (such as bool) in NLClassifier.

iot-devicesmachine-learningtflite-modelstensorflowtflite

flamearrow/Bitemap

Feb 2015 - Jan 2016

Contributions:92 commits, 79 pushes, 1 branch in 10 months

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