Hyung Chung
AI Research Scientist at Meta
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolHyung Chung is an AI research scientist with seven years of experience advancing large language models and training infrastructure, currently at Meta after research roles at OpenAI and Google Brain. He was first author on scaling work for Flan models and tech lead for T5X, contributing core infrastructure used to train PaLM and other high-profile transformer models. His hands-on work spans pretraining, instruction fine-tuning, reasoning, multilinguality, and efficient model architectures—reflected in contributions to T5X, Mesh TensorFlow, and the original T5 codebase. Based in Mountain View and holding a PhD from MIT, he blends deep research rigor with practical engineering, often improving model configuration, decoding, and tensor-parallel tooling in widely used open-source repos. An understated strength is his focus on making large-model training both flexible and reproducible, from GIN configs to sharded data pipelines.
7 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Massachusetts Institute of Technology
Github Skills (20)
machine-translation10
system-configuration10
python10
machine-learning10
feature-engineering10
data-preprocessing10
transformer-models10
custom-configuration10
tensorflow10
natural-language-processing10
nlp10
configurations10
yml-configuration10
convolutional-neural-networks9
data-pipelines9
Programming languages (2)
Github contributions (5)
google-research/t5x
Nov 2021 - Jan 2023
Role in this project:
ML Engineer
ML EngineerContributions:9 reviews, 53 commits, 5 PRs in 1 year 2 months
Contributions summary:Hyung primarily contributed to example configurations and model definitions within the T5X framework, particularly focusing on tasks related to machine translation. Their work involved adding and modifying configuration files (GIN files) to specify model parameters, training steps, and dataset configurations for various T5 models, including T5.1.1, mT5, and ByT5 variants. This included adjustments to dropout rates, loss normalization factors, and vocabulary settings. Additionally, the user worked on improving the decoding process, including relative position biases and caching mechanisms.
google-research/text-to-text-transfer-transformer
Oct 2020 - Jul 2022
Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"
Role in this project:ML Engineer
ML EngineerContributions:34 commits in 1 year 9 months
Contributions summary:Hyung made several commits related to the `text-to-text-transfer-transformer` repository, focused on modifying and implementing feature converters. These changes included implementing a feature converter for encoder-decoder architectures, and later for language modeling and prefix language modeling. The work also involved refactoring and improving the data preprocessing steps, and incorporating support for sharded datasets, which are all critical components in training and evaluating transformer models.
pytorchnlplimitsberttransfer-learning
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