Jinsung Yoon
Research Scientist at Google
Sunnyvale, California, United States
Summary
👤
Senior🎓
Top SchoolJinsung Yoon is a research scientist at Google Cloud AI with eight years of experience advancing generative models, self- and semi-supervised learning, model interpretation, and synthetic data generation. He earned his Ph.D. and M.S. in Electrical and Computer Engineering from UCLA after a B.S. from Seoul National University, and previously conducted machine-learning-for-medicine research with Prof. Mihaela van der Schaar. At Google he has translated research into code—contributing to Google Research projects like RL-based interpretable models and data valuation—and maintains influential open-source work such as the TimeGAN implementation used for time-series synthesis. Comfortable moving between theory and engineering, he blends rigorous academic training with hands-on implementation and debugging of production-ready ML systems. Based in Sunnyvale, he brings domain depth in medical ML and practical expertise in measuring and valuing data for model training.
8 years of coding experience
6 years of employment as a software developer
Bachelor's degree Electrical and Electronics Engineering, Bachelor's degree Electrical and Electronics Engineering at Seoul National UniversityUniversity of California, Los Angeles
Korean, English
Github Skills (13)
machine-learning10
time-series10
deep-learning10
tensorflow10
python10
generative-adversarial-network10
reinforcement-learning10
data-analysis10
scikit-learn9
metric9
scikit9
regression8
ai7
Programming languages (2)
Github contributions (5)
jsyoon0823/TimeGAN
Apr 2020 - Oct 2022
Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019
Role in this project:
ML Engineer
ML EngineerContributions:12 commits, 7 PRs, 10 pushes in 2 years 6 months
Contributions summary:Jinsung's primary contributions revolve around the development and refinement of the TimeGAN model. They initiated the project with the "Initial TimeGAN Commit," demonstrating their role in establishing the project's core functionality. Subsequent commits involved updates to the main files, tutorial, and metrics, and addressing bugs within the discriminative metrics. The commits also reveal the user's efforts in debugging and resolving compatibility issues.
pytorchdeep-learningadversarialneuripsgenerative-adversarial-network
google-research/google-research
Sep 2019 - Oct 2019
Google Research
Role in this project:ML Engineer
ML EngineerContributions:5 commits in 1 month
Contributions summary:Jinsung's commits focus on initial development of Reinforcement Learning based Locally Interpretable Models (RL-LIM) and Data Valuation using Reinforcement Learning (DVRL). The code changes involve defining the architecture, loss functions, and training procedures for these models. The contributions include the implementation of data value estimators and the integration of baseline models for comparison and reward calculation.
googlemachine-learningai
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