Yusuke Watanabe
AI Scientist at OpsLab
Austin, Texas, United States
Summary
👤
Senior🎓
Top SchoolYusuke Watanabe is an AI Scientist with 11 years of experience bridging rigorous ML theory and production-grade systems, holding a Ph.D. in Mathematics and Statistics. He has delivered impactful NLP and deep learning solutions at Amazon—such as a retrieval QA system that cut errors by 56% and a BERT-based extractor that doubled product attribute coverage—and built domain adaptation, activity-recognition, and probabilistic-inference methods earlier in his career at Sony. As a pragmatic researcher-engineer, he contributes to open-source deep learning tooling (example VAE work in the Chainer repo) and has shipped commercial prototypes including healthcare chatbots, a ChatGPT-powered Chrome extension, and blockchain DApps. Based in Austin, he combines strong publication and review credentials with hands-on cloud and systems design, routinely turning theoretical ideas into cost-saving, deployable algorithms.
11 years of coding experience
13 years of employment as a software developer
Bachelor's degree Physics, Bachelor's degree Physics at Kyoto UniversityDoctor of Philosophy (Ph.D.) Mathematics and Statistics, Doctor of Philosophy (Ph.D.) Mathematics and Statistics at The Graduate University for Advanced Studies
Japanese, English
Github Skills (17)
python10
chainer10
data-manipulation10
machine-learning10
numpy10
deep-learning10
mnist10
neural-network10
gpu9
cuda9
dataprep8
data-loading8
data-preprocessing8
tensor8
preprocess8
Programming languages (3)
Github contributions (5)
chainer/chainer
Feb 2016 - Nov 2016
A flexible framework of neural networks for deep learning
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 9 PRs, 26 comments in 8 months
Contributions summary:Yusuke's contributions primarily focused on developing and training a Variational Autoencoder (VAE) model within the Chainer deep learning framework. Their work included creating example code for training and evaluating the VAE on the MNIST dataset, along with the necessary data loading and preprocessing functions. Furthermore, the user added functionality to visualize the results by sampling from the latent space and reconstructing images. Minor fixes and improvements were also made to the example code.
cudapythonmxnetcaffe2flexible-framework
ywatanabex/chainer
Oct 2015 - Feb 2016
Contributions:2 PRs, 3 pushes, 1 branch in 3 months
flexible-frameworkdeep-learningneural-networksmachine-learningneural-network
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