Nao Hiranuma

Director Of Machine Learning Research at Vilya

Seattle, Washington, United States
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Summary

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Senior
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Top School
Nao Hiranuma is Director of Machine Learning Research and scientific co-founder at Vilya, bringing 11 years of deep-learning and computational biology experience to deploy geometric generative models and ML operations. He holds a PhD from the University of Washington’s Allen School and built DeepAccNet, which won CASP14’s Estimation of Model Accuracy against 68 teams, demonstrating rare expertise at the intersection of protein structure and ML. His work spans GNNs, SE(3)-equivariant transformers, 3D convnets and large-scale training pipelines on Slurm, and he’s transitioned academic models into production at Vilya. A hands-on implementer, he routinely re-implements state-of-the-art papers in PyTorch/TensorFlow and optimizes performance across millions of samples. Based in Seattle, he balances research leadership with practical ML engineering, advising how advanced geometric and model-based optimization methods can drive real-world protein design applications.
code11 years of coding experience
job11 years of employment as a software developer
bookDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at University of Washington
bookBachelor of Arts (BA) in Biology, Bachelor of Arts (BA) in Computer Science, 3.8 GPA, Bachelor of Arts (BA) in Biology, Bachelor of Arts (BA) in Computer Science, 3.8 GPA at Carleton College
languagesJapanese, English
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Github Skills (19)

keras9
julia8
python8
pytorch7
genomics5
machine-learning5
gzip4
data-science4
neural-network4
deep-learning4
zlib4
tensorflow4
tflearn2
arxiv2
math-library2

Programming languages (4)

JuliaHTMLJupyter NotebookPython

Github contributions (5)

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hiranumn/PyProtein

Jul 2019 - Jul 2019

Contributions:17 commits, 19 pushes, 1 branch in 4 days
hiranumn/IntegratedGradients

Apr 2017 - Apr 2018

Python/Keras implementation of integrated gradients presented in "Axiomatic Attribution for Deep Networks" for explaining any model defined in Keras framework.
Contributions:52 commits, 3 PRs, 50 pushes in 1 year
keraspython
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