Maxim Ziatdinov
Team Leader - Materials Discovery
Richland, Washington, United States
Summary
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Senior🎓
Top SchoolMaxim Ziatdinov is a Team Leader in Materials Discovery at Pacific Northwest National Laboratory with eight years of experience applying machine learning to experimental materials science. He designs and implements custom ML workflows—ranging from physics-informed active learning and Bayesian optimization to variational autoencoders with built-in invariances—to accelerate microscopy, synthesis, and quantum materials research. Previously he led autonomous electron microscopy projects at Oak Ridge National Laboratory, building streaming-analysis pipelines that close the loop between instruments and high-performance computing. An open-source advocate, he created widely used tools such as AtomAI to help researchers integrate ML into lab workflows. Trained with a PhD in Materials Science from Tokyo Institute of Technology, he blends deep domain knowledge, hands-on instrument experience, and production ML engineering. Colleagues describe him as a pragmatic innovator who turns complex experimental constraints into robust, reproducible software and automated discovery pipelines.
8 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD Materials Sciences, Doctor of Philosophy - PhD Materials Sciences at Tokyo Institute of Technology
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (115)
probabilistic-programming10
image-processing10
language-server-protocol10
semi-supervised-learning10
immunology10
pyro10
theia10
robust10
eclipse10
bayesian-inference10
microscopy10
optuna10
fourier-transform10
representation-learning10
robotics10
Programming languages (4)
Github contributions (5)
ziatdinovmax/Notebooks-for-papers
Nov 2018 - Jan 2021
Contributions:65 commits, 62 pushes, 1 branch in 2 years 2 months
ziatdinovmax/atomai
Feb 2020 - Oct 2022
Deep and machine learning for atomic-scale and mesoscale data
Contributions:17 releases, 1311 commits, 79 PRs in 2 years 8 months
deep-learningmachine-learningscalemulticlass-classificationatomic
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