https://github.com/ziatdinovmax/ziatdinovmax
https://github.com/ziatdinovmax/ziatdinovmax
Last synced: 8 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/ziatdinovmax/ziatdinovmax
- Owner: ziatdinovmax
- Created: 2021-07-04T23:15:18.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-11-18T02:32:06.000Z (about 1 year ago)
- Last Synced: 2025-02-05T17:12:00.483Z (10 months ago)
- Size: 75.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
### Hi there 👋
My expertise lies in designing and implementing custom machine learning solutions that drive research and development, with a current focus on AI-powered materials design and characterization. With a proven track record of collaborating closely with academic and industry partners, I excel at translating complex domain-specific challenges into efficient machine learning codes and workflows. During my 10-year tenure at the U.S. Department of Energy’s national labs (ORNL and PNNL), I led the development of machine learning codes that enabled autonomous experimentation in scanning probe and electron microscopy, and were later extended to neutron scattering experiments, chemical synthesis, and battery state-of-health assessments. To support my peers, I have authored multiple widely used open-source software packages, such as AtomAI and GPax, which streamline machine learning integration into experimental research. I also introduced the concept of the Jupyter paper to enhance transparency and reproducibility in research. My vision for the future is one where human-AI collaboration paves the way for rapid scientific innovation and practical applications.
### My Latest Blog Posts 📖:
- [Unknown Knowns, Bayesian Inference, and structured Gaussian Processes](https://towardsdatascience.com/unknown-knowns-bayesian-inference-and-structured-gaussian-processes-why-domain-scientists-know-4659b7e924a4)
- [Deep Learning Meets Gaussian Process: How Deep Kernel Learning Enables Autonomous Microscopy](https://ziatdinovmax.medium.com/deep-learning-meets-gaussian-process-how-deep-kernel-learning-enables-autonomous-microscopy-58106574cfeb)
- [Gaussian Process: First Step Towards Active Learning in Physics](https://ziatdinovmax.medium.com/gaussian-process-first-step-towards-active-learning-in-physics-239a8b260579)
- [Mastering the shifts with variational autoencoders](https://towardsdatascience.com/mastering-the-shifts-with-variational-autoencoders-ca609ec84f1)
### My Recent Papers 📜
- "Dynamic STEM-EELS for Single-Atom and Defect Measurement During Electron Beam Transformations." [**Science Advances (2024)**](https://doi.org/10.1126/sciadv.adn5899). *Contribution: Developed a deep learning-based rapid object detection and action system (RODAS) and oversaw its implementation on a multi-million-dollar electron microscope.*
- "Experimental Discovery of Structure-Property Relationships in Ferroelectric Materials via Active Learning." [**Nature Machine Intelligence (2022)**](https://doi.org/10.1038/s42256-022-00460-0). *Contribution: Developed an automated workflow for active learning of the relationship between local structures and physical properties in multi-modal experiments.*
- "From Atomically Resolved Imaging to Generative and Causal Models." [**Nature Physics (2022)**](https://doi.org/10.1038/s41567-022-01666-0). *Contribution: Introduced AI-driven extraction of domain-specific information from microscopy data for building generative models over a broader parameter space and exploring causal mechanisms underpinning functionalities.*
- "Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries." [**Advanced Materials (2022)**](https://doi.org/10.1002/adma.202201345). *Contribution: Developed an active hypothesis learning approach based on co-navigation of the hypothesis and experimental spaces in automated experiments, allowing physics discovery via active learning of competing hypotheses.*
- See the full list [here](https://scholar.google.com/citations?hl=en&user=YnSdOoUAAAAJ&view_op=list_works&sortby=pubdate)
### My Recent Patents 💡
- Ziatdinov, Maxim A., et al. "Science-driven automated experiments." U.S. Patent No. 11,982,684. 14 May 2024.