Maxim A Ziatdinov
Dr. Ziatdinov’s research is directed primarily toward the synergy of machine learning, experiment, and theory to accelerate discoveries in physical sciences. This includes the development of physics-based machine learning workflows capable of incorporating existing physical knowledge, the establishment of a critical link between cutting-edge instrumental platforms and high-performance computing facilities, and the enablement of the on-the-fly analysis of streaming data for feedback and instrument control. One of his current main interests is to transform electron and scanning probe microscopy platforms at ORNL into autonomous systems for scientific discovery. Dr. Ziatdinov is a creator and lead developer of several widely used open-source software packages, including AtomAI for deep and machine learning applications in microscopy, pyroVED for applications of invariant autoencoders in the image and spectral analysis, and GPax for physics-based active learning and Bayesian optimization in automated experiments.
IEEE Spectrum: Self-Driving Microscopes to Navigate the Nanoscale