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.

• Unknown Knowns, Bayesian Inference, and structured Gaussian Processes
• Deep Learning Meets Gaussian Process: How Deep Kernel Learning Enables Autonomous Microscopy
• Gaussian Process: First Step Towards Active Learning in Physics
• Mastering the shifts with variational autoencoders

• "Dynamic STEM-EELS for Single-Atom and Defect Measurement During Electron Beam Transformations." Science Advances (2024). 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). 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). 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). 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

• Ziatdinov, Maxim A., et al. "Science-driven automated experiments." U.S. Patent No. 11,982,684. 14 May 2024.