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My expertise lies in designing and implementing custom machine learning solutions that drive research and development, with a focus on AI-powered decision-making. 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 9-year tenure at the U.S. Department of Energy’s Oak Ridge National Laboratory, 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. My primary interest lies in developing the "smart labs" of the future, where human-AI collaboration paves the way for rapid scientific innovation and practical applications in various fields.

• 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

• 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
• Probing Electron Beam Induced Transformations on a Single-Defect Level via Automated Scanning Transmission Electron Microscopy. ACS Nano (2022). https://doi.org/10.1021/acsnano.2c07451
• From atomically resolved imaging to generative and causal models. Nature Physics (2022). https://doi.org/10.1038/s41567-022-01666-0
• Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries. Advanced Materials 2201345 (2022). https://doi.org/10.1002/adma.202201345
• See the full list here