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Many of the new characterization tools for materials are now generating enormous datasets which contain a wealth of new information that was not previously available. This “Big Data” revolution will transform our understanding of materials in ways that were previously unimagined. However, to extract the information from these large data sets, we rely on newly developing machine learning and advanced data science methods. Working with our collaborators we are extracting completely new types of information on the spatio-temporal dynamics that takes place spontaneously in many complex materials systems.
- Deep Denoising for Scientific Discovery: A Case Study in Electron Microscopy, IEEE Transactions on computational imaging, 2022
- Exploring Blob Detection to Determine Atomic Column Positions and Intensities in Time-Resolved TEM Images with Ultra-Low Signal-to-Noise, Microscopy and Microanalysis, 2022
- Developing and Evaluating Deep Neural Network-Based Denoising for Nanoparticle TEM Images with Ultra-Low Signal-to-Noise, Microscopy and Microanalysis, 2021
- Unsupervised Deep Video Denoising, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021