About Me

I am a Material Science Ph.D. candidate at the University of Michigan, Ann Arbor advised by Dr. Robert Hovden . My PhD research primarily focuses on material discovery through image processing, data analysis, and 3D reconstruction of nano- and atomic-scale images collected by electron microscopes. During the course of my Ph.D. I focused on solving inverse problems that arise in electron microscopy by using concepts from signal processing and deep learning. My current area is strongly focused on computational aspects of this field, including algorithm development/optimization, large scale implementation via cluster computing, and scientific software development.

My interests and skillset covers the spectrum from machine learning to applying mathematical optimization methods for solving computer vision problems. Within the field of computer vision, I am particularly interested in three areas of research: (1) large-scale data-analytics while using high performance computing (2) computational imaging (image processing) and (3) deep learning for classification or segmentation. For my research I developed novel algorithms and implemented / accelerated existing ones into scalable, parallel computing solutions on hardware which includes supercomputers and GPUs.

Selected Publications

J. Schwartz, Z.W. Di, A. Fielitz, et. al."Imaging atomic scale chemistry from Fused Multi-Modal Electron Microscopy" npj Computational Materials 8, 16 (2022). [Paper] [Code]

J. Schwartz, C. Harris, J. Pietryga, et. al."Real-Time 3D Analysis During Electron Tomography using tomviz" Nature Communications 13, 4458 (2022). [Paper] [Article Highlight] [Podcast Interview]

M.C. Cao, J. Schwartz, H. Zheng, Y. Jiang, R. Hovden, Y. Han "Atomic Defect Identification with Sparse Sampling and Deep Learning" Communications in Computer and Information Science 1512 (2022). [Paper] [Code]

J. Schwartz, H. Zheng, M. Hanwell, Y. Jiang, R. Hovden, "Dynamic Compressed Sensing for Real-Time Tomographic Reconstruction" Ultramicroscopy 219, 113122 (2020). [Paper]

J. Schwartz Y. Jiang, Y Wang, et. al. "Removing Stripes, Scratches and Curtaining with Non-Recoverable Compressed Sensing" Microscopy and Microanalysis 25, 705-710 (2019). [Paper] [Code]

Y. Wang, Y. Wu, J. Schwartz, S.H. Sung, R.Hovden, Z. Mi, "A Single-Junction Cathodic Approach for Stable Unassisted Solar Water Splitting" Joule 3, 1-13 (2019). [Paper]

Projects

Semantic Segmentation of Materials with 3D Deep Learning

Trained U-Net and Resiudal U-Net neural network archictures with 3D-convolutional layers for the segmentation of atom coordinates from experimental atomic-resolution 3D tomographic models. This project is in portion of a project to measure >100k atoms in a 20 nm nanoparticle with electron tomography . [Code]

Crystal Classification from Deep Learning

Trained popular convolutional neural network architectures (e.g. ResNet50, Inception) for the classification of material's crystal structure from over 10^6 simulated diffraction images [Data Challenge] . I performed distributed data parallel training with multi-GPU and multi-node supercomputers available at Oak Ridge National Laboratory. Overall, the trained networks achieved a prediction accuracy of ~50% on the test dataset. [Code]

Single Image Super-Resolution

As part of a project for a special topics course. In this work, we explored various super-resolution convolutional neural network architectures to recover high resolution images from a low resolution input. [Code] <

Jonathan Schwartz