Mentored by Mitra Taheri

Advances in Transmission Electron Microscope (TEM) resolution, precision, and collection rates have greatly improved our ability to probe complex structures and changes in materials. Machine learning frameworks, such as variational auto-encoders are powerful tools that enhance the processing and characterization of the complex signals received from the instruments.

Our auto-encoder will be trained using a set of handwritten digits provided by the MNIST database. We aim to train the auto-encoder to cluster and classify the different digits and image the latent space. We will then transition into a SrFeO3 TEM Electron Energy Loss Spectroscopy (EELS) dataset. We hope to identify, cluster, and classify transient intermediate states of observed chemical reactions and produce higher quality de-noised images.

First-year undergraduate majoring in Materials Science & Engineering, and Computer Science,at Johns Hopkins Whiting School of Engineering