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Dr. Farzana Nasrin
Math Dept, UTK
Dr. Farzana Nasrin graduated from Texas Tech University with a Ph.D. in Applied Mathematics in August 2018. Her research interests are in Computational Mathematics, Data Analysis, Statistics and Image Analysis. Currently she is a post-doctoral research associate at UTK. She studies supervised and unsupervised machine learning methods on the space of persistence diagrams with applications to acoustic signals, biomedical imaging, EEG, and materials data. Her dissertation involves development of analytical tools for smooth shape reconstruction from noisy data and visualization tools for utilizing information from advanced imaging devices.
Topological Machine Learning in Material Science
Recent advances in material science have led to the era of enormous databases of materials for different applications. Analyzing and classifying such large and complex datasets is generally challenging. Topological data analysis, that builds on techniques from topology, is a natural fit for this application. Data analysis and classification involving persistence diagrams have been applied in numerous applications such as action recognition, handwriting analysis, shape study, image analysis, sensor network, and signal analysis. In this talk I will provide a brief introduction to topological data analysis, focusing primarily on persistence diagrams. Our goal is to provide a supervised machine learning algorithm, the classifier(?), on the space of persistence diagrams. This framework is applicable to a wide variety of datasets. I will present an application in material science, on classification of crystal structures of High Entropy Alloys.