Smita Krishnaswamy
Geometric and Topological Approaches to Representation Learning in Biomedical Data
Abstract: In this talk, I will overview data geometric and topological approaches to understanding the shape and structure of the data. First, we show how diffusion geometry and deep learning can be used to obtain useful representations of the data that enable denoising, dimensionality reduction, and factor analysis of the data. Next, we will show how to learn dynamics from static snapshot data by using a manifold-regularized neural ODE-based optimal transport (TrajectoryNet). Finally, we cover a novel approach to combine diffusion geometry with topology to extract multi-granular features from the data (Diffusion Condensation and Multiscale PHATE).
Bio: Smita Krishnaswamyis an Associate Professor in the Department of Genetics at the Yale School of Medicine and Department of Computer Science in the Yale School of Applied Science and Engineering and a core member of the Program in Applied Mathematics. She is also affiliated with the Yale Center for Biomedical Data Science, Yale Cancer Center, and Program in Interdisciplinary Neuroscience. Smita’s research focuses on developing unsupervised machine learning methods (especially graph signal processing and deep-learning) to denoise, impute, visualize and extract structure, patterns, and relationships from big, high throughput, high dimensional biomedical data. Her methods have been applied a variety of datasets from many systems including embryoid body differentiation, zebrafish development, the epithelial-to-mesenchymal transition in breast cancer, lung cancer immunotherapy, infectious disease data, gut microbiome data, and patient data.
Smita teaches three courses: Machine Learning for Biology (Fall), Deep Learning Theory and applications (spring), Advanced Topics in Machine Learning & Data Mining (Spring). She completed her postdoctoral training at Columbia University in the systems biology department where she focused on learning computational models of cellular signaling from single-cell mass cytometry data. She was trained as a computer scientist with a Ph.D. from the University of Michigan’s EECS department where her research focused on algorithms for automated synthesis and probabilistic verification of nanoscale logic circuits. Following her time in Michigan, Smita spent 2 years at IBM’s TJ Watson Research Center as a researcher in the systems division where she worked on automated bug finding and error correction in logic.