Smita Krishnaswamy

Smita Krishnaswamy is an Associate Professor in the department of Genetics and Computer Science at Yale, a core member of the Program in Applied Mathematics, Computational Biology and Interdisciplinary Neuroscience. She is also affiliated with the Yale Center for Biomedical Data Science, Yale Cancer Center, and Wu-Tsai Institute. Smita’s research focuses on developing deep representation learning methods that use mathematical concepts from manifold learning, data geometry, topology and signal processing. to denoise, impute, visualize and extract structure, patterns and relationships from big, high throughput, high dimensional biomedical data. Her methods have been applied to a variety of datasets from many systems in cancer biology, immunology, neuroscience, and structural biology.

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.