Research & Publications
Smita Krishnaswamy is an Assistant 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. Smita’s research is at the intersection of applied math, machine learning and computational biology. She focuses on developing deep representation learning methods for unsupervised data exploration. Some of the key projects developed in her Lab include MAGIC (a tool for imputation and denoising of data), PHATE (a powerful new visualization method for high dimensional data that can unveil progression and cluster structures, and SAUCIE (an autoencoder-based deep learning approach for automatically batch correcting, visualizing, denoising and clustering data). These methods have been applied to a variety of biological applications in neuroscience, immunology, cancer biology, and clinical outcomes.
At Yale, Smita teaches two machine learning courses: Unsupervised Learning for Big Data and Deep learning theory and applications. 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 verification of nanoscale logic circuits that exhibit probabilistic effects.
Allergy and Immunology; Neurosciences; Computational Biology; Machine Learning; Deep Learning
- Design, Analysis and Test of Logic Circuits under UncertaintySmita Krishnaswamy, Igor L. Markov, John P. Hayes Lecture Notes in Electrical Engineering, Springer DOI:10.1007/978-90-481-9644-9