Smita Krishnaswamy has a Ph.D. from the University of Michigan’s Electrical Engineering and Computer Science department, where her research focused on algorithms for automated synthesis and verification of nanoscale logic circuits that exhibit probabilistic effects. During her Ph.D., Krishnaswamy received a best paper award at the Design Automation and Test in Europe (DATE) 2005 conference and an outstanding dissertation award. She published numerous first-author papers on probabilistic network models and algorithms for VLSICAD. In addition, her dissertation was published as a book by Springer in 2013.
Following her Ph.D., she joined IBM’s T.J. Watson Research Center as a scientist in the systems division, where she focused on formal methods for automated error detection. Her Deltasyn algorithm was eventually utilized in IBM’s p and z series high-performance chips. She then switched her research efforts to biology. Her postdoctoral training was completed at Columbia University in the Department of Systems Biology, where she focused on learning computational models of cellular signaling from single-cell mass cytometry data.
Although technologies such as mass cytometry and single-cell RNA sequencing are able to generate high-dimensional high-throughput single-cell data, the computational, modeling and visualization techniques needed to analyze and make sense of this data are still lacking. Krishnaswamy’s research addresses this challenge by developing scalable computational methods for analyzing and learning predictive network models from massive biological datasets. Her methods for characterizing interactions in cellular signaling networks, published in a recent Science paper1, reveal the computation performed by cells as they process signals in terms of stochastic response functions. Her ongoing work involves creating more sophisticated and accurate models of transformational biological processes by combining both single-cell signaling and genomic data. At Yale University, she is creating a forward-looking and interdisciplinary research group that is focused on developing computational techniques to solve today’s challenging biological and medical problems.
