Research & Publications
Smita Krishnaswamy 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. During her Ph.D., she received a best paper award at DATE 2005 (a top conference in the field of design automation), 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 TJ 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 IBMs 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 systems biology department 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. Smita'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 paper, reveal the computation performed by cells as they process signals in terms of stochastic response functions. Smita, along with experimental collaborators, have applied these methods to T cell signaling and have found that signaling response functions are reconfigured through differentiation and disease. For example, Smita and her collaborators found that subtle alterations in receptor-proximal signaling in non-obese diabetic (NOD) mice are amplified through signaling cascades leading to larger defects in downstream signals responsible for damping immune response. 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, 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.
Education & Training
- PhDUniversity of Michigan (2008)
- MSUniversity of Michigan (2004)
- BAKalamazoo College, Mathematics (2002)
- BSUniversity of Michigan (2002)
- Postdoctoral FellowshipColumbia University