MIDAS Seminar Series Presents: Smita Krishnaswamy, Detection structure and patterns in big biomedical data

Smita Krishnaswamy, Assistant Professor, Genetics and Computer Science, Yale School of Medicine

Recording

Title: Learning Representations for Enabling Insight Into Big Biomedical Data

Abstract: High-throughput, high-dimensional data have become ubiquitous in the biomedical and health sciences as a result of breakthroughs in measurement technologies like single cell RNA-sequencing, as well as vast improvements in health record data collection and storage. While these large datasets containing millions of cellular or patient observations hold great potential for understanding generative state space of the data, as well as drivers of differentiation, disease and progression, they also pose new challenges in terms of noise, missing data, measurement artifacts, and the so-called “curse of dimensionality.” In this talk I will cover methods for learning intrinsic representations of this data that extract data geometry from noise, artifact and density, and further show how such representations can be used for downstream analysis tasks such as data generation that fills in missing sparse regions of the manifold (SUGAR), manifold alignment (Harmonic Alignment), archetypal analysis (AAnet) and dynamics modeling (DyMON and TraectoryNet).

Bio: 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.