Smita Krishnaswamy, PhD

Smita Krishnaswamy’s research is focused on bringing in priors from mathematics (geometry, topology, harmonic analysis and dynamic systems, as well as signal processing), information theory, wavelet and scattering transforms into unsupervised learning and deep learning models in order to solve problems in biomedical and computational neuroscience. Technological revolutions have now made it possible to measure large quantities of high-dimensional data on biomedical systems of all sorts, including neuronal systems. However, there is a large gap between the information inherent in this data and what can be extracted by modern machine learning methods. Krishnaswamy’s research aims to bridge this gap by developing methods for unsupervised pattern detection and hypothesis generation using mathematical and data manifold priors into computationally efficient deep neural networks. Prior problems that her research group has tackled include manifold denoising of biomedical data to recover structure and relationships, methods for structure-preserving visualization of biomedical data in low dimensions, domain adaptation methods that can predict one datatype from another, and methods for learning high-dimensional dynamics from static snapshot data. Their methods have been applied to cellular measurements from a variety of systems, including cancer, immunology, neurodegeneration, and developmental. More recently, her lab has focused on combining topological data analysis with manifold learning and deep learning in order to uncover the manifold structure and time dynamics from fMRI data of multiple participants. Future research will involve improving machine learning methods using neuroscience-specific techniques and priors, along with integrating multimodal neuronal data.

computational modeling ;  deep learning ;  dimensionality reduction ;  generative models ;  graph theory ;  manifold learning ;  neural networks ;  language models  

algorithms ;  artificial intelligence ;  cellular neuroscience ;  computational biology ;  computational neuroscience ;  computational psychiatry ;  machine learning ;  biomedical image analysis  

Krishnaswamy obtained her PhD in Computer Science and Engineering from the University of Michigan in 2008. Subsequently, she joined the systems division of IBM's TJ Watson Research Center as a postdoctoral associate and research staff member. In 2010, she decided to move to another field and became a postdoctoral associate at Columbia University's Systems Biology division. Krishnaswamy started her faculty position and lab at Yale in 2015. Since then, her lab has developed fundamental computational techniques in data geometry, manifold learning, and deep learning to solve biological problems in neuroscience, immunology, cancer, and other areas.