Digvijay Kumar Yarlagadda

I am a PhD student in the Tri-Institutional Program in Computational Biology & Medicine at Cornell University, Sloan Kettering Institute and Rockefeller University. I am comentored by Christina Leslie and Joan Massagué. My research interests lie at the interface of computer vision and biology. In my current research, I am focused on developing novel representation learning methods for spatial transcriptomics data and applying these methods to study cancer metastasis.

During summer 2024, I interned with Olga Troyanskaya at Flatiron Institute, Simons Foundation and Princeton University, working on vision transformer models to integrate microscopy and genomics data. Prior to joining PhD program, I was part of Thomas Fuchs Lab at Memorial Sloan-Kettering Cancer Center where I worked on developing computer vision and deep learning algorithms for computational pathology, which sparked my interest in exploring the challenging problem of cancer metastasis through the lens of machine learning.

I have a master's degree in computer science from University of Missouri-Kansas City, where I was advised by Praveen Rao and funded by dean's international scholar award. I also took on roles to launch products at early stage startups AgShift, Strayos and Zenjoi.

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I'm interested in computer vision and deep learning research, with applications in biology and medicine.

Discrete representations can capture multi-scale spatial structures in spatial transcriptomics data.

Memory augmentated neural networks provides a better way to rememeber rare occurances of cancer in histology images.

Segmentation of histopathology images can be improved by training on patches from multiple magnifications.

Using Z-indexing creates more effictive platform for WSI storage and retrieval.

Computer vision algorithm to extract annotations on large molecular pathology datasets.

End to end part learning achieves state-of-the-art performance for multi-label prediction.