Soumya Kundu is a Machine Learning Scientist in London with nine years of experience applying deep learning to regulatory genomics and human disease. Currently at Relation Therapeutics, she develops interpretable sequence models to pinpoint how non-coding genetic variation drives disorders; her Stanford PhD and subsequent postdoctoral work focused on prioritizing causal variants at GWAS loci for diseases like Alzheimer’s, Parkinson’s, coronary artery disease, and colorectal cancer. She combines rigorous computational methods with domain knowledge in neurodegeneration and psychiatric disorders, and has taught deep-learning-in-genomics courses while mentoring students. Beyond model building, she has a track record of creating simulation and algorithmic tools during her undergraduate research to tackle phylogenetic uncertainty—an example of her broader interest in robust, reproducible computational biology.
9 years of coding experience
9 years of employment as a software developer
High School Diploma, High School Diploma at Jonathan Law High School
Master of Science - MS Computer Science and Engineering, Master of Science - MS Computer Science and Engineering at University of Connecticut
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Stanford University
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Soumya Kundu - Machine Learning Scientist at Relation