Abhimanyu Banerjee is a Senior Bioinformatics Scientist in Palo Alto with a decade of experience applying deep learning and statistical methods to regulatory genomics and rare-disease prediction. Trained at Stanford and IIT Kanpur, he builds and benchmarks DNNs (ResNets, Transformers) on large primate whole-genome datasets to predict non-coding variant pathogenicity and has driven transfer-learning and ensembling strategies to push state-of-the-art performance. At Illumina he led multi-timepoint single-cell multiome analyses of iPSC-to-cardiomyocyte differentiation, combining optimal transport trajectory inference with neural-network interpretation to reveal novel regulators of maturation. His background blends rigorous theory—published work in statistical mechanics and genomics—with hands-on ML productization for clinical and functional genomics use cases. He regularly bridges computation and experiment, collaborating with wet-lab teams and translating model insights into testable biology. Quietly ambitious, he favors interpretable models and robust benchmarks that make research directly useful for clinical genomics workflows.
Contributions:5 pushes, 1 branch in 1 year 7 months
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