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.
10 years of coding experience
3 years of employment as a software developer
MSc Integrated (5 year) Physics, MSc Integrated (5 year) Physics at Indian Institute of Technology Kanpur
Doctor of Philosophy (Ph.D.) Computational Genomics, Doctor of Philosophy (Ph.D.) Computational Genomics at Stanford University
Contributions:5 pushes, 1 branch in 1 year 7 months
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