Sudhir Ghandikota is a Senior Bioinformatics Scientist with a Ph.D. in Computer Science focused on bioinformatics and over a decade of experience bridging software engineering and translational biomedical research. He designs and implements multimodal and multiview machine learning frameworks—particularly unsupervised and self-supervised graph neural networks—for biomarker and drug-target discovery, and is extending these methods to single-cell transcriptomics. At Cincinnati Children’s Hospital he has moved ideas from doctoral research into production-grade deep learning pipelines using TensorFlow, PyTorch (including PyTorch Geometric) and Optuna for hyperparameter optimization. His background in large-scale data engineering and distributed systems from early roles (Hadoop, HBase, ETL) gives him a rare ability to take complex genomic ML from data ingestion through model development to deployable tools. He combines strong academic publication experience with practical tool-building, including interactive web-based bioinformatics utilities and reproducible pipelines for public genomics resources.
10 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Software Engineering, Doctor of Philosophy - PhD, Computer Software Engineering at University of Cincinnati
Jawaharlal Nehru Technological University Hyderabad
Contributions:10 commits, 8 pushes, 1 branch in 4 months
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