Saideep Gona is a computational genomics PhD candidate at the University of Chicago with a decade of experience applying probabilistic modeling and deep learning to next-generation sequencing for clinically relevant insights. He develops novel ML frameworks—most recently Con-EnPACT—to adapt large sequence-to-epigenome models like Enformer for population-aware association analyses and TWAS-style studies. Previously he provided bioinformatics support to clinical researchers, implemented NGS pipelines, and built a web app for large-scale ChIP-Seq analysis, demonstrating strength across research software, data engineering, and statistical genetics. Trained at Carnegie Mellon (MS) and UNC (BS), he pairs rigorous coursework in probability and algorithms with hands-on deployment experience in TensorFlow-based biomedical pipelines. Colleagues will find him equally comfortable prototyping deep models and operationalizing reproducible analyses for translational genomics.
10 years of coding experience
5 years of employment as a software developer
Bachelor’s Degree, Public Health - Environmental Health Science, Bachelor’s Degree, Public Health - Environmental Health Science at University of North Carolina at Chapel Hill
Doctor of Philosophy - PhD, Genetics, Genomics, and Systems Biology - Computational Track, Doctor of Philosophy - PhD, Genetics, Genomics, and Systems Biology - Computational Track at University of Chicago
Master of Science - MS, Computational Biology, Master of Science - MS, Computational Biology at Carnegie Mellon University
Robust Allele Specific Quantification and quality controL
Contributions:9 pushes in 4 years 4 months
quality-controlquantificationqualityallele
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