Gali Bai is a computational cancer genomics researcher and Ph.D. candidate in Biomolecular Engineering and Bioinformatics at UC Santa Cruz with nine years of experience building bioinformatics pipelines and machine learning models for high-throughput sequencing. She develops deep learning methods to detect modified bases from Oxford Nanopore signals and integrates long-read epigenetic and transcriptomic data to map chromatin accessibility and isoform expression in cancer. Previously she supported multi-omics and single-cell analysis pipelines at Dana-Farber/Harvard, and applied GWAS and large-scale NGS workflows in plant genomics using HPC. Comfortable across full-stack AI development and research software, she bridges methods development with clinical collaboration to translate computational insights into real-world oncology questions. An educator as well as a developer, she brings hands-on pipeline optimization experience and a penchant for single-molecule resolution analyses that reveal biology missed by short-read approaches.
9 years of coding experience
2 years of employment as a software developer
Master of Science, Genomics and System Biology, 3.8/4.0, Master of Science, Genomics and System Biology, 3.8/4.0 at Texas A&M University
University of California Santa Cruz
Bachelor of Science - BS, College of Agriculture and Biotechnology, 3.43/4.0, Bachelor of Science - BS, College of Agriculture and Biotechnology, 3.43/4.0 at China Agricultural University
Contributions:184 commits, 4 pushes, 2 issues in 1 year 9 months
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