Summary
Marcus Stoiber is a director and machine learning leader specializing in nanopore sequencing analytics, currently heading modified-base model development at Oxford Nanopore Technologies. With a PhD in Biostatistics from UC Berkeley and over a decade of experience across academia and industry, he has built and deployed foundational tools for modified-base detection from raw electrical signals—projects publicly available as Megalodon, Remora and Tombo. His work bridges deep algorithm development, production ML deployment, and hands-on bioinformatics, reflecting a rare combination of statistical rigor and systems engineering. Previously he led diverse computational biology efforts at Berkeley Lab, Harvard, and NIH, spanning RNA‑seq network biology, eRNA studies, and structural-variation analyses. Based in Napa, Marcus pairs open-source impact with cross-disciplinary collaborations (including EPA and industry partners), enabling practical genomics solutions from low-level signal processing to end-user tools.
11 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy (PhD) Biostatistics, Doctor of Philosophy (PhD) Biostatistics at University of California, Berkeley
Johns Hopkins University
BS Biochemistry and Mathematics, BS Biochemistry and Mathematics at Occidental College