Summary
Kyle Ferchen is a machine learning research scientist with a PhD in Cancer and Cell Biology and over a decade of research experience, now applying multimodal ML at Meta. He specializes in integrating single-cell sequencing, spectral flow cytometry, and computational modeling to map cell states and gene regulatory networks, work that contributed to a Nature paper on blood cell development. Comfortable across Python, R, C++, and visualization stacks (including D3), he builds tools that fuse high-parameter cytometry with transcriptional atlases for interpretable biological insights. At Cincinnati Children’s he led projects combining CITE-seq/TEA-seq/ChIP-seq to create atlases of stem and progenitor populations, then translated that expertise into production-scale ML research. Known for turning experimental complexity into reusable software and visualizations, he blends domain biology with rigorous model development and data storytelling. Based in Cincinnati, he brings both bench-side intuition and production ML skills to problems at the intersection of computational biology and applied machine learning.
10 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Cancer and Cell Biology, Doctor of Philosophy - PhD, Cancer and Cell Biology at University of Cincinnati
Bachelor’s Degree, Natural Sciences, 3.856, Bachelor’s Degree, Natural Sciences, 3.856 at Xavier University
English