Summary
Ross Griebenow is a research-focused machine learning engineer and PhD candidate in mathematics based in Philadelphia with nine years of experience bridging applied ML and theoretical math. He has held research roles at Vertex Labs where he advanced applied ML projects and worked at Tufts on information-theoretic and causal algorithms probing emergence and consciousness. As a teaching assistant at Temple University, he combines rigorous mathematical training—particularly in geometric group theory, hyperbolic geometry, and low-dimensional topology—with practical algorithm development. Ross brings a rare interdisciplinary palette that spans complex systems, artificial life, and information theory, allowing him to translate deep theory into tractable research code. Colleagues describe him as both technically adventurous and methodical, equally comfortable proving theorems or shipping experimental ML prototypes.
9 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Mathematics, Doctor of Philosophy - PhD, Mathematics at Temple University
Computer Science, Minor in Mathematics (B.S.), Mathematics and Computer Science, Computer Science, Minor in Mathematics (B.S.), Mathematics and Computer Science at Drexel University