Summary
Stephen Newman is a computer scientist and Ph.D. student at Princeton with eight years of experience bridging theoretical computer science and mathematical data science. His background combines rigorous training from Yale in mathematics and computer science with hands-on research, including work on the Perfect-Mirsky Conjecture during a William & Mary REU. He has practical exposure to machine learning perception from an industry stint at Matician and a taste for deep, theory-driven problems like evolutionary computation and complexity. Based in Charlottesville, he brings a research-first mindset to applied problems, routinely translating abstract theory into concrete experimental work. Colleagues can expect a thinker who blends formal rigor with curiosity about data-driven methods.
8 years of coding experience
Charlottesville High School
Combined Master's/Bachelor's degree, Mathematics (M.S./B.S.), Computer Science (B.S.), Combined Master's/Bachelor's degree, Mathematics (M.S./B.S.), Computer Science (B.S.) at Yale University