Summary
George Seelinger is a mathematician and former data scientist with 12 years of experience bridging theoretical research and practical decision systems. Currently a Postdoctoral Assistant Professor and Research Fellow at the University of Michigan, he applies deep expertise in algebraic combinatorics and symmetric function theory to problems that benefit from rigorous algorithmic thinking. His background includes building real-time bidding optimization algorithms and auction simulators in industry, giving him a rare combination of hands-on ML/engineering experience and advanced mathematical training (Ph.D., University of Virginia). He has taught flipped and active-learning calculus courses as instructor of record, showing a talent for translating complex ideas into accessible pedagogy. Based in Ann Arbor, he is comfortable moving between code, data, and abstract theory to produce reproducible, well-documented work.
12 years of coding experience
4 years of employment as a software developer
18 Credit Hours, General Education, 18 Credit Hours, General Education at Illinois State University
Doctor of Philosophy (Ph.D.), Mathematics, Doctor of Philosophy (Ph.D.), Mathematics at University of Virginia
Loyola University Chicago John Felice Rome Center
Master of Science (M.S.), Mathematics, Master of Science (M.S.), Mathematics at Loyola University Chicago
Bachelor of Science (B.S.), summa cum laude, Mathematics and Computer Science, Interdisciplinary Honors, Bachelor of Science (B.S.), summa cum laude, Mathematics and Computer Science, Interdisciplinary Honors at Loyola University of Chicago
English, French, Italian